qualitative research content analysis

What Is Qualitative Content Analysis?

Qca explained simply (with examples).

By: Jenna Crosley (PhD). Reviewed by: Dr Eunice Rautenbach (DTech) | February 2021

If you’re in the process of preparing for your dissertation, thesis or research project, you’ve probably encountered the term “ qualitative content analysis ” – it’s quite a mouthful. If you’ve landed on this post, you’re probably a bit confused about it. Well, the good news is that you’ve come to the right place…

Overview: Qualitative Content Analysis

What (exactly) is qualitative content analysis
The two main types of content analysis
When to use content analysis
How to conduct content analysis (the process)
The advantages and disadvantages of content analysis

1. What is content analysis?

Content analysis is a qualitative analysis method that focuses on recorded human artefacts such as manuscripts, voice recordings and journals. Content analysis investigates these written, spoken and visual artefacts without explicitly extracting data from participants – this is called unobtrusive research.

In other words, with content analysis, you don’t necessarily need to interact with participants (although you can if necessary); you can simply analyse the data that they have already produced. With this type of analysis, you can analyse data such as text messages, books, Facebook posts, videos, and audio (just to mention a few).

The basics – explicit and implicit content

When working with content analysis, explicit and implicit content will play a role. Explicit data is transparent and easy to identify, while implicit data is that which requires some form of interpretation and is often of a subjective nature. Sounds a bit fluffy? Here’s an example:

Joe: Hi there, what can I help you with?

Lauren: I recently adopted a puppy and I’m worried that I’m not feeding him the right food. Could you please advise me on what I should be feeding?

Joe: Sure, just follow me and I’ll show you. Do you have any other pets?

Lauren: Only one, and it tweets a lot!

In this exchange, the explicit data indicates that Joe is helping Lauren to find the right puppy food. Lauren asks Joe whether she has any pets aside from her puppy. This data is explicit because it requires no interpretation.

On the other hand, implicit data , in this case, includes the fact that the speakers are in a pet store. This information is not clearly stated but can be inferred from the conversation, where Joe is helping Lauren to choose pet food. An additional piece of implicit data is that Lauren likely has some type of bird as a pet. This can be inferred from the way that Lauren states that her pet “tweets”.

As you can see, explicit and implicit data both play a role in human interaction and are an important part of your analysis. However, it’s important to differentiate between these two types of data when you’re undertaking content analysis. Interpreting implicit data can be rather subjective as conclusions are based on the researcher’s interpretation. This can introduce an element of bias , which risks skewing your results.

Explicit and implicit data both play an important role in your content analysis, but it’s important to differentiate between them.

2. The two types of content analysis

Now that you understand the difference between implicit and explicit data, let’s move on to the two general types of content analysis : conceptual and relational content analysis. Importantly, while conceptual and relational content analysis both follow similar steps initially, the aims and outcomes of each are different.

Conceptual analysis focuses on the number of times a concept occurs in a set of data and is generally focused on explicit data. For example, if you were to have the following conversation:

Marie: She told me that she has three cats.

Jean: What are her cats’ names?

Marie: I think the first one is Bella, the second one is Mia, and… I can’t remember the third cat’s name.

In this data, you can see that the word “cat” has been used three times. Through conceptual content analysis, you can deduce that cats are the central topic of the conversation. You can also perform a frequency analysis , where you assess the term’s frequency in the data. For example, in the exchange above, the word “cat” makes up 9% of the data. In other words, conceptual analysis brings a little bit of quantitative analysis into your qualitative analysis.

As you can see, the above data is without interpretation and focuses on explicit data . Relational content analysis, on the other hand, takes a more holistic view by focusing more on implicit data in terms of context, surrounding words and relationships.

There are three types of relational analysis:

Affect extraction
Proximity analysis
Cognitive mapping

Affect extraction is when you assess concepts according to emotional attributes. These emotions are typically mapped on scales, such as a Likert scale or a rating scale ranging from 1 to 5, where 1 is “very sad” and 5 is “very happy”.

If participants are talking about their achievements, they are likely to be given a score of 4 or 5, depending on how good they feel about it. If a participant is describing a traumatic event, they are likely to have a much lower score, either 1 or 2.

Proximity analysis identifies explicit terms (such as those found in a conceptual analysis) and the patterns in terms of how they co-occur in a text. In other words, proximity analysis investigates the relationship between terms and aims to group these to extract themes and develop meaning.

Proximity analysis is typically utilised when you’re looking for hard facts rather than emotional, cultural, or contextual factors. For example, if you were to analyse a political speech, you may want to focus only on what has been said, rather than implications or hidden meanings. To do this, you would make use of explicit data, discounting any underlying meanings and implications of the speech.

Lastly, there’s cognitive mapping, which can be used in addition to, or along with, proximity analysis. Cognitive mapping involves taking different texts and comparing them in a visual format – i.e. a cognitive map. Typically, you’d use cognitive mapping in studies that assess changes in terms, definitions, and meanings over time. It can also serve as a way to visualise affect extraction or proximity analysis and is often presented in a form such as a graphic map.

To recap on the essentials, content analysis is a qualitative analysis method that focuses on recorded human artefacts . It involves both conceptual analysis (which is more numbers-based) and relational analysis (which focuses on the relationships between concepts and how they’re connected).

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3. When should you use content analysis?

Content analysis is a useful tool that provides insight into trends of communication . For example, you could use a discussion forum as the basis of your analysis and look at the types of things the members talk about as well as how they use language to express themselves. Content analysis is flexible in that it can be applied to the individual, group, and institutional level.

Content analysis is typically used in studies where the aim is to better understand factors such as behaviours, attitudes, values, emotions, and opinions . For example, you could use content analysis to investigate an issue in society, such as miscommunication between cultures. In this example, you could compare patterns of communication in participants from different cultures, which will allow you to create strategies for avoiding misunderstandings in intercultural interactions.

Another example could include conducting content analysis on a publication such as a book. Here you could gather data on the themes, topics, language use and opinions reflected in the text to draw conclusions regarding the political (such as conservative or liberal) leanings of the publication.

Content analysis is typically used in projects where the research aims involve getting a better understanding of factors such as behaviours, attitudes, values, emotions, and opinions.

4. How to conduct a qualitative content analysis

Conceptual and relational content analysis differ in terms of their exact process ; however, there are some similarities. Let’s have a look at these first – i.e., the generic process:

Recap on your research questions
Undertake bracketing to identify biases
Operationalise your variables and develop a coding scheme
Code the data and undertake your analysis

Step 1 – Recap on your research questions

It’s always useful to begin a project with research questions , or at least with an idea of what you are looking for. In fact, if you’ve spent time reading this blog, you’ll know that it’s useful to recap on your research questions, aims and objectives when undertaking pretty much any research activity. In the context of content analysis, it’s difficult to know what needs to be coded and what doesn’t, without a clear view of the research questions.

For example, if you were to code a conversation focused on basic issues of social justice, you may be met with a wide range of topics that may be irrelevant to your research. However, if you approach this data set with the specific intent of investigating opinions on gender issues, you will be able to focus on this topic alone, which would allow you to code only what you need to investigate.

With content analysis, it’s difficult to know what needs to be coded without a clear view of the research questions.

Step 2 – Reflect on your personal perspectives and biases

It’s vital that you reflect on your own pre-conception of the topic at hand and identify the biases that you might drag into your content analysis – this is called “ bracketing “. By identifying this upfront, you’ll be more aware of them and less likely to have them subconsciously influence your analysis.

For example, if you were to investigate how a community converses about unequal access to healthcare, it is important to assess your views to ensure that you don’t project these onto your understanding of the opinions put forth by the community. If you have access to medical aid, for instance, you should not allow this to interfere with your examination of unequal access.

You must reflect on the preconceptions and biases that you might drag into your content analysis - this is called "bracketing".

Step 3 – Operationalise your variables and develop a coding scheme

Next, you need to operationalise your variables . But what does that mean? Simply put, it means that you have to define each variable or construct . Give every item a clear definition – what does it mean (include) and what does it not mean (exclude). For example, if you were to investigate children’s views on healthy foods, you would first need to define what age group/range you’re looking at, and then also define what you mean by “healthy foods”.

In combination with the above, it is important to create a coding scheme , which will consist of information about your variables (how you defined each variable), as well as a process for analysing the data. For this, you would refer back to how you operationalised/defined your variables so that you know how to code your data.

For example, when coding, when should you code a food as “healthy”? What makes a food choice healthy? Is it the absence of sugar or saturated fat? Is it the presence of fibre and protein? It’s very important to have clearly defined variables to achieve consistent coding – without this, your analysis will get very muddy, very quickly.

When operationalising your variables, you must give every item a clear definition. In other words, what does it mean (include) and what does it not mean (exclude).

Step 4 – Code and analyse the data

The next step is to code the data. At this stage, there are some differences between conceptual and relational analysis.

As described earlier in this post, conceptual analysis looks at the existence and frequency of concepts, whereas a relational analysis looks at the relationships between concepts. For both types of analyses, it is important to pre-select a concept that you wish to assess in your data. Using the example of studying children’s views on healthy food, you could pre-select the concept of “healthy food” and assess the number of times the concept pops up in your data.

Here is where conceptual and relational analysis start to differ.

At this stage of conceptual analysis , it is necessary to decide on the level of analysis you’ll perform on your data, and whether this will exist on the word, phrase, sentence, or thematic level. For example, will you code the phrase “healthy food” on its own? Will you code each term relating to healthy food (e.g., broccoli, peaches, bananas, etc.) with the code “healthy food” or will these be coded individually? It is very important to establish this from the get-go to avoid inconsistencies that could result in you having to code your data all over again.

On the other hand, relational analysis looks at the type of analysis. So, will you use affect extraction? Proximity analysis? Cognitive mapping? A mix? It’s vital to determine the type of analysis before you begin to code your data so that you can maintain the reliability and validity of your research .

How to conduct conceptual analysis

First, let’s have a look at the process for conceptual analysis.

Once you’ve decided on your level of analysis, you need to establish how you will code your concepts, and how many of these you want to code. Here you can choose whether you want to code in a deductive or inductive manner. Just to recap, deductive coding is when you begin the coding process with a set of pre-determined codes, whereas inductive coding entails the codes emerging as you progress with the coding process. Here it is also important to decide what should be included and excluded from your analysis, and also what levels of implication you wish to include in your codes.

For example, if you have the concept of “tall”, can you include “up in the clouds”, derived from the sentence, “the giraffe’s head is up in the clouds” in the code, or should it be a separate code? In addition to this, you need to know what levels of words may be included in your codes or not. For example, if you say, “the panda is cute” and “look at the panda’s cuteness”, can “cute” and “cuteness” be included under the same code?

Once you’ve considered the above, it’s time to code the text . We’ve already published a detailed post about coding , so we won’t go into that process here. Once you’re done coding, you can move on to analysing your results. This is where you will aim to find generalisations in your data, and thus draw your conclusions .

How to conduct relational analysis

Now let’s return to relational analysis.

As mentioned, you want to look at the relationships between concepts . To do this, you’ll need to create categories by reducing your data (in other words, grouping similar concepts together) and then also code for words and/or patterns. These are both done with the aim of discovering whether these words exist, and if they do, what they mean.

Your next step is to assess your data and to code the relationships between your terms and meanings, so that you can move on to your final step, which is to sum up and analyse the data.

To recap, it’s important to start your analysis process by reviewing your research questions and identifying your biases . From there, you need to operationalise your variables, code your data and then analyse it.

5. What are the pros & cons of content analysis?

One of the main advantages of content analysis is that it allows you to use a mix of quantitative and qualitative research methods, which results in a more scientifically rigorous analysis.

For example, with conceptual analysis, you can count the number of times that a term or a code appears in a dataset, which can be assessed from a quantitative standpoint. In addition to this, you can then use a qualitative approach to investigate the underlying meanings of these and relationships between them.

Content analysis is also unobtrusive and therefore poses fewer ethical issues than some other analysis methods. As the content you’ll analyse oftentimes already exists, you’ll analyse what has been produced previously, and so you won’t have to collect data directly from participants. When coded correctly, data is analysed in a very systematic and transparent manner, which means that issues of replicability (how possible it is to recreate research under the same conditions) are reduced greatly.

On the downside , qualitative research (in general, not just content analysis) is often critiqued for being too subjective and for not being scientifically rigorous enough. This is where reliability (how replicable a study is by other researchers) and validity (how suitable the research design is for the topic being investigated) come into play – if you take these into account, you’ll be on your way to achieving sound research results.

One of the main advantages of content analysis is that it allows you to use a mix of quantitative and qualitative research methods, which results in a more scientifically rigorous analysis.

Recap: Qualitative content analysis

In this post, we’ve covered a lot of ground – click on any of the sections to recap:

If you have any questions about qualitative content analysis, feel free to leave a comment below. If you’d like 1-on-1 help with your qualitative content analysis, be sure to book an initial consultation with one of our friendly Research Coaches.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project.

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13 Comments

If I am having three pre-decided attributes for my research based on which a set of semi-structured questions where asked then should I conduct a conceptual content analysis or relational content analysis. please note that all three attributes are different like Agility, Resilience and AI.

Thank you very much. I really enjoyed every word.

please send me one/ two sample of content analysis

send me to any sample of qualitative content analysis as soon as possible

Many thanks for the brilliant explanation. Do you have a sample practical study of a foreign policy using content analysis?

1) It will be very much useful if a small but complete content analysis can be sent, from research question to coding and analysis. 2) Is there any software by which qualitative content analysis can be done?

Common software for qualitative analysis is nVivo, and quantitative analysis is IBM SPSS

Thank you. Can I have at least 2 copies of a sample analysis study as my reference?

Could you please send me some sample of textbook content analysis?

Can I send you my research topic, aims, objectives and questions to give me feedback on them?

please could you send me samples of content analysis?

really we enjoyed your knowledge thanks allot. from Ethiopia

can you please share some samples of content analysis(relational)? I am a bit confused about processing the analysis part

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Knowledge Base

Methodology

Content Analysis | Guide, Methods & Examples

Content Analysis | Guide, Methods & Examples

Published on July 18, 2019 by Amy Luo . Revised on June 22, 2023.

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

Books, newspapers and magazines
Speeches and interviews
Web content and social media posts
Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding). In both types, you categorize or “code” words, themes, and concepts within the texts and then analyze the results.

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis, other interesting articles.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyze.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects or concepts in a set of historical or contemporary texts.

Quantitative content analysis example

To research the importance of employment issues in political campaigns, you could analyze campaign speeches for the frequency of terms such as unemployment , jobs , and work and use statistical analysis to find differences over time or between candidates.

In addition, content analysis can be used to make qualitative inferences by analyzing the meaning and semantic relationship of words and concepts.

Qualitative content analysis example

To gain a more qualitative understanding of employment issues in political campaigns, you could locate the word unemployment in speeches, identify what other words or phrases appear next to it (such as economy, inequality or laziness ), and analyze the meanings of these relationships to better understand the intentions and targets of different campaigns.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

Finding correlations and patterns in how concepts are communicated
Understanding the intentions of an individual, group or institution
Identifying propaganda and bias in communication
Revealing differences in communication in different contexts
Analyzing the consequences of communication content, such as the flow of information or audience responses

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Unobtrusive data collection

You can analyze communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

Highly flexible

You can conduct content analysis at any time, in any location, and at low cost – all you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions, leading to various types of research bias and cognitive bias .

Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct research question .

Example research question for content analysis

Is there a difference in how the US media represents younger politicians compared to older ones in terms of trustworthiness?

Next, you follow these five steps.

1. Select the content you will analyze

Based on your research question, choose the texts that you will analyze. You need to decide:

The medium (e.g. newspapers, speeches or websites) and genre (e.g. opinion pieces, political campaign speeches, or marketing copy)
The inclusion and exclusion criteria (e.g. newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
The parameters in terms of date range, location, etc.

If there are only a small amount of texts that meet your criteria, you might analyze all of them. If there is a large volume of texts, you can select a sample .

2. Define the units and categories of analysis

Next, you need to determine the level at which you will analyze your chosen texts. This means defining:

The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
The set of categories that you will use for coding. Categories can be objective characteristics (e.g. aged 30-40 , lawyer , parent ) or more conceptual (e.g. trustworthy , corrupt , conservative , family oriented ).

Your units of analysis are the politicians who appear in each article and the words and phrases that are used to describe them. Based on your research question, you have to categorize based on age and the concept of trustworthiness. To get more detailed data, you also code for other categories such as their political party and the marital status of each politician mentioned.

3. Develop a set of rules for coding

Coding involves organizing the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

In considering the category “younger politician,” you decide which titles will be coded with this category ( senator, governor, counselor, mayor ). With “trustworthy”, you decide which specific words or phrases related to trustworthiness (e.g. honest and reliable ) will be coded in this category.

4. Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti and Diction , which can help speed up the process of counting and categorizing words and phrases.

Following your coding rules, you examine each newspaper article in your sample. You record the characteristics of each politician mentioned, along with all words and phrases related to trustworthiness that are used to describe them.

5. Analyze the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context and audience of the texts.

Let’s say the results reveal that words and phrases related to trustworthiness appeared in the same sentence as an older politician more frequently than they did in the same sentence as a younger politician. From these results, you conclude that national newspapers present older politicians as more trustworthy than younger politicians, and infer that this might have an effect on readers’ perceptions of younger people in politics.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Measures of central tendency
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Thematic analysis
Cohort study
Peer review
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Implicit bias
Cognitive bias
Conformity bias
Hawthorne effect
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Attrition bias
Social desirability bias

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Content Analysis

Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. As an example, researchers can evaluate language used within a news article to search for bias or partiality. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text.

Description

Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study may analyze various forms of text in its analysis. To analyze the text using content analysis, the text must be coded, or broken down, into manageable code categories for analysis (i.e. “codes”). Once the text is coded into code categories, the codes can then be further categorized into “code categories” to summarize data even further.

Three different definitions of content analysis are provided below.

Definition 1: “Any technique for making inferences by systematically and objectively identifying special characteristics of messages.” (from Holsti, 1968)

Definition 2: “An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity, and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).

Definition 3: “A research technique for the objective, systematic and quantitative description of the manifest content of communication.” (from Berelson, 1952)

Uses of Content Analysis

Identify the intentions, focus or communication trends of an individual, group or institution

Describe attitudinal and behavioral responses to communications

Determine the psychological or emotional state of persons or groups

Reveal international differences in communication content

Reveal patterns in communication content

Pre-test and improve an intervention or survey prior to launch

Analyze focus group interviews and open-ended questions to complement quantitative data

Types of Content Analysis

There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.

Conceptual Analysis

Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (an issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.

General steps for conducting a conceptual content analysis:

1. Decide the level of analysis: word, word sense, phrase, sentence, themes

2. Decide how many concepts to code for: develop a pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.

Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question.

Option B allows the researcher to stay focused and examine the data for specific concepts.

3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.

When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared.

When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.

4. Decide on how you will distinguish among concepts:

Should text be coded exactly as they appear or coded as the same when they appear in different forms? For example, “dangerous” vs. “dangerousness”. The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes.

What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept? For example, “dangerous” vs. “the person is scary” vs. “that person could cause harm to me”. These word segments may not merit separate categories, due the implicit meaning of “dangerous”.

5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.

6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?

7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize errors far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.

8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted, or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.

Relational Analysis

Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.

To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.

There are three subcategories of relational analysis to choose from prior to going on to the general steps.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.

Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Text is defined as a string of words called a “window” that is scanned for the co-occurrence of concepts. The result is the creation of a “concept matrix”, or a group of interrelated co-occurring concepts that would suggest an overall meaning.

Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

General steps for conducting a relational content analysis:

1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes. 2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words. 3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:

Strength of relationship: degree to which two or more concepts are related.

Sign of relationship: are concepts positively or negatively related to each other?

Direction of relationship: the types of relationship that categories exhibit. For example, “X implies Y” or “X occurs before Y” or “if X then Y” or if X is the primary motivator of Y.

4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded. 5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding. 6. Map out representations: such as decision mapping and mental models.

Reliability and Validity

Reliability : Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:

Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time.

Reproducibility: tendency for a group of coders to classify categories membership in the same way.

Accuracy: extent to which the classification of text corresponds to a standard or norm statistically.

Validity : Three criteria comprise the validity of a content analysis:

Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.

Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could throw off one’s results and make any conclusion invalid.

Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.

Advantages of Content Analysis

Directly examines communication using text

Allows for both qualitative and quantitative analysis

Provides valuable historical and cultural insights over time

Allows a closeness to data

Coded form of the text can be statistically analyzed

Unobtrusive means of analyzing interactions

Provides insight into complex models of human thought and language use

When done well, is considered a relatively “exact” research method

Content analysis is a readily-understood and an inexpensive research method

A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.

Disadvantages of Content Analysis

Can be extremely time consuming

Is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation

Is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study

Is inherently reductive, particularly when dealing with complex texts

Tends too often to simply consist of word counts

Often disregards the context that produced the text, as well as the state of things after the text is produced

Can be difficult to automate or computerize

Textbooks & Chapters

Berelson, Bernard. Content Analysis in Communication Research.New York: Free Press, 1952.

Busha, Charles H. and Stephen P. Harter. Research Methods in Librarianship: Techniques and Interpretation.New York: Academic Press, 1980.

de Sola Pool, Ithiel. Trends in Content Analysis. Urbana: University of Illinois Press, 1959.

Krippendorff, Klaus. Content Analysis: An Introduction to its Methodology. Beverly Hills: Sage Publications, 1980.

Fielding, NG & Lee, RM. Using Computers in Qualitative Research. SAGE Publications, 1991. (Refer to Chapter by Seidel, J. ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’.)

Methodological Articles

Hsieh HF & Shannon SE. (2005). Three Approaches to Qualitative Content Analysis.Qualitative Health Research. 15(9): 1277-1288.

Elo S, Kaarianinen M, Kanste O, Polkki R, Utriainen K, & Kyngas H. (2014). Qualitative Content Analysis: A focus on trustworthiness. Sage Open. 4:1-10.

Application Articles

Abroms LC, Padmanabhan N, Thaweethai L, & Phillips T. (2011). iPhone Apps for Smoking Cessation: A content analysis. American Journal of Preventive Medicine. 40(3):279-285.

Ullstrom S. Sachs MA, Hansson J, Ovretveit J, & Brommels M. (2014). Suffering in Silence: a qualitative study of second victims of adverse events. British Medical Journal, Quality & Safety Issue. 23:325-331.

Owen P. (2012).Portrayals of Schizophrenia by Entertainment Media: A Content Analysis of Contemporary Movies. Psychiatric Services. 63:655-659.

Choosing whether to conduct a content analysis by hand or by using computer software can be difficult. Refer to ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’ listed above in “Textbooks and Chapters” for a discussion of the issue.

QSR NVivo: http://www.qsrinternational.com/products.aspx

Atlas.ti: http://www.atlasti.com/webinars.html

R- RQDA package: http://rqda.r-forge.r-project.org/

Rolly Constable, Marla Cowell, Sarita Zornek Crawford, David Golden, Jake Hartvigsen, Kathryn Morgan, Anne Mudgett, Kris Parrish, Laura Thomas, Erika Yolanda Thompson, Rosie Turner, and Mike Palmquist. (1994-2012). Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University. Available at: https://writing.colostate.edu/guides/guide.cfm?guideid=63 .

As an introduction to Content Analysis by Michael Palmquist, this is the main resource on Content Analysis on the Web. It is comprehensive, yet succinct. It includes examples and an annotated bibliography. The information contained in the narrative above draws heavily from and summarizes Michael Palmquist’s excellent resource on Content Analysis but was streamlined for the purpose of doctoral students and junior researchers in epidemiology.

At Columbia University Mailman School of Public Health, more detailed training is available through the Department of Sociomedical Sciences- P8785 Qualitative Research Methods.

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Methodology

Content Analysis | A Step-by-Step Guide with Examples

Published on 5 May 2022 by Amy Luo . Revised on 5 December 2022.

Books, newspapers, and magazines
Speeches and interviews
Web content and social media posts
Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding). In both types, you categorise or ‘code’ words, themes, and concepts within the texts and then analyse the results.

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects, or concepts in a set of historical or contemporary texts.

In addition, content analysis can be used to make qualitative inferences by analysing the meaning and semantic relationship of words and concepts.

Finding correlations and patterns in how concepts are communicated
Understanding the intentions of an individual, group, or institution
Identifying propaganda and bias in communication
Revealing differences in communication in different contexts
Analysing the consequences of communication content, such as the flow of information or audience responses

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Unobtrusive data collection

You can analyse communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

Highly flexible

You can conduct content analysis at any time, in any location, and at low cost. All you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions.

Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct research question .

Next, you follow these five steps.

Step 1: Select the content you will analyse

Based on your research question, choose the texts that you will analyse. You need to decide:

The medium (e.g., newspapers, speeches, or websites) and genre (e.g., opinion pieces, political campaign speeches, or marketing copy)
The criteria for inclusion (e.g., newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
The parameters in terms of date range, location, etc.

If there are only a small number of texts that meet your criteria, you might analyse all of them. If there is a large volume of texts, you can select a sample .

Step 2: Define the units and categories of analysis

Next, you need to determine the level at which you will analyse your chosen texts. This means defining:

The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
The set of categories that you will use for coding. Categories can be objective characteristics (e.g., aged 30–40, lawyer, parent) or more conceptual (e.g., trustworthy, corrupt, conservative, family-oriented).

Step 3: Develop a set of rules for coding

Coding involves organising the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Step 4: Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti , and Diction , which can help speed up the process of counting and categorising words and phrases.

Step 5: Analyse the results and draw conclusions

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Chapter 17. Content Analysis

Introduction.

Content analysis is a term that is used to mean both a method of data collection and a method of data analysis. Archival and historical works can be the source of content analysis, but so too can the contemporary media coverage of a story, blogs, comment posts, films, cartoons, advertisements, brand packaging, and photographs posted on Instagram or Facebook. Really, almost anything can be the “content” to be analyzed. This is a qualitative research method because the focus is on the meanings and interpretations of that content rather than strictly numerical counts or variables-based causal modeling. [1] Qualitative content analysis (sometimes referred to as QCA) is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest—in other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue. This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis. It is also a nice segue between our data collection methods (e.g., interviewing, observation) chapters and chapters 18 and 19, whose focus is on coding, the primary means of data analysis for most qualitative data. In many ways, the methods of content analysis are quite similar to the method of coding.

Although the body of material (“content”) to be collected and analyzed can be nearly anything, most qualitative content analysis is applied to forms of human communication (e.g., media posts, news stories, campaign speeches, advertising jingles). The point of the analysis is to understand this communication, to systematically and rigorously explore its meanings, assumptions, themes, and patterns. Historical and archival sources may be the subject of content analysis, but there are other ways to analyze (“code”) this data when not overly concerned with the communicative aspect (see chapters 18 and 19). This is why we tend to consider content analysis its own method of data collection as well as a method of data analysis. Still, many of the techniques you learn in this chapter will be helpful to any “coding” scheme you develop for other kinds of qualitative data. Just remember that content analysis is a particular form with distinct aims and goals and traditions.

An Overview of the Content Analysis Process

The first step: selecting content.

Figure 17.2 is a display of possible content for content analysis. The first step in content analysis is making smart decisions about what content you will want to analyze and to clearly connect this content to your research question or general focus of research. Why are you interested in the messages conveyed in this particular content? What will the identification of patterns here help you understand? Content analysis can be fun to do, but in order to make it research, you need to fit it into a research plan.

Figure 17.1. A Non-exhaustive List of "Content" for Content Analysis

To take one example, let us imagine you are interested in gender presentations in society and how presentations of gender have changed over time. There are various forms of content out there that might help you document changes. You could, for example, begin by creating a list of magazines that are coded as being for “women” (e.g., Women’s Daily Journal ) and magazines that are coded as being for “men” (e.g., Men’s Health ). You could then select a date range that is relevant to your research question (e.g., 1950s–1970s) and collect magazines from that era. You might create a “sample” by deciding to look at three issues for each year in the date range and a systematic plan for what to look at in those issues (e.g., advertisements? Cartoons? Titles of articles? Whole articles?). You are not just going to look at some magazines willy-nilly. That would not be systematic enough to allow anyone to replicate or check your findings later on. Once you have a clear plan of what content is of interest to you and what you will be looking at, you can begin, creating a record of everything you are including as your content. This might mean a list of each advertisement you look at or each title of stories in those magazines along with its publication date. You may decide to have multiple “content” in your research plan. For each content, you want a clear plan for collecting, sampling, and documenting.

The Second Step: Collecting and Storing

Once you have a plan, you are ready to collect your data. This may entail downloading from the internet, creating a Word document or PDF of each article or picture, and storing these in a folder designated by the source and date (e.g., “ Men’s Health advertisements, 1950s”). Sølvberg ( 2021 ), for example, collected posted job advertisements for three kinds of elite jobs (economic, cultural, professional) in Sweden. But collecting might also mean going out and taking photographs yourself, as in the case of graffiti, street signs, or even what people are wearing. Chaise LaDousa, an anthropologist and linguist, took photos of “house signs,” which are signs, often creative and sometimes offensive, hung by college students living in communal off-campus houses. These signs were a focal point of college culture, sending messages about the values of the students living in them. Some of the names will give you an idea: “Boot ’n Rally,” “The Plantation,” “Crib of the Rib.” The students might find these signs funny and benign, but LaDousa ( 2011 ) argued convincingly that they also reproduced racial and gender inequalities. The data here already existed—they were big signs on houses—but the researcher had to collect the data by taking photographs.

In some cases, your content will be in physical form but not amenable to photographing, as in the case of films or unwieldy physical artifacts you find in the archives (e.g., undigitized meeting minutes or scrapbooks). In this case, you need to create some kind of detailed log (fieldnotes even) of the content that you can reference. In the case of films, this might mean watching the film and writing down details for key scenes that become your data. [2] For scrapbooks, it might mean taking notes on what you are seeing, quoting key passages, describing colors or presentation style. As you might imagine, this can take a lot of time. Be sure you budget this time into your research plan.

Researcher Note

A note on data scraping : Data scraping, sometimes known as screen scraping or frame grabbing, is a way of extracting data generated by another program, as when a scraping tool grabs information from a website. This may help you collect data that is on the internet, but you need to be ethical in how to employ the scraper. A student once helped me scrape thousands of stories from the Time magazine archives at once (although it took several hours for the scraping process to complete). These stories were freely available, so the scraping process simply sped up the laborious process of copying each article of interest and saving it to my research folder. Scraping tools can sometimes be used to circumvent paywalls. Be careful here!

The Third Step: Analysis

There is often an assumption among novice researchers that once you have collected your data, you are ready to write about what you have found. Actually, you haven’t yet found anything, and if you try to write up your results, you will probably be staring sadly at a blank page. Between the collection and the writing comes the difficult task of systematically and repeatedly reviewing the data in search of patterns and themes that will help you interpret the data, particularly its communicative aspect (e.g., What is it that is being communicated here, with these “house signs” or in the pages of Men’s Health ?).

The first time you go through the data, keep an open mind on what you are seeing (or hearing), and take notes about your observations that link up to your research question. In the beginning, it can be difficult to know what is relevant and what is extraneous. Sometimes, your research question changes based on what emerges from the data. Use the first round of review to consider this possibility, but then commit yourself to following a particular focus or path. If you are looking at how gender gets made or re-created, don’t follow the white rabbit down a hole about environmental injustice unless you decide that this really should be the focus of your study or that issues of environmental injustice are linked to gender presentation. In the second round of review, be very clear about emerging themes and patterns. Create codes (more on these in chapters 18 and 19) that will help you simplify what you are noticing. For example, “men as outdoorsy” might be a common trope you see in advertisements. Whenever you see this, mark the passage or picture. In your third (or fourth or fifth) round of review, begin to link up the tropes you’ve identified, looking for particular patterns and assumptions. You’ve drilled down to the details, and now you are building back up to figure out what they all mean. Start thinking about theory—either theories you have read about and are using as a frame of your study (e.g., gender as performance theory) or theories you are building yourself, as in the Grounded Theory tradition. Once you have a good idea of what is being communicated and how, go back to the data at least one more time to look for disconfirming evidence. Maybe you thought “men as outdoorsy” was of importance, but when you look hard, you note that women are presented as outdoorsy just as often. You just hadn’t paid attention. It is very important, as any kind of researcher but particularly as a qualitative researcher, to test yourself and your emerging interpretations in this way.

The Fourth and Final Step: The Write-Up

Only after you have fully completed analysis, with its many rounds of review and analysis, will you be able to write about what you found. The interpretation exists not in the data but in your analysis of the data. Before writing your results, you will want to very clearly describe how you chose the data here and all the possible limitations of this data (e.g., historical-trace problem or power problem; see chapter 16). Acknowledge any limitations of your sample. Describe the audience for the content, and discuss the implications of this. Once you have done all of this, you can put forth your interpretation of the communication of the content, linking to theory where doing so would help your readers understand your findings and what they mean more generally for our understanding of how the social world works. [3]

Analyzing Content: Helpful Hints and Pointers

Although every data set is unique and each researcher will have a different and unique research question to address with that data set, there are some common practices and conventions. When reviewing your data, what do you look at exactly? How will you know if you have seen a pattern? How do you note or mark your data?

Let’s start with the last question first. If your data is stored digitally, there are various ways you can highlight or mark up passages. You can, of course, do this with literal highlighters, pens, and pencils if you have print copies. But there are also qualitative software programs to help you store the data, retrieve the data, and mark the data. This can simplify the process, although it cannot do the work of analysis for you.

Qualitative software can be very expensive, so the first thing to do is to find out if your institution (or program) has a universal license its students can use. If they do not, most programs have special student licenses that are less expensive. The two most used programs at this moment are probably ATLAS.ti and NVivo. Both can cost more than $500 [4] but provide everything you could possibly need for storing data, content analysis, and coding. They also have a lot of customer support, and you can find many official and unofficial tutorials on how to use the programs’ features on the web. Dedoose, created by academic researchers at UCLA, is a decent program that lacks many of the bells and whistles of the two big programs. Instead of paying all at once, you pay monthly, as you use the program. The monthly fee is relatively affordable (less than $15), so this might be a good option for a small project. HyperRESEARCH is another basic program created by academic researchers, and it is free for small projects (those that have limited cases and material to import). You can pay a monthly fee if your project expands past the free limits. I have personally used all four of these programs, and they each have their pluses and minuses.

Regardless of which program you choose, you should know that none of them will actually do the hard work of analysis for you. They are incredibly useful for helping you store and organize your data, and they provide abundant tools for marking, comparing, and coding your data so you can make sense of it. But making sense of it will always be your job alone.

So let’s say you have some software, and you have uploaded all of your content into the program: video clips, photographs, transcripts of news stories, articles from magazines, even digital copies of college scrapbooks. Now what do you do? What are you looking for? How do you see a pattern? The answers to these questions will depend partially on the particular research question you have, or at least the motivation behind your research. Let’s go back to the idea of looking at gender presentations in magazines from the 1950s to the 1970s. Here are some things you can look at and code in the content: (1) actions and behaviors, (2) events or conditions, (3) activities, (4) strategies and tactics, (5) states or general conditions, (6) meanings or symbols, (7) relationships/interactions, (8) consequences, and (9) settings. Table 17.1 lists these with examples from our gender presentation study.

Table 17.1. Examples of What to Note During Content Analysis

One thing to note about the examples in table 17.1: sometimes we note (mark, record, code) a single example, while other times, as in “settings,” we are recording a recurrent pattern. To help you spot patterns, it is useful to mark every setting, including a notation on gender. Using software can help you do this efficiently. You can then call up “setting by gender” and note this emerging pattern. There’s an element of counting here, which we normally think of as quantitative data analysis, but we are using the count to identify a pattern that will be used to help us interpret the communication. Content analyses often include counting as part of the interpretive (qualitative) process.

In your own study, you may not need or want to look at all of the elements listed in table 17.1. Even in our imagined example, some are more useful than others. For example, “strategies and tactics” is a bit of a stretch here. In studies that are looking specifically at, say, policy implementation or social movements, this category will prove much more salient.

Another way to think about “what to look at” is to consider aspects of your content in terms of units of analysis. You can drill down to the specific words used (e.g., the adjectives commonly used to describe “men” and “women” in your magazine sample) or move up to the more abstract level of concepts used (e.g., the idea that men are more rational than women). Counting for the purpose of identifying patterns is particularly useful here. How many times is that idea of women’s irrationality communicated? How is it is communicated (in comic strips, fictional stories, editorials, etc.)? Does the incidence of the concept change over time? Perhaps the “irrational woman” was everywhere in the 1950s, but by the 1970s, it is no longer showing up in stories and comics. By tracing its usage and prevalence over time, you might come up with a theory or story about gender presentation during the period. Table 17.2 provides more examples of using different units of analysis for this work along with suggestions for effective use.

Table 17.2. Examples of Unit of Analysis in Content Analysis

Every qualitative content analysis is unique in its particular focus and particular data used, so there is no single correct way to approach analysis. You should have a better idea, however, of what kinds of things to look for and what to look for. The next two chapters will take you further into the coding process, the primary analytical tool for qualitative research in general.

The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

Handling qualitative data
Transcripts
Field notes
Survey data and responses
Visual and audio data
Data organization
Data coding
Coding frame
Auto and smart coding
Organizing codes
Qualitative data analysis
Introduction

What is meant by content analysis?

Quantitative content analysis, practical examples of quantitative content analysis, using atlas.ti for content analysis.

Thematic analysis
Thematic analysis vs. content analysis
Narrative research
Phenomenological research
Discourse analysis
Grounded theory
Deductive reasoning
Inductive reasoning
Inductive vs. deductive reasoning
Qualitative data interpretation
Qualitative analysis software

Content analysis

Qualitative data collection usually leads to a strictly qualitative data analysis , but that need not always be the case. If a required analysis involves quantifying data , there are a number of data organization and data analysis methods that might be helpful in giving structure to raw data for frequency or statistical analysis.

This part of the guide will explore the idea of quantitative content analysis. Where quantitative analysis is useful, there are tools in qualitative data analysis software like ATLAS.ti that can reorganize your data for a content analysis that can supplement your use of qualitative research methods. Let's explore content analysis by providing a brief overview of this approach, then by looking at the quantitative aspects of content analysis.

Content analysis, in its simplest form, is a research method for interpreting and quantifying textual data , such as speeches, interviews , articles, social media posts , and so on. It allows researchers to sift through large volumes of data to identify patterns, themes, or biases and turn these into quantifiable variables that can be further analyzed.

At its core, content analysis combines elements of both qualitative and quantitative research methods . The method itself is systematic and replicable, aiming to condense a significant amount of text into fewer content categories based on explicit rules of coding. Yet, the interpretive component of understanding the context, nuances, and underlying meanings of the content being analyzed remains essential, borrowing heavily from qualitative research traditions.

This flexibility makes content analysis a versatile research approach applicable to numerous disciplines, such as communication, marketing, sociology, psychology, and political science, among others. Its uses range from studying cultural shifts over time, media representation of specific groups, political speeches, sentiments expressed in social media, and much more.

Differences from other research methods

The uniqueness of content analysis primarily stems from its ability to convert qualitative textual data into quantitative data , which can then be systematically examined. This capability sets it apart from many other research methodologies, each of which has its strengths and weaknesses.

Content analysis offers a less intrusive way of understanding a subject matter or phenomenon than more interpretive approaches. Unlike with an ethnographic or observational approach , there's no direct involvement with the study's subjects. Instead, the researcher examines texts and communications to uncover patterns, themes, or biases. This can be especially advantageous when researching sensitive topics or populations that are difficult to access.

Contrasting with quantitative methods associated with surveys and experiments, content analysis allows for a more contextual and nuanced understanding of data. While surveys and experiments can yield numerical data about attitudes, behaviors, and opinions, they often lack depth and fail to capture the richness of subjective experiences. Content analysis, on the other hand, provides more depth by enabling the researcher to delve into the intricacies of language and communication.

In comparison to discourse analysis , another method for studying a text, content analysis is more focused on the manifest content - the actual text - rather than the underlying discourses or power dynamics. Discourse analysis typically explores the relationships between text, context, and societal structures.

Lastly, unlike thematic analysis, which identifies, analyzes, and reports themes within data, content analysis goes a step further by transforming these themes into measurable variables. This quantification allows researchers to perform statistical analyses, giving content analysis an edge in examining the relationships between variables.

In essence, content analysis straddles the line between qualitative and quantitative methodologies, extracting the best of both worlds. It allows researchers to maintain the depth and richness of qualitative data while taking advantage of the numerical robustness of quantitative analysis. This makes content analysis a valuable addition to the researcher's toolkit.

Advantages of content analysis

Content analysis offers several advantages that make it a valuable tool for researchers in various disciplines. These advantages extend across its methodological flexibility, analytical depth, and practical adaptability.

Methodological flexibility : Content analysis allows for both qualitative and quantitative research, enabling researchers to explore themes in-depth while also making quantifiable comparisons. It's a versatile method, adaptable to a variety of research questions and data sources.
Rich, in-depth analysis : Content analysis provides a rich, textured understanding of data. Coding and categorizing allow researchers to delve into the complexities of language and communication, exploring nuanced meanings and connotations.
Unobtrusive method : As content analysis involves studying existing texts and communications, it is an unobtrusive method that does not require interaction with research participants. This can make it an excellent choice for sensitive research topics.
Ability to handle large data sets : Content analysis can manage large volumes of textual data, making it suitable for studies involving extensive texts or long timeframes. As we will see later in this section, the coding process in a content analysis approach can thus be relatively more straightforward.
Replicability : The systematic nature of content analysis lends itself to replicability. By creating explicit rules for coding and categorizing, other researchers can reproduce the study, enhancing its reliability.
Longitudinal analysis : Content analysis allows for longitudinal studies , as it can examine texts and communication over extended periods. This ability can be invaluable for tracking changes and trends over time.
Cost-effective : Compared to many other research methods , content analysis can be a cost-effective approach. Since it primarily involves analyzing existing texts, it often requires fewer resources than methods involving primary data collection .

The flexibility, depth, and practicality of content analysis make it a powerful tool for answering a range of research questions. Despite some limitations, which we will explore in the next section, the advantages of content analysis often make it an appealing choice for researchers.

Disadvantages of content analysis

While content analysis is a valuable tool, it's essential to acknowledge its limitations. These include:

Dependence on the quality of source materials : Content analysis relies on the quality of the source materials. If the documents or texts used for analysis are biased, incomplete, or inaccurate, it can lead to skewed results.
Contextual understanding : Texts often derive their meaning from context. Isolating texts for analysis can sometimes result in the loss of crucial contextual information, which may affect the overall interpretation of the results.
Coding and categorization limitations: The process of coding and categorizing can be time-consuming and prone to bias or error, potentially affecting the reliability and validity of the results.
Lack of depth compared to other qualitative methods : While content analysis allows for in-depth analysis, it may not reach the same level of depth as methods such as interviews or participant observations, particularly when exploring participants' feelings, thoughts, or motivations.
Difficulty in establishing causality : Content analysis can identify patterns and associations in the data but establishing causality can be challenging due to its primarily descriptive nature. As a result, conducting conceptual and relational analysis can prove challenging.
Focus on manifest content : Content analysis typically focuses on manifest content - the visible, surface content. Latent content, which refers to the underlying meanings or connotations, can sometimes be overlooked, limiting the depth of analysis.

Despite these limitations, with careful consideration and thoughtful application, content analysis remains a useful method. Understanding its potential drawbacks helps researchers apply the method more effectively and interpret their findings with due consideration. The next section will introduce qualitative content analysis, a specific type of content analysis that, while sharing some of the limitations mentioned here, offers unique advantages of its own.

What is qualitative content analysis?

Qualitative content analysis is a specific type of content analysis that primarily focuses on the interpretation and understanding of textual data. While it shares some similarities with its quantitative counterpart—such as the use of systematic and replicable methods—qualitative content analysis tends to dive deeper into the nuances, meanings, and contexts of the data.

At the heart of a qualitative analysis is the process of categorizing and coding data to identify patterns, themes, and relationships. The categories are usually derived inductively—that is, they emerge from the data itself rather than being pre-established. This approach offers a higher degree of flexibility and is especially beneficial when exploring a new or under-researched area.

An excellent example of the application of qualitative content analysis can be seen in qualitative health research. Consider a study examining patients' experiences with a chronic disease, such as diabetes. Here, qualitative content analysis would not only identify and categorize themes related to the disease experience, such as challenges in managing the condition, the impact on daily life, or interactions with healthcare professionals. It could also delve into the patients' psychological or emotional state regarding the management of their condition, as well as their attitudinal and behavioral responses to their condition and the healthcare system. For instance, the analysis might uncover feelings of frustration or resignation, proactive strategies for disease management, or attitudes toward healthcare advice.

Another distinctive characteristic of qualitative content analysis is its emphasis on context. Rather than viewing data in isolation, it considers the broader context in which the communication occurs. It takes into account aspects like the social, cultural, and historical background, the intention of the speaker, and the perception of the audience. This contextual understanding provides a richer, more nuanced analysis.

Also noteworthy is the iterative nature of qualitative content analysis. The process of coding, categorizing, and interpreting the data is not linear but recursive. As the analysis progresses, the researcher may revise the coding scheme, refine categories, and re-interpret the data, gradually enhancing the depth and precision of the analysis.

While qualitative content analysis provides an in-depth understanding of textual data, it can be more time-consuming and require more interpretative skill than quantitative content analysis. However, as we will explore in the next sections, both methods have their unique strengths and can complement each other in providing a comprehensive understanding of the data.

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Having explored content analysis in its broad scope and delved into qualitative analysis methods behind content analysis, we now shift our focus to quantitative content analysis. This approach retains the systematic, objective nature of content analysis but introduces a more numerical, count-based method of analyzing textual data . As such, it stands at the intersection of qualitative and quantitative research paradigms, offering the opportunity to transform the same data used in a qualitative analysis into a form that can be statistically analyzed.

In the subsequent subsections, we will define this research technique, detail the steps involved in its implementation, discuss its benefits and limitations, and illustrate its practical application with some examples. By the end of this section, you should have a solid understanding of quantitative content analysis and its role in your research toolkit.

Defining quantitative content analysis

This research approach, also known as deductive or 'classical' content analysis, is used to quantify patterns in textual data. This approach systematically transforms a text into numerical data, allowing for statistical analysis. This means that the content is categorized and counted to provide an objective, quantifiable overview of its characteristics.

Quantitative content analysis is predominantly concerned with manifest content—the visible, obvious components of the text. It examines what the text explicitly says rather than delving into possible latent meanings or underlying connotations. The text's elements—such as words, phrases, sentences, or specific themes—are coded into predefined categories, and the frequency of these categories is then quantified. This quantification allows for a more precise and broad-scale analysis of the data.

It's important to note that while quantification is a fundamental aspect of this approach, quantitative analysis still involves an element of interpretation. For instance, the development of coding schemes and the categorization of data require the researcher to understand and interpret the content. As such, even though it's labeled as 'quantitative,' this approach maintains a crucial qualitative component.

Despite this, the predominant focus of a quantitative approach is on the numerical, allowing it to provide a structured, replicable, and count-based exploration of textual data. The value of this approach lies in its ability to deliver an empirical, data-driven understanding of the content, enabling researchers to make statistical inferences and comparisons. In the next subsection, we will discuss the steps involved in conducting quantitative content analysis.

Steps in conducting quantitative analysis

The process typically involves several key steps:

Define the research question : The research question should be suitable for a quantitative approach. It should examine the frequency or patterns of certain aspects in a body of text.
Select the sample : Based on the research question, decide what texts to analyze. The texts could be anything from newspaper articles, social media posts , and speeches to transcripts of interviews or focus groups . Make sure to define a clear and replicable strategy for sample selection.
Define categories and develop a coding scheme : This step involves identifying the aspects of the text you are interested in and developing a set of categories to classify these aspects. Each category should be clearly defined, mutually exclusive, and collectively exhaustive.
Pilot-test the coding scheme : Before you start the actual analysis, it is advisable to pilot-test the coding scheme on a smaller subset of the sample. This helps ensure that your categories cover all relevant aspects of the content and that the coding scheme is reliable.
Code the content : In this step, the selected content is coded according to the coding scheme. Each part of the content that corresponds to a category is counted as a 'unit.' The units could be individual words, phrases, sentences, paragraphs, or even entire documents, depending on the research question and the nature of the categories.
Analyze and interpret the data : The coded data is then analyzed, often using statistical methods. You can calculate the frequencies of each category, compare frequencies between different parts of the text or different texts, or examine the relationships between categories. The analysis should be linked back to the research question and the wider context of the research.
Present the findings : Finally, the findings are reported in a clear and comprehensible manner, often using tables or graphs to display the frequencies of categories. It's also important to discuss the findings in the context of the research question and existing literature.

These steps provide a general framework for conducting quantitative content analysis. However, depending on the specifics of your research project, you may need to adapt or expand on these steps. For instance, if your research involves a large volume of text or multiple coders, you may need to include additional steps to ensure the consistency and reliability of the coding process.

With the process outlined above, here are a few practical examples illustrating a quantitative application of content analysis.

One common application of quantitative content analysis is in media studies. For instance, a researcher might use it to examine the representation of gender roles in a sample of popular movies. The researcher could define a set of categories reflecting different aspects of gender representation, such as the occupation, behaviors, or speech of male and female characters. By coding and quantifying these categories, the researcher could provide an empirical, data-driven analysis of gender representation in movies.

In political science, a researcher might use quantitative content analysis to analyze politicians' speeches. For example, they could examine the frequency of certain themes or keywords to gain insights into a politician's focus areas or ideological leanings. This approach allows for a systematic, objective assessment of political communication.

In health research, quantitative content analysis could be used to analyze patient reviews of healthcare providers. Categories could be developed to capture aspects like the quality of care, communication skills, waiting times, etc. By coding and quantifying these categories, the researcher could identify patterns and trends in patient satisfaction.

These examples illustrate the breadth of applications for quantitative content analysis. Whether you're exploring social issues, political discourses, customer reviews, or any other type of textual data, quantitative content analysis provides a method for systematically coding, categorizing, and quantifying your data. By offering a way to transform qualitative data into a form that can be statistically analyzed, it adds a valuable tool to your research toolkit.

ATLAS.ti is particularly useful to researchers who want to conduct content analysis from both quantitative and qualitative approaches . For research inquiries that rely more on interpretation to identify patterns and abundance in the data, then thematic analysis may be more appropriate for your study.

On the other hand, when you are relying on counting words or phrases to determine key insights, a quantitative approach to content analysis will be a useful component of your study's methodology. To facilitate your analysis, a number of tools in ATLAS.ti will provide you with the ability to conduct a quantitative inquiry.

Word Frequencies and Concepts

A word cloud is a common but meaningful visualization in qualitative research , as it shows what words appear more often than others. The greater the frequency of a word, the closer to the center of the cloud that word is placed. While a word cloud relies on statistics, it presents the analysis in a visual manner that allows your research audience to quickly grasp the meaning.

ATLAS.ti's Word Cloud tool determines the frequencies and creates the visualization quickly and easily. All the researcher needs to do is select the documents they want to analyze. They can then refine their word cloud by including or excluding certain classes of words, such as adverbs or determiners, or by setting a required minimum frequency for the word to appear in the cloud.

The Concepts tool works similarly to Word Clouds, except it relies on collocations of words to determine which phrases are more prevalent in your data than others.

Once the researcher selects the data they want to analyze, the words included in the most common concepts will appear in a visualization resembling a word cloud. Hovering over any of these words will show which phrases are relevant to that word and where those phrases can be found in the data. This allows the researcher to look at the phrase in context and add codes as necessary.

Text Search

Most people are familiar with a text search function in a word processor or a web browser. ATLAS.ti's Text Search tool has a similar search capability but also employs language models developed through machine learning to help you expand your search quickly and efficiently.

When entering a word to search, the researcher can also choose from a list of synonyms they can include in their search. In research on sustainability, for example, the words "preserve" and "save" might be similar enough to be included in one inquiry. As a result, ATLAS.ti allows the researcher to choose related words relevant to their search.

Searching for inflected forms is also important to a quantitative approach to content analysis. Given that "preserves," "preserving," and "preservation" all come from the word "preserve," it's only appropriate to include them in one search. The option in ATLAS.ti to search for inflected forms makes it easy to search the data for all possible versions of a word. And in Text Search, all results can be easily coded so those codes can be used in content analysis.

Insightful content analysis with ATLAS.ti

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Approaches to Qualitative Research in Mathematics Education pp 365–380 Cite as

Qualitative Content Analysis: Theoretical Background and Procedures

Philipp Mayring 6
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Part of the book series: Advances in Mathematics Education ((AME))

Qualitative Content Analysis designates a bundle of text analysis procedures integrating qualitative and quantitative steps of analysis, which makes it an approach of mixed methods. This contribution defines it with a background of quantitative content analysis and compares it with other social science text analysis approaches (e.g. Grounded Theory). The basic theoretical and methodological assumptions are elaborated: reference to a communication model, rule orientation of analysis, theoretical background of those content analytical rules, categories in the center of the procedure, necessity of pilot testing of categories and rules, necessity of intra- and inter-coder reliability checks. Then the two main procedures, inductive category formation and deductive category assignment, are described by step models. Finally the procedures are compared with similar techniques (e.g. codebook analysis) and strengths and weaknesses are discussed.

Qualitative content analysis

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Home » Content Analysis – Methods, Types and Examples

Content Analysis – Methods, Types and Examples

Table of Contents

Content Analysis

Definition:

Content analysis is a research method used to analyze and interpret the characteristics of various forms of communication, such as text, images, or audio. It involves systematically analyzing the content of these materials, identifying patterns, themes, and other relevant features, and drawing inferences or conclusions based on the findings.

Content analysis can be used to study a wide range of topics, including media coverage of social issues, political speeches, advertising messages, and online discussions, among others. It is often used in qualitative research and can be combined with other methods to provide a more comprehensive understanding of a particular phenomenon.

Types of Content Analysis

There are generally two types of content analysis:

Quantitative Content Analysis

This type of content analysis involves the systematic and objective counting and categorization of the content of a particular form of communication, such as text or video. The data obtained is then subjected to statistical analysis to identify patterns, trends, and relationships between different variables. Quantitative content analysis is often used to study media content, advertising, and political speeches.

Qualitative Content Analysis

This type of content analysis is concerned with the interpretation and understanding of the meaning and context of the content. It involves the systematic analysis of the content to identify themes, patterns, and other relevant features, and to interpret the underlying meanings and implications of these features. Qualitative content analysis is often used to study interviews, focus groups, and other forms of qualitative data, where the researcher is interested in understanding the subjective experiences and perceptions of the participants.

Methods of Content Analysis

There are several methods of content analysis, including:

Conceptual Analysis

This method involves analyzing the meanings of key concepts used in the content being analyzed. The researcher identifies key concepts and analyzes how they are used, defining them and categorizing them into broader themes.

Content Analysis by Frequency

This method involves counting and categorizing the frequency of specific words, phrases, or themes that appear in the content being analyzed. The researcher identifies relevant keywords or phrases and systematically counts their frequency.

Comparative Analysis

This method involves comparing the content of two or more sources to identify similarities, differences, and patterns. The researcher selects relevant sources, identifies key themes or concepts, and compares how they are represented in each source.

Discourse Analysis

This method involves analyzing the structure and language of the content being analyzed to identify how the content constructs and represents social reality. The researcher analyzes the language used and the underlying assumptions, beliefs, and values reflected in the content.

Narrative Analysis

This method involves analyzing the content as a narrative, identifying the plot, characters, and themes, and analyzing how they relate to the broader social context. The researcher identifies the underlying messages conveyed by the narrative and their implications for the broader social context.

Content Analysis Conducting Guide

Here is a basic guide to conducting a content analysis:

Define your research question or objective: Before starting your content analysis, you need to define your research question or objective clearly. This will help you to identify the content you need to analyze and the type of analysis you need to conduct.
Select your sample: Select a representative sample of the content you want to analyze. This may involve selecting a random sample, a purposive sample, or a convenience sample, depending on the research question and the availability of the content.
Develop a coding scheme: Develop a coding scheme or a set of categories to use for coding the content. The coding scheme should be based on your research question or objective and should be reliable, valid, and comprehensive.
Train coders: Train coders to use the coding scheme and ensure that they have a clear understanding of the coding categories and procedures. You may also need to establish inter-coder reliability to ensure that different coders are coding the content consistently.
Code the content: Code the content using the coding scheme. This may involve manually coding the content, using software, or a combination of both.
Analyze the data: Once the content is coded, analyze the data using appropriate statistical or qualitative methods, depending on the research question and the type of data.
Interpret the results: Interpret the results of the analysis in the context of your research question or objective. Draw conclusions based on the findings and relate them to the broader literature on the topic.
Report your findings: Report your findings in a clear and concise manner, including the research question, methodology, results, and conclusions. Provide details about the coding scheme, inter-coder reliability, and any limitations of the study.

Applications of Content Analysis

Content analysis has numerous applications across different fields, including:

Media Research: Content analysis is commonly used in media research to examine the representation of different groups, such as race, gender, and sexual orientation, in media content. It can also be used to study media framing, media bias, and media effects.
Political Communication : Content analysis can be used to study political communication, including political speeches, debates, and news coverage of political events. It can also be used to study political advertising and the impact of political communication on public opinion and voting behavior.
Marketing Research: Content analysis can be used to study advertising messages, consumer reviews, and social media posts related to products or services. It can provide insights into consumer preferences, attitudes, and behaviors.
Health Communication: Content analysis can be used to study health communication, including the representation of health issues in the media, the effectiveness of health campaigns, and the impact of health messages on behavior.
Education Research : Content analysis can be used to study educational materials, including textbooks, curricula, and instructional materials. It can provide insights into the representation of different topics, perspectives, and values.
Social Science Research: Content analysis can be used in a wide range of social science research, including studies of social media, online communities, and other forms of digital communication. It can also be used to study interviews, focus groups, and other qualitative data sources.

Examples of Content Analysis

Here are some examples of content analysis:

Media Representation of Race and Gender: A content analysis could be conducted to examine the representation of different races and genders in popular media, such as movies, TV shows, and news coverage.
Political Campaign Ads : A content analysis could be conducted to study political campaign ads and the themes and messages used by candidates.
Social Media Posts: A content analysis could be conducted to study social media posts related to a particular topic, such as the COVID-19 pandemic, to examine the attitudes and beliefs of social media users.
Instructional Materials: A content analysis could be conducted to study the representation of different topics and perspectives in educational materials, such as textbooks and curricula.
Product Reviews: A content analysis could be conducted to study product reviews on e-commerce websites, such as Amazon, to identify common themes and issues mentioned by consumers.
News Coverage of Health Issues: A content analysis could be conducted to study news coverage of health issues, such as vaccine hesitancy, to identify common themes and perspectives.
Online Communities: A content analysis could be conducted to study online communities, such as discussion forums or social media groups, to understand the language, attitudes, and beliefs of the community members.

Purpose of Content Analysis

The purpose of content analysis is to systematically analyze and interpret the content of various forms of communication, such as written, oral, or visual, to identify patterns, themes, and meanings. Content analysis is used to study communication in a wide range of fields, including media studies, political science, psychology, education, sociology, and marketing research. The primary goals of content analysis include:

Describing and summarizing communication: Content analysis can be used to describe and summarize the content of communication, such as the themes, topics, and messages conveyed in media content, political speeches, or social media posts.
Identifying patterns and trends: Content analysis can be used to identify patterns and trends in communication, such as changes over time, differences between groups, or common themes or motifs.
Exploring meanings and interpretations: Content analysis can be used to explore the meanings and interpretations of communication, such as the underlying values, beliefs, and assumptions that shape the content.
Testing hypotheses and theories : Content analysis can be used to test hypotheses and theories about communication, such as the effects of media on attitudes and behaviors or the framing of political issues in the media.

When to use Content Analysis

Content analysis is a useful method when you want to analyze and interpret the content of various forms of communication, such as written, oral, or visual. Here are some specific situations where content analysis might be appropriate:

When you want to study media content: Content analysis is commonly used in media studies to analyze the content of TV shows, movies, news coverage, and other forms of media.
When you want to study political communication : Content analysis can be used to study political speeches, debates, news coverage, and advertising.
When you want to study consumer attitudes and behaviors: Content analysis can be used to analyze product reviews, social media posts, and other forms of consumer feedback.
When you want to study educational materials : Content analysis can be used to analyze textbooks, instructional materials, and curricula.
When you want to study online communities: Content analysis can be used to analyze discussion forums, social media groups, and other forms of online communication.
When you want to test hypotheses and theories : Content analysis can be used to test hypotheses and theories about communication, such as the framing of political issues in the media or the effects of media on attitudes and behaviors.

Characteristics of Content Analysis

Content analysis has several key characteristics that make it a useful research method. These include:

Objectivity : Content analysis aims to be an objective method of research, meaning that the researcher does not introduce their own biases or interpretations into the analysis. This is achieved by using standardized and systematic coding procedures.
Systematic: Content analysis involves the use of a systematic approach to analyze and interpret the content of communication. This involves defining the research question, selecting the sample of content to analyze, developing a coding scheme, and analyzing the data.
Quantitative : Content analysis often involves counting and measuring the occurrence of specific themes or topics in the content, making it a quantitative research method. This allows for statistical analysis and generalization of findings.
Contextual : Content analysis considers the context in which the communication takes place, such as the time period, the audience, and the purpose of the communication.
Iterative : Content analysis is an iterative process, meaning that the researcher may refine the coding scheme and analysis as they analyze the data, to ensure that the findings are valid and reliable.
Reliability and validity : Content analysis aims to be a reliable and valid method of research, meaning that the findings are consistent and accurate. This is achieved through inter-coder reliability tests and other measures to ensure the quality of the data and analysis.

Advantages of Content Analysis

There are several advantages to using content analysis as a research method, including:

Objective and systematic : Content analysis aims to be an objective and systematic method of research, which reduces the likelihood of bias and subjectivity in the analysis.
Large sample size: Content analysis allows for the analysis of a large sample of data, which increases the statistical power of the analysis and the generalizability of the findings.
Non-intrusive: Content analysis does not require the researcher to interact with the participants or disrupt their natural behavior, making it a non-intrusive research method.
Accessible data: Content analysis can be used to analyze a wide range of data types, including written, oral, and visual communication, making it accessible to researchers across different fields.
Versatile : Content analysis can be used to study communication in a wide range of contexts and fields, including media studies, political science, psychology, education, sociology, and marketing research.
Cost-effective: Content analysis is a cost-effective research method, as it does not require expensive equipment or participant incentives.

Limitations of Content Analysis

While content analysis has many advantages, there are also some limitations to consider, including:

Limited contextual information: Content analysis is focused on the content of communication, which means that contextual information may be limited. This can make it difficult to fully understand the meaning behind the communication.
Limited ability to capture nonverbal communication : Content analysis is limited to analyzing the content of communication that can be captured in written or recorded form. It may miss out on nonverbal communication, such as body language or tone of voice.
Subjectivity in coding: While content analysis aims to be objective, there may be subjectivity in the coding process. Different coders may interpret the content differently, which can lead to inconsistent results.
Limited ability to establish causality: Content analysis is a correlational research method, meaning that it cannot establish causality between variables. It can only identify associations between variables.
Limited generalizability: Content analysis is limited to the data that is analyzed, which means that the findings may not be generalizable to other contexts or populations.
Time-consuming: Content analysis can be a time-consuming research method, especially when analyzing a large sample of data. This can be a disadvantage for researchers who need to complete their research in a short amount of time.

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Difficulties and challenges experienced by nurses in eldercare institutions in Albania: A qualitative content analysis

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliations Nursing Department, Health University of Applied Sciences Tyrol FH Gesundheit Tirol, Innsbruck, Austria, Department of Nursing Science and Gerontology, UMIT - Private University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria

Roles Data curation, Investigation, Writing – original draft

Affiliation Nursing Department, University of Shkodra “Luigj Gurakuqi,” Shkoder, Albania

Roles Conceptualization, Data curation, Methodology, Software, Validation

Affiliation Department of History, Martin-Luther-Universität Halle/Wittenberg, Wittenberg, Germany

Roles Writing – review & editing

Affiliation Research and Innovation Unit, Health University of Applied Sciences Tyrol/FH Gesundheit Tirol, Innsbruck, Austria

Nertila Podgorica,
Emiljano Pjetri,
Andreas W. Müller (M. A.),
Susanne Perkhofer

Published: March 27, 2024
https://doi.org/10.1371/journal.pone.0300774
Reader Comments

Introduction

The global and Albanian populations of elderly people are steadily increasing. It is estimated that the number of elderly adults requiring care in Albania will rise from 90.9 thousand to 130.4 thousand by 2030. Despite the envisaged increase in the number and life expectancy of the elderly population in Albania, which will result in an increased demand for nursing care, little is known about the difficulties and challenges that nurses face while providing care for elderly Albanian individuals.

To explore the difficulties and challenges nurses experience while caring for elderly people in Albanian eldercare institutions.

The study employed a qualitative design using purposive sampling of 20 nurses in 8 eldercare institutions who participated in face-to-face semi-structured interviews. The audio-recorded interviews were transcribed and subsequently subjected to analysis using Graneheim and Lundman’s qualitative conventional content analysis. Data analysis was supported by the qualitative data analysis software MAXQDA 2020. The reporting of this study followed the consolidated criteria for reporting qualitative research (COREQ) checklist.

Five key categories emerged from data analysis: (1) professional difficulties, (2) educational difficulties, (3) relationship challenges, (4) increased mental stress, and (5) participation in advocacy. This study showed that nursing staff experienced many barriers, challenges, and unmet needs when implementing care for elderly people in long-term care facilities.

The findings indicate that nurses working in eldercare institutions faced significant challenges in caring for elderly people. Nurses need more legal, financial, educational, and emotional support. The study indicates that more organizational and national support is necessary for nursing staff to care for elderly people in eldercare Albanian institutions properly. Eldercare institution leaders need to recognize the importance of their role in overcoming the barriers and providing adequate support for their staff in caring for elderly people.

Citation: Podgorica N, Pjetri E, Müller (M. A.) AW, Perkhofer S (2024) Difficulties and challenges experienced by nurses in eldercare institutions in Albania: A qualitative content analysis. PLoS ONE 19(3): e0300774. https://doi.org/10.1371/journal.pone.0300774

Editor: Sylvester Chidi Chima, University of KwaZulu-Natal College of Health Sciences, SOUTH AFRICA

Received: March 25, 2022; Accepted: March 5, 2024; Published: March 27, 2024

Copyright: © 2024 Podgorica et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because of participants privacy.Data are available from the RCSEQ Privatuniversität UMIT GmbH und der fh Gesundheit Tirol 6060 Hall in Tirol, Eduard Wallnöfer-Zentrum 1 - TN: 0508648/3942, E: [email protected] Institutional Data Access / Ethics Committee (contact via [email protected] ) for researchers who meet the criteria for access to confidential data.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Albania is currently experiencing a rapid and irreversible aging of its population. According to a report by the Albanian Institute of Statistics, by 2031, the population will slightly decrease to 2.74 million, while the old-age dependency ratio is forecast to reach 32.7%. Furthermore, the number of persons aged 65 or older is expected to increase to 21.8% of the total population. Statistical data from the United Nations report that the population in Albania is expected to decrease to 2.42 million by 2050 and 1.94 million by 2070, and the elderly dependency ratio will increase to 40.7% by 2050 and 76.1% by 2070 [ 1 – 3 ]. It is estimated that the number of older people in Albania potentially in need of long-term care will increase from 90.9 thousand to 130.4 thousand by 2030 [ 1 , 3 , 4 ]. These statistical data show that the healthcare system in Albania is facing challenges and that there are difficulties and problems in the long-term care (LTC) system, which is still underdeveloped in Albania [ 1 , 5 ]. Albania currently lacks a formal LTC system [ 1 , 3 ], as there is no official definition of LTC. There are provisions for long-term care in various laws, such as healthcare, social welfare, and social security, but a proper system is not defined [ 1 , 6 ]. The Ministry of Health and Social Protection of Albania (MHSP) manages all these services. Elderly services are provided through social services in public service centers, such as community, residential, and regular or palliative day centers. These public services are financed from the state budget and municipal budgets; social services are provided in non-public service centers [ 5 , 7 – 10 ]. Nonetheless, these public service centers face limitations in capacity and capability to provide adequate eldercare. They serve different groups of people, including people over 60 who are disabled or chronically ill or who have been abandoned by their families. There are 39 residential centers for the elderly in Albania, of which 14 provide services in the Tirana district. In comparison, the remaining 25 offer services in Korça (6 centers), Shkodra (5 centers), Berat (3 centers), Durres, Vlorë and Elbasan (2 centers each), Lezhë, Dibër, Gjirokastër, Fier and Kukës (1 center each) [ 1 , 11 ]. The services for older people in these centers are guaranteed following the standards approved by the Albanian government: multidisciplinary teams assess the needs of older people and design and implement the time plan of individual interventions to meet the identified needs [ 4 , 5 , 10 ]. The staff of these centers is mainly composed of doctors and nurses. At the same time, there are only a few social workers and nursing assistants, even though the profession of social worker and nursing assistant is provided for in the authorized structure [ 3 , 4 ]. This occurs because neither university curricula nor training institutions in Albania provide adequate training for nursing assistants and social workers in caring for older people [ 8 , 9 ]. Nurses are considered the backbone of the healthcare system in providing essential healthcare services to the population. The knowledge, training, behavior, and care nurses provide significantly impact the quality of care, as studies indicate that care providers’ perspective influences the quality of care delivered to older individuals [ 12 ]. Nurses face various problems and difficulties when implementing nursing care and meeting their responsibilities to older people at eldercare institutions. These problems and challenges are related to the facilities’ physical environment and technical equipment, as well as the educational level of nurses [ 4 , 8 , 13 ]. The lack of staff training and a suitable environment is proven by the evidence to affect the quality of care provided to older people in eldercare facilities [ 9 ]. Empirical data revealed that nurses face difficulties in caring for the elderly due to communication and relationship issues with them, their relatives, or staff [ 8 , 10 , 14 ]. While working in LTC facilities, nurses care for people with dementia and other elderly with various health conditions who express uncertainty in decision-making regarding therapy or other decisions, especially at the end of their lives. As a result of being confronted with different moral dilemmas and being unable to protect older people, the nurses encounter various emotional problems [ 15 , 16 ]. These challenges intensify when elderly residents exhibit increased aggressive behavior, leading to psychological distress in nurses, reduced working hours, attrition from the profession, and, ultimately, a shortage of nurses [ 14 , 17 ]. In addition to the above issues, nurses working in LTC facilities confront many other difficulties and challenges in providing qualitative care to older people [ 14 ]. Since there is insufficient data on nurses’ challenges and difficulties while caring for older people in eldercare institutions in Albania, the evidence mentioned above comes mainly from studies conducted in other countries [ 1 , 9 , 14 ]. Most older individuals in Albania receive care from their families, particularly when they face serious illnesses or lose the ability to work [ 1 , 3 , 4 , 6 ]. Older individuals in Albania have been cared for by family members, with children taking on the responsibility until the end of their parents’ lives [ 8 ]. However, this traditional care system is undergoing a rapid transformation [ 10 ] due to increased life expectancy, low fertility, increased female employment, the migration wave, and changes in Albanian culture. These changes have heightened the demand for care in eldercare institutions, as older individuals have fewer opportunities to reside with their adult children. The swift pace of these changes has resulted in a dearth of studies examining the experiences of nurses and the difficulties they encounter in caring for the elderly within Albanian eldercare institutions. Only a few studies have identified the barriers that affect the care of older people in Albania.

Hence, this study explored the difficulties and challenges nurses experience while caring for elderly people in Albanian eldercare institutions.

Materials and methods

Study design and setting.

This qualitative study was conducted in 2019–2020 using the conventional content analysis approach. This method facilitates the depiction of experiences and the identification, summarization, and categorization of manifest and latent content within lived experiences [ 18 ]. Additionally, it aligns with the study’s objective, aiming to delve into the difficulties and challenges encountered by nurses providing care for elderly individuals in eldercare institutions in Albania.

Participants of this study were recruited from four different types of institutions, such as daily eldercare centers, public nursing homes, palliative care centers, and private nursing homes ( Table 1 ), as there is no infrastructure for a long-term care system in Albania [ 1 , 8 ].

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Study population and eligibility criteria

Nurses meeting the inclusion criteria were invited to participate in the research. The inclusion criteria comprised the nurses’ voluntary participation in the study, a minimum of one year of clinical work experience in providing care for older individuals in eldercare facilities, proficiency in Albanian communication, strong verbal skills, and the capability to articulate and share their experiences in detail. Before the researchers started interviewing, they tested the interview guide with two nurses to ensure the questions were clear, understandable, and easy to answer. These two nurses did not participate in the study, and their interviews were not included in the analysis.

Sampling procedure and sample size

A purposive sample of nurses was recruited through social contact with nurses and directors of each facility based on their potential to provide information about their nursing knowledge and experiences for the study [ 19 ]. The initial 30 contacts who met the inclusion criteria and were willing to describe their nursing experiences consistent with the purpose of the study received a consent form by mail or e-mail and were verbally informed about the project. Once the participants signed and accepted, the consent form was mailed to the principal investigators before the start of the interview. Of these nurses, only 20 were interviewed, and the sample size was determined based on the principles of saturation as proposed by Malterud et al. [ 20 ]. Sampling was discontinued when no new information was obtained from study participants. All nurses voluntarily participated and were free to stop or withdraw from participating at any time without negative consequences.

Data collection tool

A semi-structured interview guide was employed in this study, developed based on a review of the literature and modified to align with the specific objectives of the research [ 21 ]. The guide was used to collect detailed data regarding the difficulties and challenges nurses face when caring for the elderly living in eldercare institutions in Albania. An information sheet was used to manage the sociodemographic data of the study participants. The interview guide, initially prepared in English, underwent translation into Albanian (the national language) to ensure better understanding by the participants. To verify the appropriateness of the questions, a testing phase was conducted before the commencement of data collection.

Data collection procedure

After obtaining participants’ informed consent, data was collected through in-depth and semi-structured individual face-to-face interviews ( Table 2 ). The interviews, conducted by leading Albanian researchers (NP, EP) possessing a Ph.D. in nursing science and expertise in qualitative research, occurred between June 2019 and March 2020. Before the interviews, researchers communicated with participants, aiming to establish a friendly atmosphere. This involved introducing themselves, describing the study’s objectives and the reasons for their interest in the subject, and responding to possible questions from the participants. These efforts contributed to building a positive rapport and trust between researchers and participants. Additionally, participants received assurances regarding their freedom to answer questions and participate voluntarily, the confidentiality of their information, and the option to withdraw from the study at any point. Participants were given the autonomy to choose the location and timing of their interviews.

https://doi.org/10.1371/journal.pone.0300774.t002

Interviews were conducted without time constraints, scheduled outside regular working hours, in a separate room at the participant’s workplace, ensuring privacy with only the interviewer and participant present. The interview process continued until all relevant experiences of the participants had been fully explained, and comprehensive responses had been obtained from the interviewees. A digital audio recorder was used to record the interviews. In semi-structured interviews, the questions are not fixed and predetermined but evolved based on the interview process. Follow-up questions followed each interview to obtain further information or clarify the participants’ answers. The interviews were digitally recorded in Albanian, and researchers noted non-verbal data such as mood, tone of voice, facial expressions, and posture during the sessions. Interviews lasted 40–70 minutes and continued until saturation occurred after the 15th interview, but after coding these interviews, all authors agreed to conduct more interviews to ensure saturation [ 20 , 22 ]. The first 15 interviews were conducted between June and December 2019, two others in January 2020, and three additional interviews were conducted between February 27 and March 5, 2020, before the COVID-19 restrictions started in Albania. In all, 20 in-depth individual interviews with nurses were completed. There were no withdrawals from the study and no repetition of any interviews.

Data analysis

The data underwent analysis following Graneheim and Lundman’s qualitative conventional content analysis [ 22 ]. Data analysis began shortly after the first interview by manually transcribing the recorded audio data and typing it directly into the computer using Microsoft Word software. After anonymizing the transcriptions by removing personal identifiers and assigning unique identification numbers, the transcripts were reviewed for accuracy using spot-checking, taking a small set of transcripts (3 of 20) by the researchers who speak Albanian and English (NP; EP), who read and reread all the transcripts carefully and translated them afterward into English following Santos et al. [ 23 ] strategy for translating qualitative interviews and research reports which suggest having language experts on research teams and translation and back-translation issues directly considered within the team. A professional translator then checked the translated transcripts. The back translation was done again by the same researchers. In the next step, three researchers (NP, EP, and AM) repeated the reading of the transcripts with an open mind to gain an overall impression and a general understanding of each transcript. Transcribed and translated data sets were imported into MAXQDA 2020, a software supporting qualitative data organization and coding [ 24 ]. In the next step, open data coding began by carefully reading the data line-by-line, extracting and coding each word, phrase, or paragraph aligned with the study’s purpose. In the categorization phase, codes were consistently compared and ranked according to their affiliations, including their differences and similarities. Codes were then subcategorized, sorted, and combined into fewer, more comprehensive categories. To assign findings to the same category, researchers approached the text with a degree of interpretation (i.e., open to underlying meanings). The first and second authors did the coding and initial categorization independently, and the third did the validation and suggestions for changes. This iterative and non-linear process [ 18 , 25 ] moved between parts and the whole until no new codes or categories could be added. All subcategories and categories had been defined, and the final coding scheme was created by the first three authors, one of whom was a senior female researcher with a Ph.D. in nursing science and extensive experience in qualitative research, and the other two male authors, one of whom was a lecturer with a Ph.D. in nursing and the other an expert in qualitative research in social and medical sciences. The other authors then reviewed the coding scheme and agreed upon or discussed it until a consensus was reached.

To ensure the reliability of the findings, the authors discussed the meanings, subcategories, and categories that emerged from the analysis following the specific objectives of this study. Additionally, meetings with study participants were conducted to ensure accuracy, facilitate discussion, and seek consensus on the identified subcategories and categories [ 26 ]. Participants’ suggestions were considered, and adjustments were made as deemed appropriate. Consolidated Criteria for Reporting Qualitative Studies (COREQ) was used to provide a comprehensive report [ 27 ]. Finally, to ensure transparency [ 28 ], each subcategory was enhanced with citations. Each citation is in reference to the specific numbered interview. An example of the construction of the coding scheme is presented in Fig 1 .

https://doi.org/10.1371/journal.pone.0300774.g001

Ethical considerations

The Research Committees of two universities approved the ethical application for Scientific and Ethical Issues (RCSEQ, no. 2117) of the University of Medicine, Health and Technology (UMIT), Hall in Tirol, Austria, and the Ethical Committee, Faculty of Natural Sciences, Department of Nursing, University of Shkodra (Prot,no. 20/2. Dt. 12/04/2018) Shkoder Albania. The study was conducted following the tenets of the Declaration of Helsinki [ 29 ] and had the approval of the head of each institution. Confidentiality of the collected data was ensured, and written informed consent was obtained from all research participants. In addition, participants were free to decline participation or withdraw at any stage of the research process. All interviews were recorded with the permission of the participants, and all the audio files were securely stored in password-protected computers.

Demographic characteristics

The findings were extracted from the analysis of 20 in-depth individual interviews with nurses. The characteristics of the participants are presented in Table 3 .

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Five categories with fourteen corresponding subcategories were developed to capture nurses’ difficulties and challenges when caring for elderly people in different eldercare institutions in Albania ( Fig 2 ).

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Professional difficulties

Within the realm of professional challenges, nurses highlighted three primary subcategories: inadequate physical environment, insufficient material resources, and constraints related to time and staff. All participants consistently pointed out various obstacles when caring for older adults, such as a shortage of staff, an overwhelming number of residents, time constraints, limited space, and inappropriate structural conditions.

Inadequate physical environment.

One major issue highlighted by the nurses is the lack of suitable facilities in Albania, aside from private institutions, for providing essential care to the elderly. The environmental conditions within these institutions are not conducive for nurses to effectively carry out their responsibilities. The interviewed nurses work in nursing homes, daily elderly care centers, and palliative care centers. These institutions accommodate residents of various ages, many of whom suffer from mental illnesses or other health conditions.

In this study, certain participants shared challenges related to the physical design and layout of the ward environment, particularly in creating a familiar setting to address responsive behaviors in residents. “They want to stay in single rooms , but the center doesn’t have such conditions because of the lack of funds” (Participant 16). Participants stated that it was difficult to respect residents’ preferences and even intimacies who share rooms with other residents with dementia. “Because the facility does not have private rooms or good conditions , we respect their preferences as much as possible . Residents stay in double rooms . However , older residents with severe conditions like paralysis or advanced dementia have quads . Old females remain separated from the males , but their intimacy is maintained only when they go to the bathroom” (Participant 3).

Inadequate material resources.

The participants confronted the challenge of insufficient resources, hindering their ability to provide adequate care for the elderly. Nurses felt that they were pushing the limits of their patients’ safety due to the unavailability of the necessary equipment. They were also angry about the systematic factors that did not provide them with sufficient wages and the support that they needed. “There are even residents who drink and then get drunk . They are a problem , especially when working nights . Because our institution has no entertainment , they ask us to go out and play dominoes . They disturb others , or they even cause violence . This happens because we can’t do anything , and no one helps or supports us because of the disorganization and the lack of the necessary materials to offer a daily life to the residents“( Participant 4).

Insufficient time and staff.

The participants highlighted the challenge of inadequate staffing levels, with most expressing concerns about insufficient numbers of nurses and social workers per resident. “We can’t always give care when they need it . We don’t have enough staff in one shift“( Participant 8). “There should be more staff . One nurse has to do almost everything . With only one nurse and one care worker , there are thirty people” (Participant 5). Nurses reported difficulty caring for elderly people due to workload, too many residents, and lack of time and staff. “We were warned to take care of them . But it’s too tricky because two people can’t monitor fifty elderly people“( Participant 15). In addition, the lack of staff, time, and increased workload made it difficult for nurses to focus on qualitative care. Some reported feeling guilty about not being able to provide the best care for elderly people. “The most common problem is their need for attention and their wishes to be fulfilled . Considering the time and workload , we try to fulfil their wishes somehow” (Participant 18). “It is a challenge to listen to the old residents . The staff has to provide services to them . We don’t have enough time to make sure we’re doing right“( Participant 20). “Sometimes we feel guilty . But the negligence in the care of the older people happens because of the lack of staff . Because of the lack of funds , the staff is insufficient” (Participant 19).

Educational difficulties

Under this category, three major subcategories were identified to explain educational issues: lack of knowledge, inadequate education, and learning by doing.

Lack of knowledge.

The participants underscored a significant challenge related to the lack of knowledge in geriatric care, emphasizing that university curricula did not adequately equip them with expertise and specialization in caring for the elderly. “Even at the university , there was a lack of information about caring for older adults . Perhaps because we were the first generation , this may have happened . The university is not consolidated . We need to get knowledge , especially for older people with dementia , Parkinson’s , and all the other diseases . We need to be specialized senior nurses” (Participant 11). “We first participated in the first training regarding caring for older adults 20 years ago . We need to get trained and informed more” (Participant 14).

Insufficient education.

Participants highlighted a significant issue related to insufficient education in geriatric care, expressing concerns about the lack of training that can impact the quality of care provided to elderly residents. One nurse emphasized this challenge, stating, “I have not taken part in any training related to the care of the elderly . I have tried to find a course related to these issues , but the classes I have attended have not been helpful , considering my profession . However , we are more aware of what we should and shouldn’t do . Without information about such cases , you can even do things wrong . Even if we learn from our work , we still need proper training to fulfill our duties and protect our residents“( Participant 9). Participants advocated for continuous education not only for staff but also for the elderly residents. One participant expressed the need for training in caring for and communicating with older adults, emphasizing that this aspect had been neglected in the past: “I think the staff and the older people need to be trained in caring for and communicating with older adults , something which has never happened before” (Participant 11). Nurses asked for training in caring for elderly adults, especially dementia care. As one nurse noted, “We haven’t been trained on how to care for older adults or how to care for dementia . There is a lack of information in the college . It can improve the quality of care we give to the elderly and help us to be more confident and professional if we have knowledge and training about elderly care , especially dementia care” (Participant 1). The participants underscored the need for professional education to provide specialized care to elderly people, particularly those with dementia at different stages and symptoms. Despite attending some training sessions, they expressed a desire for more comprehensive education in caring for older adults. One nurse noted, “We’ve attended training on diagnosing the patient , but it wasn’t explicitly related to the elderly” (Participant 15). “ We recently attended a training on the standards we should meet , organized by two psychology professors . But we need more training in caring for older adults . The problem is that our institution does not offer proper care for the elderly , and we , the nurses , are not even specialized in caring for the elderly” (Participant 13).

Learning by doing.

A prevalent theme among participants was the concept of learning by doing, indicating that their knowledge and skills in caring for older adults were primarily acquired through practical experience rather than formal education. Participants acknowledged the valuable contributions of previous staff, emphasizing their higher education and multilingual abilities. One participant stated, “We also learned a lot from the previous staff . They had higher education and could speak many languages” (Participant 12). Nurses learned to solve unforeseen problems while caring for older adults without guidance or resources or only through their family education: “Human resources were too limited in terms of staff and professors . We learned by working , although we did not have enough information . I think we got something done” (Participant 2). “I don’t have any update on elderly care even though I have been working here for almost thirty years . We learn by working and practicing it daily” (Participant 4). “Rather than being trained in an institution , we have been working based on our family training” (Participant 2).

Relationship challenges

Many nurses cited staff-resident-family relationships as barriers to appropriate care for older adults. They emphasized the lack of communication and conflict between and among staff, residents, and families.

Lack of communication among staff and residents.

Numerous participants highlighted the communication challenges they face, particularly when interacting with elderly individuals diagnosed with dementia. While nurses acknowledged the importance of patience in these situations, time constraints often hindered their ability to engage in meaningful communication. The need to prioritize residents’ physical needs was emphasized by one participant, who stated, “I will listen to them , but if I am in charge of something else , I will tell them to wait if it is not urgent . Sometimes , they’re reasonable . Other times , they act like they don’t understand what I’m saying . We have to prioritize their physical needs” (Participant 9). The difficulties in communication were further compounded by the diverse range of residents with varying ages and health conditions. Instances of alcoholism, previous incarceration, and both physical and mental disabilities among residents contributed to communication problems. The nursing staff, despite facing these challenges, made efforts to adapt to the residents’ diverse needs. However, the complexity of their job increased when residents encountered problems among themselves, requiring intervention and separation. As described by one participant, “Some people here are alcoholics or have been in prison , as such we may have communication problems with them . We also have people with physical and mental disabilities . Even though it’s difficult , we try to adapt ourselves to them . In general , they’re not aggressive , but there are cases in which they have problems with each other . Sometimes , we have to intervene and separate them from each other” (Participant 8).

This theme underscores the intricate nature of communication challenges within eldercare institutions, emphasizing the diverse backgrounds and conditions of the residents that contribute to the complexity of interactions for nursing staff.

Communication among staff.

Regarding communication among staff members, most participants expressed overall positive communication within the team. However, some nurses acknowledged minor disagreements arising from different positions and cultural backgrounds. One nurse highlighted this aspect, stating, “In some ways , we face problems communicating with each other because we have different cultures and ways of caring for patients . I take care of them as a nurse . Another staff member helps as a caretaker” (Participant 17).

Meanwhile, communication between nurses and doctors was generally perceived as effective and correct. Nurses reported rarely experiencing conflicts with doctors, emphasizing that doctors typically make decisions and nurses follow their instructions. As described by one participant: “We never happen to conflict with the doctors , and we are better at communicating with them” (Participant 7). The collaborative dynamic between nurses and doctors contributes to the smooth functioning of the team.

Conflicts among staff and relatives.

Numerous participants highlighted challenges in communication and misunderstandings with the families of residents, particularly those with dementia. The participants expressed frustration by stating: “We sometimes have problems communicating with the families because they do not accept the situation of their relatives“( Participant 10). Conflicts with family members were reported, often stemming from dilemmas faced by relatives regarding the care of their loved ones. One respondent emphasized the complexities involved: “Sometimes the relatives are in a dilemma whether to do the right thing or not to bring their father or mother , so they often need us to listen or confirm when they’re right . There are cases in which family members impose things on the patients . For example , they force them to eat more than they want , which is a source of conflict for us” (Participant 2).

Increased mental stress

Nurses in this study mentioned their psychological stress while caring for elderly residents with various conditions. They emphasized the residents’ aggressive behavior and the nurses’ negative attitudes.

Aggressive behaviors of residents.

Participants recounted distressing experiences involving aggressive behaviors exhibited by residents and their families. One participant highlighted the challenge, stating, “There are cases when family members don’t cooperate with us and insult us . This makes our job even more difficult” (Participant 18). Despite understanding that aggressive behavior in elderly residents is often a consequence of dementia, nurses reported feeling hurt and demoralized. Instances of verbal insults, physical aggression, and biting were mentioned, creating emotional challenges for the nursing staff: “We care for elders and treat them like family members . Sometimes they insult us verbally and become aggressive . They hit us or bite us , and when they do that , we feel bad and insulted” (Participant 15). While acknowledging the difficulties, nurses emphasized the importance of effective communication in managing such situations. One nurse stressed: “I think it’s more important than anything else to be able to communicate . Every older resident has their character . Some seniors are grateful to us for our services .

On the other hand , some of them may throw food at our uniform if we don’t do what they want . Sometimes , there are even insults . But let’s not consider their abuses“( Participant 12). “Yes , sometimes we have aggressive residents . However , we do our best to calm them down through conversation . We try not to give them tranquilizers , but sometimes we have to use them and feel guilty about it … there is no other way” (Participant 6).

Nurses’ negative attitudes.

Most participants highlighted concerns about the negative attitudes exhibited by some nursing staff, emphasizing a lack of a sense of mission and emotional engagement. This was seen as a hindrance to holistic care, reducing care to mechanical and physical tasks. One participant stated: “In my opinion , the attitude of some of the nurses was wrong . They do not have a sense of mission or a sense of professional duty; they have no emotions” (Participant 1). “I have been working here for an extended period . Sometimes , it looks like I’m acting like a robot … When we care for residents with dementia , we have to talk to them more , listen to them , and give them some stimulation . Still , sometimes I feel pressured by the resident , relatives , or other staff . I change diapers , take residents to physiotherapy , and feed them lunch or dinner; lots of stress , that’s all . I’m sorry , but many residents need me to help them physically“( Participant 13).

Participation in advocacy

The participants indicated that they are often confronted with ethical issues when they advocate for their elderly residents. These are listed in three subcategories: ethical problems related to elder rights, futile nutrition and medications, and cultural influences.

Ethical problems related to elderly people’s rights.

Nurses highlighted ethical dilemmas related to older people’s rights, particularly in institutionalization cases where informed consent may be lacking. The challenge lies in the fact that many elderly individuals are institutionalized without being adequately informed or asked for their consent. Nurses expressed the difficulty of defending the rights of older people in such situations. According to one participant: “Most of them are here at the request of their family members . The family members often give them the wrong information by telling them that our center is a hospital . When we tell them the truth , they understand the reality as the days pass” (Participant 7). “Family members usually decide to bring their parents to our center . They may be abroad or busy with their work . But we can’t keep them against their will , so we also try to get their consent” (Participant 6). “The official document is part of every palliative care report . But we never use it because the resident is not informed , and we cannot do anything wrong . The older adult must be well informed about what he has and what we serve him . It’s the patient’s right to sign in , and he must accept everything with full awareness . We haven’t used an official document very often . Most colleagues do not know much about it“( Participant 11).

Futile nutrition and medication.

Feeding and futile therapies were other challenges expressed by the participants. Nurses said they often have to feed elderly people even when they do not want food. In some cases, family members insisted on providing food to unconscious patients, believing it would contribute to their well-being. “This happens because the patient is unconscious at the family member’s request in the last phase . There have been cases in the last days of life where patients have refused to eat . However , we must feed them , even though we cannot give them basic food , but bland food for better treatment” (Participant 17). “The relatives insist on giving the patient futile therapy until the last moment of his life , even though they know we don’t give curative therapy . The doctor must tell them what’s best . However , the relatives are supposed to decide . I always try to convince the families not to give the patient such things . They are worthless and only harm the patient . But if they still insist on it , I try to give the patient the minimum of the doctor’s prescribed treatment” (Participant 8).

In certain instances, nurses also argued that elderly residents receive futile therapy that may harm rather than benefit them. However, the decision lies with the family: “Some residents have dementia , crises , or emotional problems . If we consult the doctor , we can treat with or without the patient’s consent . This makes me feel horrible because I do nothing to advocate for my old resident” (Participant 19). “Older adult residents who have dementia are refusers of treatment . When this happens , we try to find ways to make it possible for them to receive the medication without consent . For example , we could give them the medicine in their food . I know this doesn’t seem right and has nothing to do with my role as advocator , but I am forced to do so ” (Participant 16).

Cultural aspects influence.

Nurses participating in the study highlighted significant challenges arising from cultural aspects that impact their ability to advocate for elderly residents in Albanian eldercare institutions. The participants expressed a sense of powerlessness in advocating for residents with no family to request visits to the facility. Moreover, cultural norms and taboos restrict the disclosure of significant health problems and diagnoses to elderly individuals, emphasizing the influence of societal beliefs on communication and decision-making in healthcare settings. “Nobody asks them if they want to come here . The government brings them here , and we have to accept them in our institution without any advocacy regarding information or other things” (Participant 13). “Based on the information we get , we try to be compassionate and polite . We are somehow restricted from discussing the pathology with the patient because of the taboos and mentality of our society . Patients often accept the information given to them by family members and do not ask many questions . This is a part of the culture in Albania . So , we give patients the news their relatives want them to know . We do nothing to protect the older adult residents here , which makes me feel terrible” (Participant 3). “Because the familiars insist on not telling them the truth , most of our residents do not have this information about their diagnosis . They ask us not to tell patients the truth . However , this phenomenon of not telling the truth about the diagnosis to the patient is part of the Albanian culture . It is considered a problem for us” (Participant 1).

This qualitative study sheds light on the multifaceted challenges faced by nurses in Albanian eldercare institutions, categorizing their experiences into five key areas. Following previous literature, these results showed that the work environment impacts nurses’ work and attitudes toward caring for older adults [ 30 , 31 ]. Likewise, in their studies, Rush et al. (2017), Adibelli & Kılıç (2013), and Kong et al. (2022) found that the physical work environment influenced the work of the nurse and their attitude toward caring for elderly people and their health [ 30 , 31 ]. According to this study’s findings, Albanian eldercare institution leaders, policymakers, and other stakeholders need to consider the importance of the physical environment, redesign the institutional setting to be more appropriate, and make the institution more home-like for elderly people. In an attempt to provide high-quality care, it is necessary to have enough nurses [ 14 , 31 ]. The participants in this study were overloaded with work and did not have enough time to care for the elderly because of inadequate standards and time to provide proper care. They reported feeling guilty for not providing the best care due to insufficient time and staff.

Similarly, Kang & Hur (2021) found that the shortage of nurses prevents communication and care for older persons, especially those with dementia [ 17 ]. The results of this study show that Albanian eldercare institutions need adequate staffing standards and financial support to implement appropriate care for elderly residents successfully. This study revealed that the participants had inadequate education and training in caring for the elderly, especially those with different mental and physical conditions. Most of the participants highlighted problems related to a lack of knowledge and skills in eldercare, especially in caring for older people with dementia. These results are reinforced by other authors’ findings, who emphasized the importance of staff and elderly education in elderly care in their studies [ 17 , 30 , 32 – 34 ]. Thus, to provide the best care for older people, the leaders of Albanian eldercare institutions and universities should provide continuous training on care for the elderly.

Adequate care requires excellent and healthy communication, mutual trust and understanding, and good teamwork and relationships [ 14 , 35 , 36 ]. Similarly to previous studies, this study found that nurses experienced relationship and communication difficulties with residents, especially those with dementia and their families [ 14 , 17 , 30 , 32 , 33 ]. These difficulties are mainly related to the Albanian culture, where families care for elderly people. Likewise, Adibelli & Kılıç (2013) and Nasrabadi et al. (2021) describe these difficulties as primarily related to cultural problems [ 30 , 37 ]. In addition, the nurse participants in this study reported issues of communication and relationships within the team. These findings support previous reports on the importance of communication, relationships, and collaboration while caring for elderly people [ 14 , 38 ]. Eldercare institution leaders need to recognize the importance of their role in team relationships and provide education and support to improve communication and relationships between staff, residents, and their families.

Another result showed that most of the nurses included in this study suffered from increased psychological stress when caring for elderly residents, especially those with various mental health problems, such as dementia. Emotional distress and stress were experiences of nurses whom the aggressive behavior of some residents with dementia had hurt. Adding to previous findings, participants in this study reported hurtful experiences while caring for some elderly people [ 14 , 17 , 35 ]. These experiences led to negative attitudes among nurses, who consistently said they were apathetic and mechanical in treating residents with dementia. They also had difficulty treating and respecting them as human beings. Accordingly, nurses who experienced aggressive behavior from elderly residents felt anxiety, frustration, and discomfort while caring for them, according to Kang & Hur’s (2021) meta-synthesis [ 17 ]. Consistent with Kang & Hur’s (2021) findings, participants in this study reported difficulty communicating with residents in the face of aggression. Therefore, measures such as using safety alarm devices and counseling programs to support nurses’ psychological and physical health should be taken to protect nurses facing emotional stress and provide qualitative care [ 14 , 17 ].

Another significant challenge described by participants was advocacy related to ethical issues in caring for older adults in long-term care facilities. They found it challenging to advocate for elderly residents regarding self-determination, truth-telling, and trust. Along the same lines, Arcadi & Ventimiglia (2017) and Luca et al. (2021) emphasized the need for nurses to develop a trusting relationship [ 16 , 39 ]. Participants in this study believed that advocacy skills are needed when ethical issues arise, such as when the needs of the elderly are not met or considered, as also mentioned by Josse-Eklund et al. (2014), when excessive or unnecessary therapy may occur against the expressed will of the patient, or when patients with dementia are unable to express their will regarding therapeutic decisions made by family members or physicians [ 40 ]. In such situations, participants emphasized that they cannot protect the rights of elderly residents, most of whom are placed in institutions against their will and without their consent. Family members make decisions, although older people have the right to information, choice, and approval following relevant regulations and laws [ 9 ]. Despite the various conflicts nurses can encounter with colleagues or families, the importance of nurses’ ability to guarantee and protect the rights and wishes of older people in the centers where they work has been described and highlighted in earlier studies [ 16 ]. Managers of facilities for eldercare should consider this fact, and more support should be provided. In addition, participants in this study believed that cultural aspects such as social relationships influence the views of the patient, family, and nurses on a clinical situation and cause different difficulties for nurses in caring for the elderly. Indeed, Vries et al. (2019) support these findings, arguing that cultural aspects such as ethnicity, religiosity, and spirituality, as well as the level of health literacy, greatly influence people’s decisions when making plans that consist not only of advanced directives for resuscitation but also wishes at the end of their lives [ 7 , 9 , 41 ]. In the present study, despite the different organizational contexts of the eight eldercare institutions, the participants described similar challenges in caring for elderly residents. The results of this study indicate that Albanian eldercare institutions / long-term care facilities need to develop a supportive organizational system and provide support for the successful implementation of elderly care. Furthermore, the Albanian government must offer more financial and legitimate aid for eldercare institutions.

Limitations and strengths

This study comes with some limitations commonly found in qualitative studies. Firstly, it relies predominantly on the perspectives of nurses, potentially limiting the exploration of broader frameworks and contextual conditions. Secondly, as a qualitative study, this study is limited in scope and relies on the subjective interpretations and decisions of the involved researchers. Additionally, the study is confined to eight eldercare institutions in three regions of Albania, which may restrict the generalizability of the findings to other contexts or countries.

Nevertheless, this is one of the few studies conducted in Albania regarding nursing staff challenges and difficulties in eldercare facilities. Thus, the current study creates an opportunity for further extensive national research into nurses’ knowledge, perception, attitude, and other relevant areas that may affect older people and their care in Albania. Future studies should consider the experience of all healthcare professionals involved in providing care to older people in all Albanian eldercare institutions.

In this study, a significant number of nurses highlighted substantial challenges in providing care to elderly patients. The key obstacles identified encompassed the absence of specialized long-term care infrastructure, insufficient social support and funding, inadequate knowledge and training in geriatric care, and the absence of clear policy guidelines for the care of older individuals. The findings underscore the urgent need for enhanced legal and financial support at the national level and economic and educational support at the facility level to enhance staffing conditions and the physical environment in long-term care facilities, thereby improving the overall care for older individuals. Establishing suitable long-term care environments across all regions, along with comprehensive guidelines, enhanced nurses’ knowledge, improved working conditions, and ongoing training are imperative. Initiatives should include reinforcing geriatric nursing programs in Albanian universities to adequately equip professionals in addressing the healthcare requirements of the elderly. Long-term care facilities should implement geriatric education at the facility level for staff and families, focusing on knowledge enrichment, improved communication skills, and ethical problem-solving practices. Leaders of these facilities play a pivotal role in recognizing and addressing the challenges faced by nurses, emphasizing their crucial contribution to ensuring quality care for older individuals.

Supporting information

https://doi.org/10.1371/journal.pone.0300774.s001

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Open access
Published: 28 March 2024

Using the consolidated Framework for Implementation Research to integrate innovation recipients’ perspectives into the implementation of a digital version of the spinal cord injury health maintenance tool: a qualitative analysis

John A Bourke 1 , 2 , 3 ,
K. Anne Sinnott Jerram 1 , 2 ,
Mohit Arora 1 , 2 ,
Ashley Craig 1 , 2 &
James W Middleton 1 , 2 , 4 , 5

BMC Health Services Research volume 24 , Article number: 390 ( 2024 ) Cite this article

Metrics details

Despite advances in managing secondary health complications after spinal cord injury (SCI), challenges remain in developing targeted community health strategies. In response, the SCI Health Maintenance Tool (SCI-HMT) was developed between 2018 and 2023 in NSW, Australia to support people with SCI and their general practitioners (GPs) to promote better community self-management. Successful implementation of innovations such as the SCI-HMT are determined by a range of contextual factors, including the perspectives of the innovation recipients for whom the innovation is intended to benefit, who are rarely included in the implementation process. During the digitizing of the booklet version of the SCI-HMT into a website and App, we used the Consolidated Framework for Implementation Research (CFIR) as a tool to guide collection and analysis of qualitative data from a range of innovation recipients to promote equity and to inform actionable findings designed to improve the implementation of the SCI-HMT.

Data from twenty-three innovation recipients in the development phase of the SCI-HMT were coded to the five CFIR domains to inform a semi-structured interview guide. This interview guide was used to prospectively explore the barriers and facilitators to planned implementation of the digital SCI-HMT with six health professionals and four people with SCI. A team including researchers and innovation recipients then interpreted these data to produce a reflective statement matched to each domain. Each reflective statement prefaced an actionable finding, defined as alterations that can be made to a program to improve its adoption into practice.

Five reflective statements synthesizing all participant data and linked to an actionable finding to improve the implementation plan were created. Using the CFIR to guide our research emphasized how partnership is the key theme connecting all implementation facilitators, for example ensuring that the tone, scope, content and presentation of the SCI-HMT balanced the needs of innovation recipients alongside the provision of evidence-based clinical information.

Conclusions

Understanding recipient perspectives is an essential contextual factor to consider when developing implementation strategies for healthcare innovations. The revised CFIR provided an effective, systematic method to understand, integrate and value recipient perspectives in the development of an implementation strategy for the SCI-HMT.

Trial registration

Peer Review reports

Injury to the spinal cord can occur through traumatic causes (e.g., falls or motor vehicle accidents) or from non-traumatic disease or disorder (e.g., tumours or infections) [ 1 ]. The onset of a spinal cord injury (SCI) is often sudden, yet the consequences are lifelong. The impact of a SCI is devastating, with effects on sensory and motor function, bladder and bowel function, sexual function, level of independence, community participation and quality of life [ 2 ]. In order to maintain good health, wellbeing and productivity in society, people with SCI must develop self-management skills and behaviours to manage their newly acquired chronic health condition [ 3 ]. Given the increasing emphasis on primary health care and community management of chronic health conditions, like SCI, there is a growing responsibility on all parties to promote good health practices and minimize the risks of common health complications in their communities.

To address this need, the Spinal Cord Injury Health Maintenance Tool (SCI-HMT) was co-designed between 2018 and 2023 with people living with SCI and their General Practitioners (GPs) in NSW, Australia [ 4 ] The aim of the SCI-HMT is to support self-management of the most common and arguably avoidable potentially life-threatening complications associated with SCI, such as mental health crises, autonomic dysreflexia, kidney infections and pressure injuries. The SCI-HMT provides comprehensible information with resources about the six highest priority health areas related to SCI (as indicated by people with SCI and GPs) and was developed over two phases. Phase 1 focused on developing a booklet version and Phase 2 focused on digitizing this content into a website and smartphone app [ 4 , 5 ].

Enabling the successful implementation of evidence-based innovations such as the SCI-HMT is inevitably influenced by contextual factors: those dynamic and diverse array of forces within real-world settings working for or against implementation efforts [ 6 ]. Contextual factors often include background environmental elements in which an intervention is situated, for example (but not limited to) demographics, clinical environments, organisational culture, legislation, and cultural norms [ 7 ]. Understanding the wider context is necessary to identify and potentially mitigate various challenges to the successful implementation of those innovations. Such work is the focus of determinant frameworks, which focus on categorising or classing groups of contextual determinants that are thought to predict or demonstrate an effect on implementation effectiveness to better understand factors that might influence implementation outcomes [ 8 ].

One of the most highly cited determinant frameworks is the Consolidated Framework for Implementation Research (CFIR) [ 9 ], which is often posited as an ideal framework for pre-implementation preparation. Originally published in 2009, the CFIR has recently been subject to an update by its original authors, which included a literature review, survey of users, and the creation of an outcome addendum [ 10 , 11 ]. A key contribution from this revision was the need for a greater focus on the place of innovation recipients, defined as the constituency for whom the innovation is being designed to benefit; for example, patients receiving treatment, students receiving a learning activity. Traditionally, innovation recipients are rarely positioned as key decision-makers or innovation implementers [ 8 ], and as a consequence, have not often been included in the application of research using frameworks, such as the CFIR [ 11 ].

Such power imbalances within the intersection of healthcare and research, particularly between those receiving and delivering such services and those designing such services, have been widely reported [ 12 , 13 ]. There are concerted efforts within health service development, health research and health research funding, to rectify this power imbalance [ 14 , 15 ]. Importantly, such efforts to promote increased equitable population impact are now being explicitly discussed within the implementation science literature. For example, Damschroder et al. [ 11 ] has recently argued for researchers to use the CFIR to collect data from innovation recipients, and that, ultimately, “equitable population impact is only possible when recipients are integrally involved in implementation and all key constituencies share power and make decisions together” (p. 7). Indeed, increased equity between key constituencies and partnering with innovation recipients promotes the likelihood of sustainable adoption of an innovation [ 4 , 12 , 14 ].

There is a paucity of work using the updated CFIR to include and understand innovation recipients’ perspectives. To address this gap, this paper reports on a process of using the CFIR to guide the collection of qualitative data from a range of innovation recipients within a wider co-design mixed methods study examining the development and implementation of SCI-HMT. The innovation recipients in our research are people living with SCI and GPs. Guided by the CFIR domains (shown in the supplementary material), we used reflexive thematic analysis [ 16 ]to summarize data into reflective summaries, which served to inform actionable findings designed to improve implementation of the SCI-HMT.

The procedure for this research is multi-stepped and is summarized in Fig. 1 . First, we mapped retrospective qualitative data collected during the development of the SCI-HMT [ 4 ] against the five domains of the CFIR in order to create a semi-structured interview guide (Step 1). Then, we used this interview guide to collect prospective data from health professionals and people with SCI during the development of the digital version of the SCI-HMT (Step 2) to identify implementation barriers and facilitators. This enabled us to interpret a reflective summary statement for each CFIR domain. Lastly, we developed an actionable finding for each domain summary. The first (RESP/18/212) and second phase (2019/ETH13961) of the project received ethical approval from The Northern Sydney Local Health District Human Research Ethics Committee. The reporting of this study was conducted in line with the consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines [ 17 ]. All methods were performed in accordance with the relevant guidelines and regulations.

Procedure of synthesising datasets to inform reflective statements and actionable findings. a Two health professionals had a SCI (one being JAB); b Two co-design researchers had a SCI (one being JAB)

Step one: retrospective data collection and analysis

We began by retrospectively analyzing the data set (interview and focus group transcripts) from the previously reported qualitative study from the development phase of the SCI-HMT [ 4 ]. This analysis was undertaken by two team members (KASJ and MA). KASJ has a background in co-design research. Transcript data were uploaded into NVivo software (Version 12: QSR International Pty Ltd) and a directed content analysis approach [ 18 ] was applied to analyze categorized data a priori according to the original 2009 CFIR domains (intervention characteristics, outer setting, inner setting, characteristics of individuals, and process of implementation) described by Damschroder et al. [ 9 ]. This categorized data were summarized and informed the specific questions of a semi-structured interview guide. The final output of step one was an interview guide with context-specific questions arranged according to the CFIR domains (see supplementary file 1). The interview was tested with two people with SCI and one health professional.

Step two: prospective data collection and analysis

In the second step, semi-structured interviews were conducted by KASJ (with MA as observer) with consenting healthcare professionals who had previously contributed to the development of the SCI-HMT. Healthcare professionals included GPs, Nurse Consultants, Specialist Physiotherapists, along with Health Researchers (one being JAB). In addition, a focus group was conducted with consenting individuals with SCI who had contributed to the SCI-HMT design and development phase. The interview schedule designed in step one above guided data collection in all interviews and the focus group.

The focus group and interviews were conducted online, audio recorded, transcribed verbatim and uploaded to NVivo software (Version 12: QSR International Pty Ltd). All data were subject to reflexive, inductive and deductive thematic analysis [ 16 , 19 ] to better understand participants’ perspectives regarding the potential implementation of the SCI-HMT. First, one team member (KASJ) read transcripts and began a deductive analysis whereby data were organized into CFIR domains-specific dataset. Second, KASJ and JAB analyzed this domain-specific dataset to inductively interpret a reflective statement which served to summarise all participant responses to each domain. The final output of step two was a reflective summary statement for each CFIR domain.

Step three: data synthesis

In the third step we aimed to co-create an actionable finding (defined as tangible alteration that can be made to a program, in this case the SCI-HMT [ 20 ]) based on each domain-specific reflective statement. To achieve this, three codesign researchers (KAS and JAB with one person with SCI from Step 2 (deidentified)) focused on operationalising each reflective statement into a recommended modification for the digital version of the SCI-HMT. This was an iterative process guided by the specific CFIR domain and construct definitions, which we deemed salient and relevant to each reflective statement (see Table 2 for example). Data synthesis involved line by line analysis, group discussion, and repeated refinement of actionable findings. A draft synthesis was shared with SCI-HMT developers (JWM and MA) and refinement continued until consensus was agreed on. The final outputs of step three were an actionable finding related to each reflective statement for each CFIR domain.

The characteristics of both the retrospective and prospective study participants are shown in Table 1 . The retrospective data included data from a total of 23 people: 19 people with SCI and four GPs. Of the 19 people with SCI, 12 participated in semi-structured interviews, seven participated in the first focus group, and four returned to the second focus group. In step 2, four people with SCI participated in a focus group and six healthcare professionals participated in one-on-one semi-structured interviews. Two of the healthcare professionals (a GP and a registrar) had lived experience of SCI, as did one researcher (JAB). All interviews and focus groups were conducted either online or in-person and ranged in length between 60 and 120 min.

In our overall synthesis, we actively interpreted five reflective statements based on the updated CFIR domain and construct definitions by Damschroder et al. [ 11 ]. Table 2 provides a summary of how we linked the updated CFIR domain and construct definitions to the reflective statements. We demonstrate this process of co-creation below, including illustrative quotes from participants. Importantly, we guide readers to the actionable findings related to each reflective statement in Table 2 . Each actionable statement represents an alteration that can be made to a program to improve its adoption into practice.

Participants acknowledged that self-management is a major undertaking and very demanding, as one person with SCI said, “ we need to be informed without being terrified and overwhelmed”. Participants felt the HMT could indeed be adapted, tailored, refined, or reinvented to meet local needs. For example, another person with SCI remarked:

“Education needs to be from the get-go but in bite sized pieces from all quarters when readiness is most apparent… at all time points , [not just as a] a newbie tool or for people with [long-term impairment] ” (person with SCI_02).

Therefore, the SCI-HMT had to balance complexity of content while still being accessible and engaging, and required input from both experts in the field and those with lived experience of SCI, for example, a clinical nurse specialist suggested:

“it’s essential [the SCI-HMT] is written by experts in the field as well as with collaboration with people who have had a, you know, the lived experience of SCI” (healthcare professional_03).

Furthermore, the points of contact with healthcare for a person with SCI can be challenging to navigate and the SCI-HMT has the potential to facilitate a smoother engagement process and improve communication between people with SCI and healthcare services. As a GP suggested:

“we need a tool like this to link to that pathway model in primary health care , [the SCI-HMT] it’s a great tool, something that everyone can read and everyone’s reading the same thing” (healthcare professional_05).

Participants highlighted that the ability of the SCI-HMT to facilitate effective communication was very much dependent on the delivery format. The idea of digitizing the SCI-HMT garnered equal support from people with SCI and health care professionals, with one participant with SCI deeming it to be “ essential” ( person with SCI_01) and a health professional suggesting a “digitalized version will be an advantage for most people” (healthcare professional_02).

Outer setting

There was strong interest expressed by both people with SCI and healthcare professionals in using the SCI-HMT. The fundamental premise was that knowledge is power and the SCI-HMT would have strong utility in post-acute rehabilitation services, as well as primary care. As a person with SCI said,

“ we need to leave the [spinal unit] to return to the community with sufficient knowledge, and to know the value of that knowledge and then need to ensure primary healthcare provider [s] are best informed” (person with SCI_04).

The value of the SCI-HMT in facilitating clear and effective communication and shared decision-making between healthcare professionals and people with SCI was also highlighted, as shown by the remarks of an acute nurse specialist:

“I think this tool is really helpful for the consumer and the GP to work together to prioritize particular tests that a patient might need and what the regularity of that is” (healthcare professional_03).

Engaging with SCI peer support networks to promote the SCI-HMT was considered crucial, as one person with SCI emphasized when asked how the SCI-HMT might be best executed in the community, “…peers, peers and peers” (person with SCI_01). Furthermore, the layering of content made possible in the digitalized version will allow for the issue of approachability in terms of readiness for change, as another person with SCI said:

“[putting content into a digital format] is essential and required and there is a need to put summarized content in an App with links to further web-based information… it’s not likely to be accessed otherwise” (person with SCI_02).

Inner setting

Participants acknowledged that self-management of health and well-being is substantial and demanding. It was suggested that the scope, tone, and complexity of the SCI-HMT, while necessary, could potentially be resisted by people with SCI if they felt overwhelmed, as one person with SCI described:

“a manual that is really long and wordy, like, it’s [a] health metric… they maybe lack the health literacy to, to consume the content then yes, it would impede their readiness for [self-management]” (person with SCI_02).

Having support from their GPs was considered essential, and the HMT could enable GP’s, who are under time pressure, to provide more effective health and advice to their patients, as one GP said:

“We GP’s are time poor, if you realize then when you’re time poor you look quickly to say oh this is a patient tool - how can I best use this?” (healthcare professional_05).

Furthermore, health professional skills may be best used with the synthesis of self-reported symptoms, behaviors, or observations. A particular strength of a digitized version would be its ability to facilitate more streamlined communication between a person with SCI and their primary healthcare providers developing healthcare plans, as an acute nurse specialist reflected, “ I think that a digitalized version is essential with links to primary healthcare plans” (healthcare professional_03).

Efficient communication with thorough assessment is essential to ensure serious health issues are not missed, as findings reinforce that the SCI-HMT is an educational tool, not a replacement for healthcare services, as a clinical nurse specialist commented, “ remember, things will go wrong– people end up very sick and in acute care “ (healthcare professional_02).

The SCI-HMT has the potential to provide a pathway to a ‘hope for better than now’ , a hope to ‘remain well’ and a hope to ‘be happy’ , as the informant with SCI (04) declared, “self-management is a long game, if you’re keeping well, you’ve got that possibility of a good life… of happiness”. Participants with SCI felt the tool needed to be genuine and

“acknowledge the huge amount of adjustment required, recognizing that dealing with SCI issues is required to survive and live a good life” (person with SCI_04).

However, there is a risk that an individual is completely overwhelmed by the scale of the SCI-HMT content and the requirement for lifelong vigilance. Careful attention and planning were paid to layering the information accordingly to support self-management as a ‘long game’, which one person with SCI reflected in following:

“the first 2–3 year [period] is probably the toughest to get your head around the learning stuff, because you’ve got to a stage where you’re levelling out, and you’ve kind of made these promises to yourself and then you realize that there’s no quick fix” (person with SCI_01).

It was decided that this could be achieved by providing concrete examples and anecdotes from people with SCI illustrating that a meaningful, healthy life is possible, and that good health is the bedrock of a good life with SCI.

There was universal agreement that the SCI-HMT is aspirational and that it has the potential to improve knowledge and understanding for people with SCI, their families, community workers/carers and primary healthcare professionals, as a GP remarked:

“[different groups] could just read it and realize, ‘Ahh, OK that’s what that means… when you’re doing catheters. That’s what you mean when you’re talking about bladder and bowel function or skin care” (healthcare professional_04).

Despite the SCI-HMT providing an abundance of information and resources to support self-management, participants identified four gaps: (i) the priority issue of sexuality, including pleasure and identity, as one person with SCI remarked:

“ sexuality is one of the biggest issues that people with SCI often might not speak about that often cause you know it’s awkward for them. So yeah, I think that’s a that’s a serious issue” (person with SCI_03).

(ii) consideration of the taboo nature of bladder and bowel topics for indigenous people, (iii) urgent need to ensure links for SCI-HMT care plans are compatible with patient management systems, and (iv) exercise and leisure as a standalone topic taking account of effects of physical activity, including impact on mental health and wellbeing but more especially for fun.

To ensure longevity of the SCI-HMT, maintaining a partnership between people with SCI, SCI community groups and both primary and tertiary health services is required for liaison with the relevant professional bodies, care agencies, funders, policy makers and tertiary care settings to ensure ongoing education and promotion of SCI-HMT is maintained. For example, delivery of ongoing training of healthcare professionals to both increase the knowledge base of primary healthcare providers in relation to SCI, and to promote use of the tools and resources through health communities. As a community nurse specialist suggested:

“ improving knowledge in the health community… would require digital links to clinical/health management platforms” (healthcare professional_02).

In a similar vein, a GP suggested:

“ our common GP body would have continuing education requirements… especially if it’s online, in particular for the rural, rural doctors who you know, might find it hard to get into the city” (healthcare professional_04).

The successful implementation of evidence-based innovations into practice is dependent on a wide array of dynamic and active contextual factors, including the perspectives of the recipients who are destined to use such innovations. Indeed, the recently updated CFIR has called for innovation recipient perspectives to be a priority when considering contextual factors [ 10 , 11 ]. Understanding and including the perspectives of those the innovation is being designed to benefit can promote increased equity and validation of recipient populations, and potentially increase the adoption and sustainability of innovations.

In this paper, we have presented research using the recently updated CFIR to guide the collection of innovation recipients’ perspectives (including people with SCI and GPs working in the community) regarding the potential implementation barriers and facilitators of the digital version of the SCI-HMT. Collected data were synthesized to inform actionable findings– tangible ways in which the SCI-HMT could be modified according of the domains of the CFIR (e.g., see Keith et al. [ 20 ]). It is important to note that we conducted this research using the original domains of the CFIR [ 9 ] prior to Damschroder et al. publishing the updated CFIR [ 11 ]. However, in our analysis we were able to align our findings to the revised CFIR domains and constructs, as Damschroder [ 11 ] suggests, constructs can “be mapped back to the original CFIR to ensure longitudinal consistency” (p. 13).

One of the most poignant findings from our analyses was the need to ensure the content of the SCI-HMT balanced scientific evidence and clinical expertise with lived experience knowledge. This balance of clinical and experiential knowledge demonstrated genuine regard for lived experience knowledge, and created a more accessible, engaging, useable platform. For example, in the innovation and individual domains, the need to include lived experience quotes was immediately apparent once the perspective of people with SCI was included. It was highlighted that while the SCI-HMT will prove useful to many parties at various stages along the continuum of care following onset of SCI, there will be those individuals that are overwhelmed by the scale of the content. That said, the layering of information facilitated by the digitalized version is intended to provide an ease of navigation through the SCI-HMT and enable a far greater sense of control over personal health and wellbeing. Further, despite concerns regarding e-literacy the digitalized version of the SCI-HMT is seen as imperative for accessibility given the wide geographic diversity and recent COVID pandemic [ 21 ]. While there will be people who are challenged by the technology, the universally acceptable use of the internet is seen as less of a barrier than printed material.

The concept of partnership was also apparent within the data analysis focusing on the outer and inner setting domains. In the outer setting domain, our findings emphasized the importance of engaging with SCI community groups, as well as primary and tertiary care providers to maximize uptake at all points in time from the phase of subacute rehabilitation onwards. While the SCI-HMT is intended for use across the continuum of care from post-acute rehabilitation onwards, it may be that certain modules are more relevant at different times, and could serve as key resources during the hand over between acute care, inpatient rehabilitation and community reintegration.

Likewise, findings regarding the inner setting highlighted the necessity of a productive partnership between GPs and individuals with SCI to address the substantial demands of long-term self-management of health and well-being following SCI. Indeed, support is crucial, especially when self-management is the focus. This is particularly so in individuals living with complex disability following survival after illness or injury [ 22 ], where health literacy has been found to be a primary determinant of successful health and wellbeing outcomes [ 23 ]. For people with SCI, this tool potentially holds the most appeal when an individual is ready and has strong partnerships and supportive communication. This can enable potential red flags to be recognized earlier allowing timely intervention to avert health crises, promoting individual well-being, and reducing unnecessary demands on health services.

While the SCI-HMT is an educational tool and not meant to replace health services, findings suggest the current structure would lead nicely to having the conversation with a range of likely support people, including SCI peers, friends and family, GP, community nurses, carers or via on-line support services. The findings within the process domain underscored the importance of ongoing partnership between innovation implementers and a broad array of innovation recipients (e.g., individuals with SCI, healthcare professionals, family, funding agencies and policy-makers). This emphasis on partnership also addresses recent discussions regarding equity and the CFIR. For example, Damschroder et al. [ 11 ] suggests that innovation recipients are too often not included in the CFIR process, as the CFIR is primarily seen as a tool intended “to collect data from individuals who have power and/or influence over implementation outcomes” (p. 5).

Finally, we feel that our inclusion of innovation recipients’ perspectives presented in this article begins to address the notion of equity in implementation, whereby the inclusion of recipient perspectives in research using the CFIR both validates, and increases, the likelihood of sustainable adoption of evidence-based innovations, such as the SCI-HMT. We have used the CFIR in a pragmatic way with an emphasis on meaningful engagement between the innovation recipients and the research team, heeding the call from Damschroder et al. [ 11 ], who recently argued for researchers to use the CFIR to collect data from innovation recipients. Adopting this approach enabled us to give voice to innovation recipient perspectives and subsequently ensure that the tone, scope, content and presentation of the SCI-HMT balanced the needs of innovation recipients alongside the provision of evidence-based clinical information.

Our research is not without limitations. While our study was successful in identifying a number of potential barriers and facilitators to the implementation of the SCI-HMT, we did not test any implementation strategies to impact determinants, mechanisms, or outcomes. This will be the focus of future research on this project, which will investigate the impact of implementation strategies on outcomes. Focus will be given to the context-mechanism configurations which give rise to particular outcomes for different groups in certain circumstances [ 7 , 24 ]. A second potential concern is the relatively small sample size of participants that may not allow for saturation and generalizability of the findings. However, both the significant impact of secondary health complications for people with SCI and the desire for a health maintenance tool have been established in Australia [ 2 , 4 ]. The aim our study reported in this article was to achieve context-specific knowledge of a small sample that shares a particular mutual experience and represents a perspective, rather than a population [ 25 , 26 ]. We feel our findings can stimulate discussion and debate regarding participant-informed approaches to implementation of the SCI-HMT, which can then be subject to larger-sample studies to determine their generalisability, that is, their external validity. Notably, future research could examine the interaction between certain demographic differences (e.g., gender) of people with SCI and potential barriers and facilitators to the implementation of the SCI-HMT. Future research could also include the perspectives of other allied health professionals working in the community, such as occupational therapists. Lastly, while our research gave significant priority to recipient viewpoints, research in this space would benefit for ensuring innovation recipients are engaged as genuine partners throughout the entire research process from conceptualization to implementation.

Employing the CFIR provided an effective, systematic method for identifying recipient perspectives regarding the implementation of a digital health maintenance tool for people living with SCI. Findings emphasized the need to balance clinical and lived experience perspectives when designing an implementation strategy and facilitating strong partnerships with necessary stakeholders to maximise the uptake of SCI-HMT into practice. Ongoing testing will monitor the uptake and implementation of this innovation, specifically focusing on how the SCI-HMT works for different users, in different contexts, at different stages and times of the rehabilitation journey.

Data availability

The datasets supporting the conclusions of this article are available available upon request and with permission gained from the project Steering Committee.

Abbreviations

spinal cord injury

HMT-Spinal Cord Injury Health Maintenance Tool

Consolidated Framework for Implementation Research

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Acknowledgements

Authors of this study would like to thank all the consumers with SCI and healthcare professionals for their invaluable contribution to this project. Their participation and insights have been instrumental in shaping the development of the SCI-HMT. The team also acknowledges the support and guidance provided by the members of the Project Steering Committee, as well as the partner organisations, including NSW Agency for Clinical Innovation, and icare NSW. Author would also like to acknowledge the informant group with lived experience, whose perspectives have enriched our understanding and informed the development of SCI-HMT.

The SCI Wellness project was a collaborative project between John Walsh Centre for Rehabilitation Research at The University of Sydney and Royal Rehab. Both organizations provided in-kind support to the project. Additionally, the University of Sydney and Royal Rehab received research funding from Insurance and Care NSW (icare NSW) to undertake the SCI Wellness Project. icare NSW do not take direct responsibility for any of the following: study design, data collection, drafting of the manuscript, or decision to publish.

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John Walsh Centre for Rehabilitation Research, Northern Sydney Local Health District, St Leonards, NSW, Australia

John A Bourke, K. Anne Sinnott Jerram, Mohit Arora, Ashley Craig & James W Middleton

The Kolling Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia

Burwood Academy Trust, Burwood Hospital, Christchurch, New Zealand

John A Bourke

Royal Rehab, Ryde, NSW, Australia

James W Middleton

State Spinal Cord Injury Service, NSW Agency for Clinical Innovation, St Leonards, NSW, Australia

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Contributions

Project conceptualization: KASJ, MA, JWM; project methodology: JWM, MA, KASJ, JAB; data collection: KASJ and MA; data analysis: KASJ, JAB, MA, JWM; writing—original draft preparation: JAB; writing—review and editing: JAB, KASJ, JWM, MA, AC; funding acquisition: JWM, MA. All authors contributed to the revision of the paper and approved the final submitted version.

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Correspondence to John A Bourke .

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Ethics approval and consent to participate.

The first (RESP/18/212) and second phase (2019/ETH13961) of the project received ethical approval from The Northern Sydney Local Health District Human Research Ethics Committee. All participants provided informed, written consent. All data were to be retained for 7 years (23rd May 2030).

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Not applicable.

Competing interests

MA part salary (from Dec 2018 to Dec 2023), KASJ part salary (July 2021 to Dec 2023) and JAB part salary (Jan 2022 to Aug 2022) was paid from the grant monies. Other authors declare no conflicts of interest.

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Bourke, J.A., Jerram, K.A.S., Arora, M. et al. Using the consolidated Framework for Implementation Research to integrate innovation recipients’ perspectives into the implementation of a digital version of the spinal cord injury health maintenance tool: a qualitative analysis. BMC Health Serv Res 24 , 390 (2024). https://doi.org/10.1186/s12913-024-10847-x

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http://orcid.org/0000-0003-3067-9416 Jo Daniels 1 , 2 ,
http://orcid.org/0000-0002-8013-3297 Emilia Robinson 1 ,
http://orcid.org/0000-0001-5686-5132 Elizabeth Jenkinson 3 ,
http://orcid.org/0000-0002-2064-4618 Edward Carlton 4 , 5
1 Department of Psychology , University of Bath , Bath , UK
2 Psychology , North Bristol NHS Trust , Westbury on Trym , Bristol , UK
3 Department of Health and Social Sciences , University of the West of England , Bristol , UK
4 Emergency Department, Southmead Hospital , North Bristol NHS Trust , Westbury on Trym , UK
5 Bristol Medical School , University of Bristol , Bristol , UK
Correspondence to Dr Jo Daniels, Department of Psychology, University of Bath, Bath, UK; j.daniels{at}bath.ac.uk

Background Staff retention in Emergency Medicine (EM) is at crisis level and could be attributed in some part to adverse working conditions. This study aimed to better understand current concerns relating to working conditions and working practices in Emergency Departments (EDs).

Methods A qualitative approach was taken, using focus groups with ED staff (doctors, nurses, advanced care practitioners) of all grades, seniority and professional backgrounds from across the UK. Snowball recruitment was undertaken using social media and Royal College of Emergency Medicine communication channels. Focus group interviews were conducted online and organised by profession. A semi-structured topic guide was used to explore difficulties in the work environment, impact of these difficulties, barriers and priorities for change. Data were analysed using a directive content analysis to identify common themes.

Results Of the 116 clinical staff who completed the eligibility and consent forms, 46 met criteria and consented, of those, 33 participants took part. Participants were predominantly white British (85%), females (73%) and doctors (61%). Four key themes were generated: ‘culture of blame and negativity’, ‘untenable working environments’, ‘compromised leadership’ and ‘striving for support’. Data pertaining to barriers and opportunities for change were identified as sub-themes. In particular, strong leadership emerged as a key driver of change across all aspects of working practices.

Conclusion This study identified four key themes related to workplace concerns and their associated barriers and opportunities for change. Culture, working environment and need for support echoed current narratives across healthcare settings. Leadership emerged more prominently than in prior studies as both a barrier and opportunity for well-being and retention in the EM workplace. Further work is needed to develop leadership skills early on in clinical training, ensure protected time to deliver the role, ongoing opportunities to refine leadership skills and a clear pathway to address higher levels of management.

qualitative research
staff support

Data availability statement

Data are available upon reasonable request. Requests go to the corresponding author - Jo Daniels ([email protected], University of Bath, UK). De-identified participant data can be made available upon reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/emermed-2023-213189

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Retention of staff in emergency medicine is at crisis level and has been a high priority area for over a decade.

Multiple guidelines have been published to outline improvements that need to be made to retain staff; however, little improvement has been seen on the ground in EDs.

Key factors such as staff burnout and poor working conditions are known to influence intention to leave; however, it is unclear why change has not taken place despite knowledge of these problems and existing guidelines seeking to address these issues.

WHAT THIS STUDY ADDS

This qualitative study assessed perceived barriers that may be inhibiting the implementation to working conditions and working practices in EDs.

Leadership is identified as an important driver of change in working practices and can play an important role in workplace well-being and retention.

Key recommendations for avenues of improvement are made, identifying key actions at government, professional, organisational and personal level.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study identifies leadership as a key opportunity for change and as a result makes specific recommendations for policy and practice regarding leadership in emergency medicine.

Introduction

Emergency Medicine (EM) is facing a global staffing crisis. 1 Record numbers of staff continue to leave the UK NHS with EM the most affected specialty. 2 EM reports the highest work intensity of all medical specialties, 3 with ‘intensity’ recognised as one of the leading factors in job dissatisfaction, attrition and career burnout. 3–5 These factors are amplified in an already stretched workforce. 2 Psychological well-being of the EM workforce is compromised, with working conditions recognised as playing a key role. 6 7 Staff attrition has a systemic impact: lower staff ratios lead to higher workloads, reduced quality of care, 8 higher levels of medical errors 9 and poorer staff well-being, 10 all factors associated with staff absence and intention to leave. 11 The landscape of EM has also changed; increased prevalence of high patient acuity, multimorbidity and an ageing population all bear considerable impact.

Key sector stakeholder initiatives and policy recommendations relating to retention and well-being 12–14 are largely generic and forfeit relevance to the specialty due to the lack of specificity to the clinical context within which these guidelines need to be implemented. Retention improvement programmes suggest approaches should be tailored per organisation, 12 however, this assumes that the challenges faced by staff across specialities and disciplines are homogeneous. In a specialty which reports the highest pressured environment, highest attrition and rates of burnout, 15 considerations of workplace context and specificity of policy recommendations are likely to be crucial. Interventions or initiatives must take account of the unique demands of the EM working environment, and how feasible it is to implement recommendations.

The James Lind Alliance (JLA) priority setting partnership in EM 16 identified initiatives to improve staff retention as research priorities in 2017 and again in the 2022 JLA refresh, 17 signalling the need for further research in this area due to a deepening workforce crisis. Current guidelines and initiatives target working conditions which are known to be associated with retention; however, these initiatives have been poorly implemented or enforced, with few formal evaluations of such interventions. 5 Moreover, current research is limited to the perspectives of specific professional groups and most are survey-based studies. 18

In order to better address current working conditions, with a view to improving retention, this research was aimed at determining practical barriers and opportunities for change in the ED working environment as perceived by professional staff working in this environment. This will tooffer insight into the shared experiences, constraints and priorities of those working within the ED.

Enhanced understanding of these issues can provide a firm basis from which to shape, inform and underpin future policies and workplace initiatives, ensuring that practical barriers and opportunities for change are embedded in a way that optimises relevance and feasibility of implementation in the ED working environment.

Study aims and objectives

This study sought to engage three core professional groups (doctors, nurses, advanced care practitioners; ACPs) who work within an EM context to better understand (a) primary concerns relating to working conditions; (b) perceived barriers to implementing change and (c) perceived opportunities and targets for change. Findings will be used to underpin key recommendations that are tailored to the needs of an over-burdened and under-resourced ED.

This qualitative study forms part of a larger collaborative project between the University of Bath and the Royal College of Emergency Medicine (RCEM), funded by a UKRI Policy Fund. The full recommendations relating to the four core themes are available on the RCEM website (Psychologically Informed Practice and Policy (PIPP) | RCEM).

Methodology

This study uses a qualitative approach involving online focus groups in order to gain a rich and detailed understanding of participant perspectives and views, unrestricted by closed question responses. Focus groups offer the opportunity to gain an understanding of shared experiences and narratives, using a dynamic approach to the subject matter, allowing further probing for clarification and participant interaction for deeper insights. The COVID Clinicians Cohort (CoCCo) study 19 was used to organise data into key categories; this model mirrors Maslow’s Hierarchy of Needs 20 from a workplace perspective.

Participants

To be eligible for participation, ED staff must have been currently employed in a UK NHS ED as either a doctor, nurse or ACP.

ACPs are a recently developed workforce of accredited clinicians who have received advanced training to expand the scope of their usual role (eg, paramedic, nurse), permitting them to take on additional clinical responsibility in the ED.

These three groups are core affiliates of the RCEM and represent the majority of the workforce in the ED. The ED setting was used as the focus (rather than all acute care settings) as this represents the core and central setting for EM.

Recruitment and procedure

Online adverts and qualtrics survey links were distributed through social media (ie, Twitter) and RCEM communication channels using snowball recruitment methods. Profession-specific focus group interviews were conducted online using MS teams by two study researchers (JD, ER) using a semi-structured topic guide (see online supplemental materials ). The guide was shaped by the scope of study aims and the current evidence base and explored difficulties in the work environment, impact of these difficulties, barriers and priorities for change. Focus groups were 60–90 min in duration and were recorded using encrypted audio recorders, transcribed and stored securely. Participants were given debrief information sheets following the focus group. Transcripts were not returned to participants and no repeat focus groups were carried out.

Supplemental material

Directive content analysis was applied to the data. 21 This analysis strategy was used to identify common themes from participant responses, using deductive codes by identifying key concepts from existing theory 19 and prior research. Two researchers (ER, JD) read through each transcript, highlighting passages that could be categorised in the pre-determined codes. Any passages that could not be categorised within the initial coding theme were given new codes. Further coding was then conducted and this iteration was reviewed and updated. After coding was completed, initial notes from the focus groups were revisited to ensure all reflective notes were incorporated where relevant. Final themes were refined through an iterative process between JD, ER and EJ (qualitative analysis expert), with all stages of analysis reaching consensus agreement with regard to the content and labelling of codes and themes.

Patient and public involvement

As this study focused on staff experiences in an EM workplace, a Clinical Advisory Group (CAG) was used in place of patient or public involvement. The CAG comprised of five clinicians working in the ED who advised on the scope and priorities of the study. This included two medical consultants, one charge nurse, one trainee and one specialty grade doctor. Of those, three were males and two were females. All CAG members were offered renumeration for their time.

Of the 117 total responses to the study advert, 16 respondents were eligible but not available to attend focus groups and 55 either did not consent or were not eligible based on their role and/or department. From the remaining 46 respondents, 13 of these could not attend or cancelled, leaving a final sample of N=33 (28% of total responses). Due to higher response rates from doctors, these focus groups were further grouped by grade; nurses and ACPs were grouped by profession only and were organised base on availability. There were 11 groups in total (see table 1 ). Participants were mostly female, and from a white British background. Ages were spread fairly evenly across the categories, except ages 35–44 which included substantially fewer participants.

View inline

Participant and focus group characteristics

Following analysis of the qualitative data, four key themes were generated. These were termed: ‘culture of blame and negativity’, ‘untenable working environments’, ‘compromised leadership’ and ‘striving for support’. Data within these themes that were identified as ‘barriers’ or ‘opportunities’ for change were extracted ( table 2 ). Illustrative participant quotes are identified by researcher codes, which reflect the profession and a recoded group number, to preserve anonymity.

Primary concerns, barriers and opportunities for change

Culture of blame and negativity

When asked about the most difficult aspects of their working conditions, participants commonly reported a culture of blame and negativity in the ED. The work culture not only felt unsupportive and ‘toxic’ but had a marked effect on well-being. Participants described a culture which was quick to blame rather than support:

You worry about making a mistake, and if you did make a mistake who would have your back. (ACP, G7) You very rarely get anyone saying that was a good job. (SAS doctor, G8)

This was particularly felt top-down, where those in management position were perceived to take an unsympathetic view of extended waiting times and unmet targets, despite the tangible constraints of operating at overcapacity and ‘exit block’, problems that participants perceived to be out of their control. Participants in all groups indicated that the negative culture instils anxiety over how they might be perceived by peers, but particularly by senior colleagues:

That’s a classic example… she’s a senior member of the team, really knows her job…. She was quite critical really, in a very negative way about how you managed that patient. (Nurse, G11)

Some participants reported senior colleagues having unrealistic expectations of the more junior staff, with little consideration of the increased pressures that have arisen in recent years:

It’s ridiculous to compare the needs, even for our senior colleagues who were registrars five years ago, the reality of running the department overnight is not the same as it was then. (SAS doctor, G1)

Existing structures and working practices of the NHS were described as ‘archaic’ and ‘old fashioned’, leading staff to feel blamed if they could not cope with the pressures and disempowered to seek support due to the expectation that they should be ‘unbreakable’ (Trainee, G9). Participants also voiced that they were unclear on lines of accountability, who to approach for what problem. This barrier to escalating their concerns was further compounded by the belief that both clinical leadership and higher management were generally overburdened and unreceptive to discussions on workplace concerns.

Increasing pressure and longer waiting times were described as driving antisocial behaviour from patients, exposing staff to risks to physical and psychological well-being:

So the long wait causes verbal or physical violence and aggression, which has a massive impact on staff well-being. (Nurse, G11)

Participants highlighted the desire to be supported to learn from difficult experiences and develop in light of them, suggesting that a simple checking in on how individual staff members are progressing would be well received and beneficial to well-being:

We have intermittent debriefs… but it’s not every time. It doesn’t necessarily need to be every time, but it’s not as frequent as it should be. Even if it is just ask are you okay? (Trainee, G5)

Interprofessional respect and development of a more empathic culture of shared responsibility were flagged as key opportunities for change that would support better team cohesion:

We need to change how we speak and respect each group, and we need to try and understand each other’s point of view, and if we could get better ways of working, but just talking to each other about what are my problems, what are your problems, why is this stressing you, what’s stressing us, how can we work together to do that. (ACP, G2)

Findings suggest that EM professionals are confronted with outdated perceptions of clinical demand from within teams and systems, with unrealistic expectations which compound a blame and shame culture when expectations are not met. Operating within this chronically under-resourced system was framed as compromising workforce well-being and risking burnout, yet participants indicated that simple interventions such as check-ins, clearer lines of accountability and a more civil and respectful culture would offer key opportunities for growth and sustainability even in the face of a staffing crisis.

Untenable work environments

The complex work environment within the ED was described as being of significant concern, compromising care and leaving staff feeling undervalued due to basic needs being unmet. Participants frequently reported poor quality or inadequate facilities, such as provision of toilets, lockers and changing rooms, hot food only available within limited hours, poorly functioning IT systems and rest spaces being in a different building.

So you’re just basically sharing (toilets) with the patients. In the urgent care centre there’s two toilets for the whole of the department in there, often one of those is broken…and not enough lockers for every member of staff. (ACP, G2) Stuff like working computers, a consistently working POD system… those little things I think make a bigger impact on your life than how many people come in through the front door. (Trainee, G5)

A lack of physical space for administrative tasks was highlighted by many clinical staff, being described as ‘woefully inadequate’ (ACP, G2). Wards were described as ‘unfit for purpose ’ (Nurse, G11), which was attributed, in part, to higher management lacking understanding of the needs and practices of the ED. One example highlighted the long-term impact of ED workspace changes that were not fit for purpose:

…it was clear that no clinical staff had been involved. Doors were in the wrong space, no sinks in the right place, not enough storage, poor flow, poor layout (ACP, G2)

Existing rest spaces or staff rooms were reported to be taken over to provide more clinical room, limiting the space for staff to change, rest and decompress.

The nurses were getting changed in a corridor, now they seem to have a cubicle they can get changed in. But the facilities for the same trust are really very different. (Nurse, G10)

This was perceived to be particularly important due to working in the high-pressure environments of a crowded ED, where staff voiced concerns regarding the sustainability of working with a high workload safely without private spaces.

EDs were perceived to be more busy, for reasons associated with shifts in societal expectations and perceptions of the scope and role of ED:

Go back ten years ago in the emergency department and people would try their best at home, would take painkillers, will see how it goes, not wanting to trouble A&E, but seems like now it seems like A&E is the open door for everybody just to come in with everything. (ACP, G7)

Participants used emotionally laden language when describing the intensity of the workload itself, with parallels drawn between being at war and working on the NHS frontline, where staff worked under similar levels of intensity but longer term and without rest.

…when people are deployed (in the forces) they are deployed for 6 months…because that 6 months is intense, it’s intense on your body, it’s intense on your mind, it’s intense on your family, it’s intense on everything about you, and that’s while you were deployed for 6 months, and then there’s some recovery time coming back. (Consultant, G4)

Comparisons were also made to the sinking of ‘the Titanic’:

There is the jollying everybody along, being the redcoat on the shift, cheering everybody up, saying everything is going to be okay, but feeling like you’re just rearranging the deckchairs on the Titanic (Nurse, G10)

The impact of a consistently high workload was described as being compacted by a lack of agency and autonomy over working patterns, which was perceived to be related to non-clinical staff making decisions about shifts without understanding the inherent pressures:

The people who control our rotas are… her job is a rota co-ordinator, she works in an office, she is administrative, and the person who signs that off is the manager for the department, again non-clinical, and getting leave is a nightmare, it’s awful. (Trainee doctor, G5)

Consultants identified that there were limited options to reduce workload when approaching retirement, and they did not necessarily feel well-equipped to continue operating under high pressure and for long hours. Those in training posts reported insufficient time to meet requirements or study due to workload, influencing both career progression and confidence in the role.

You are getting no progression because you’re not getting your training, and I know that personally in the last year I made my decision that I will not continue to work clinically, I will step back in the next few years because there’s… why would I stay doing something that there’s no reward for? (Nurse, G11)

Participants agreed that there was both a need and an opportunity for the ED to be a ‘nicer place to work’ (ACP, G2). Specific suggestions included a full staffing quota, ensuring staff are adequately rested to return to work and the opportunity for peer support:

My top three things would be coming on with a full staffing quote so you know there’s no gaps in the rota, so you’re all there. Everyone is well rested and ready for the shift, just being able to talk to each other on the shop floor and being quite open with each other on how everyone is feeling. (ACP, G7)

Many of the suggested changes directed at making working conditions in the ED more sustainable related to basic needs such as being able to take breaks, access healthy food and functioning IT when needed:

…having those opportunities to go off and have a five minutes when you need to, to be able to continue your shift. (ACP, G7) It would be really nice to be able to have some healthy nice food in the department. (Nurse, G11) As more and more of our job goes electronic, electronic notes, electronic prescribing, actually having IT systems that are fit for purpose, everyone has access to (Trainee doctor, G9)

Self-rostering was frequently mentioned as a positive experience for participants and a useful avenue to help participants to deliver better care and improve well-being:

One day off between a set of shifts is not enough to decompress and be re-energised to start back on your next set of shifts. So I think the rota, we have moved to a more self-rostering method now, and I think that’s helping with staff well-being, especially in our team. (A7)

Overall, working in existing ED environments was described as ‘untenable’ and ‘unsustainable’ in terms of both the working environment and the lack of agency and autonomy over high-intensity workloads. Many of the problems and solutions relate to provision of resources to meet basic needs, many of which are subject to professional and NHS regulations; however, due to pressures this is not being implemented.

Compromised leadership

Clinical leads in the ED were perceived to hold responsibility for setting the tone for culture and behaviour in the ED, leading by example:

And you lead by example as well, so if your consultant in charge is not taking a break you feel like you can’t ask to take a break. It’s the same with the nurses, if the nurse in charge is not taking a break then a lot of the junior nurses won’t come and ask for a break because again you’re guided by the leadership aren’t you? (A7)

The clinical lead in the ED is a key conduit for change, from a cultural and environmental perspective especially. However, participants expressed frustration about feeling that their voices were not heard or valued outside of the department, in part due to clinical leads being reluctant to escalate their concerns due to the discrepancies between clinical priorities within the ED and the priorities expressed by trust level executive management:

You’ve got the clinical side, and we are to one degree or another worried about the patients, and then you have got the management side and they are worried about figures, times or money, and those two things don’t really mesh together (ACP, G2)

Yet, within the EDs, leadership was described as being poorly supported in terms of protected time to train and deliver the role fully. Consultants voiced reluctance to take on a leadership role due to lack of ‘visible leaders’ to provide inspiration or exemplar: ‘There is no one for us to look up to, to lead us’ (Consultant, G4), ‘We need compassionate leadership’ (SAS doctor, G1).

A lack of definition or clear understanding of what the clinical role entailed was reported to make it difficult for clinical leads to be effective in their role:

People tell you that you’re there to lead, and you’re like I know but what does that mean? And then you don’t know if you’ve got to go to all these meetings, which ones you really need to go to, which ones can I not go to, also for me I do the job on my own. (Clinical lead, G6)

Participants emphasised they need a ‘clear definition of what the college would see the role to be, and how much time they would expect it to take of your job ’ (Clinical lead, G6). Any possibility for growth was hampered by a lack of training or support from colleagues to help with even the practicalities of the role (such as recruitment and personnel management):

I have literally started last week on a leadership course that’s been for other clinical leads in the organisation. But I feel a bit could have done with this maybe earlier. But that’s more about your leadership qualities and conflict resolution, it’s all that side of it as opposed to the actual practicalities of the job. (Clinical Lead, G6)

When considering possible solutions to these difficulties, participants suggested that an accessible time to do the job and an online repository may offer an opportunity to share resources, learn from one another and foster development:

I think sharing all the stuff we shared on the WhatsApp, trying to share stuff, so how to write a business case, what you need to do. (Clinical lead, G6) I should be doing work at a time I am getting paid, so you need to give me that time. (Trainee doctor, G9)

Mentorship was also deemed to be important for successful delivery of the role:

I think personally as leads and stuff we should all have some kind of mentoring type…Supervision, that’s the thing, we don’t get any. (Nurses, G10)

Participants described having difficulties feeding into emerging issues to address unmet need, blocked from communication with leaders by ‘layers of bureaucratic sediment’. This was compounded by the career trajectory of NHS management, where often those in post would swiftly move on for promotion.

Overall, clinical leadership within the ED was described as compromised, unsupported and, ultimately, a key barrier or missed opportunity for change in culture and working practices in the ED. However, there were clear indications of opportunities for growth and change, including a need for compassionate leadership, shared resources, time to do the job and mentorship.

Striving for support

This final theme encompasses the concerns raised by participants regarding well-being and staff support, specifically the barriers to accessing well-being support and their preferences in relation to what changes are likely to improve their well-being. Common barriers included having to attend support or well-being services during time off, with the scheduling of support geared to a ‘nine to five’ non-clinical workforce (ACP, G2). Mental health stigma in the ED was also cited as a key barrier.

I think for me it still feels like a bit of a stigma about saying I am struggling what should I do next. (Nurses, G11) There’s nowhere that I can express how I am feeling or even understand how I am feeling. (Consultant, G4)

This was reinforced by well-being not being viewed as a priority, with team check-ins or formal appraisals described as having ‘nothing in there about wellbeing’ (Clinical lead, G6), despite suggestions that simple well-being check-ins would suffice.

Participants suggested that support should not be purely accessed after the fact but something that should be prioritised and routinely available to staff to safeguard mental health:

… psychological support…it shouldn’t be something that we access when there is a problem, it should be something where we go well every month on a Friday at this time I go and talk to someone about what I have seen. (Trainee, G9)

Participants’ lack of understanding about which services were being offered was raised by many, with participants often able to list services available, or where the staff support centre was based, but not how or when one might access them. This offers a key opportunity for collaboration between staff support services and the ED to develop clearer pathways or a clear role for a departmental well-being lead.

Peer support was consistently highlighted as a highly valued resource that should be considered part of supportive culture ‘gives you somebody else to share the load with, and not be that single voice’ (Trainee doctor, G9). However, limited physical space and time to engage in peer activities were cited as barriers:

Well yeah it would be lovely to sit down and chat with my peers, apart from the fact that 1) we’re constantly busy, 2) we don’t have anywhere where we can sit and have a confidential gas. (SAS doctor, G8)

Overall, accounts suggested that existing support was largely unfit for purpose, and where it was easy to access (such as peer support) and available, it was often incompatible with ED working practices and within a culture where seeking support was often stigmatised.

Some participants expressed that having a psychologist embedded within the department was highly valued as a resource, particularly the different levels of support dependent on need:

…(during the pandemic) we setup weekly drop-in sessions with the psychologist… and it was really great for a lot of people to be able to drop-in, and then that led on to having one to one for people who felt they needed that, and also within ED we had a psychologist come round to our supervision when we needed them. (ACP, G7)

Participants reflected that psychological input introduced in response to the impact of the COVID-19 pandemic was highly valued. While many were open to discussion about their mental health and well-being, for many, stigma still permeates the ED culture and is further compounded by poor understanding and communication of available resources. Appointment of well-being leads, more value placed on well-being (including informal peer support) and routine access to psychology are suggested as opportunities to make strides towards improved well-being.

This study identified four key themes describing the difficulties in the ED work place. Working culture, physical working environment, pathways to care and leadership represent the core workplace concerns within our sample. These issues were perceived to play an instrumental role in their ability to sustain good working practices, well-being and, importantly, their intention to leave. Participants identified key barriers and opportunities within their work contexts which resonate with existing research and policy and can be used to shape the future policy and research development. 22 , 2 5 These findings act as a basis for the development of specialty-specific targets for change that are aligned with the views and voices of those working in this working environment and also take account the barriers and opportunities faced in the fast-paced unique environment of the ED. For a full set of EM-specific recommendations to underpin change across all of these four areas, see the Psychologically Informed Practice and Policy (PIPP) recommendations ( https://rcem.ac.uk/workforce/psychologically-informed-practice-and-policy-pipp/ )

Several of our findings have been noted in previous studies, particularly the role of culture, environment and access to support. 22 Most of the research examining factors associated with working conditions and retention in EM are profession specific 3 6 18 19 and are not readily generalisable to other professional groups in the ED. However, our study included doctors, nurses and ACPs from which emerged common cross-cutting themes affecting all of these professions working in the ED, themes which are consistent with the broader literature 9 10 but specific to the EM working environment.

As reflected in the work by Darbyshire et al , 5 the nature of the problems described were systemic; the workplace challenges were interrelated and appeared reciprocal in influence, arguably maintaining one another. The cyclical nature itself proves a key barrier to change, which raises the question: which is the primary target to effect most change? Leadership has a pivotal influence across these themes and is unequivocally vital to workforce transformation; however, this is an area that has been largely neglected in EM, with very little research seeking to develop or evaluate leadership interventions in this environment. Indeed, there is an assumption that leadership naturally develops over time and is fully formed on appointment to the role. 23 However, leadership within the ED is particularly complex and demanding due to the range of competencies required (clinical, managerial and administrative) 23 and the high-pressured environment within which this role needs to be delivered. This warrants tailored training and support to fully succeed. In settings where the nature of the work is unpredictable and at times clinically critical, leadership is pivotal to patient outcomes and team functioning, 23 24 which are particularly crucial in the ED setting. Leadership has the potential to be a powerful driver in workforce transformation, cultural change 25 and team functioning within these highly skilled, professionally interdependent teams. 26 To fully harness the capacity of leaders as agents of change, those in leadership positions must be sufficiently skilled, 27 feel supported to act on important issues 27 and have time to do the job. Yet, participants in this study reported poor role definition, lack of training and absence of protected time to deliver the role. This was compounded by blurred lines of accountability that led to impotence to effect change.

Implications

The development of leadership in EM should now be a primary focus. There are clear steps that can be taken to begin to mobilise and maximise the pivotal influence of leadership in effecting change, across government, professional, organisational and individual levels.

On a public policy level, there has been a rapid growth of government level publications and resources to recognise the role of leadership as a conduit to better patient and team health. 28 However, recommended leadership training is often generic and never mandated. This is surprising given the clear links with patient safety and team functioning. 23 24 Leadership training in healthcare should be mandated by government bodies, not least due to links with patient safety. 29

Significant work has been undertaken by RCEM to integrate and embed mandatory leadership training into the training curriculum for EM trainees, without which they cannot progress. While this demonstrates forward thinking and some future-proofing for the medical profession, it cannot cease at this point, it must be supported with continuing professional development post-training. The relevant professional bodies provide access to good quality leadership training such as the RCEM EM Leaders Programme and the RCN Leadership Programme, however, this is largely online without protected time to access or support development. More work is needed to ensure leadership training is visible, supported as part of a workplan, and a priority area championed by all relevant professional bodies.

Further work is needed to ensure that leadership competencies are introduced at an early stage of training 23 so the necessary skills are embedded and cultivated on the pathway towards and within leadership roles, rather than ad hoc when necessity dictates. This falls to both training and professional bodies to work together to ensure that theory-driven leadership is a core part of the teaching curriculum, with mentorship and practical resources (such as role definition, a personal development plan, human resource support) to complement and facilitate the necessary continuing professional development throughout a clinical career. Responsibility then moves to the employing local NHS trusts to support the development of those individuals within leadership positions. It is at this level that ED clinical leads and their teams can harness their influence; local NHS trust policies are driven by guidance from government and professional bodies, however, they have the power to shape local policy and mandate change in view of the needs of a service. We summarise key recommendations to underpin change at a local NHS level in Box 1 .

Key leadership recommendations for local NHS trust level commissioning

Those in leadership positions should be supported to attend leadership training as part of their workplan, within their workplace hours. This would include top-up training and training assignments.

Support to engage with a leadership mentorship or coaching programme as part of their workplan, with a view to continuing professional leadership development and creating safe spaces to problem-solve, reflect and seek support.

Access to the consultation service within the local NHS staff support services.

Appointment of a designated ‘Wellbeing Lead’ with protected time and support to deliver the role.

Clear description of roles and responsibilities, to include protected time dedicated to undertaking additional responsibilities associated with a leadership role and a professional development plan that is reviewed annually.

Support to engage with the EM clinical lead network in order to access resources to support the delivery of the role and access peer support when necessary.

Clear lines of accountability at an NHS organisational level with identified pathways to escalate concerns.

EM, emergency medicine.

Appointment of well-being leads within the ED, as outlined in the RCEM PIPP recommendations 30 and other key documents, 22 is also a key step towards workplace transformation through leadership; however, it is imperative this role is also supported with protected time and development. A well-being lead with a clearly defined remit and role would play a pivotal gatekeeper role in encouraging attitudes towards well-being in the ED by delivering ‘warm handovers’ and well-being initiatives, such as informal check-ins, staff team activities (ie, safety huddles), and well-being surveys.

On an individual level, those in leadership positions are more likely to succeed by harnessing the influence and opportunity that accompanies the role, identifying and taking inventory of challenges and barriers, clarifying lines of accountability to drive forward change and advocating for the needs of their team. Two mechanisms by which leadership bears the greatest influence include leading and prioritising a continuous cycle of quality improvement (eg, autonomy over work patterns, access to rest spaces, patient flow, taking steps to address the diversity gap) and role modelling of positive professional behaviours. 26 The latter includes compassionate and inclusive attributes but also speaks to the necessity to meet basic needs: taking breaks, adhering to annual leave, destigmatising views on mental health and openness to learning and change. Those in leadership roles should be encouraged to engage with the leadership networks, broadened to encompass a platform or virtual environment (ie, repository) to share and access resources and be granted access to leadership consultation with the well-being team as and when necessary. Those in leadership positions should also be provided with clear referral processes and internal professional standards to help address any incivility, including bullying, harassment and issues of inclusion. This would help promote a culture of care and interprofessional valuing and respect, improving team cohesion.

Finally, it is imperative that lines of accountability are clear for those in a leadership position. While many NHS trusts differ in their management structures, each trust will have communication pathways to divisional and executive management leadership teams. In order to drive the full potential of leaders to action change through these mechanisms, it is fundamental that pathways from ‘shop floor’ to the chief executive are clear and opinions and concerns of ED leadership are welcomed.

Flow through the ED, staff ratios, pay and pension structures are of course prime targets for change and where the current high-profile focus lies. However, leadership is a key conduit to change and those with mandatory powers must now move to recognise this in order to unlock the full potential of this role.

Limitations and future directions

There are inherent limitations in the small size of some of the participant groups, and as such the views and opinions expressed cannot be considered transferable across their respective professions. While many prospective participants did not proceed to focus group meetings due to last minute requests to cover shifts, the participant pool was comfortably within the bounds of what is acceptable for a qualitative study.

Findings should be interpreted in light of the sample consisting mainly of white women, therefore the views of males and minority groups may not be fully represented. Doctors made up a higher proportion of the final sample; this may be a consequence of using RCEM communication channels as a primary recruitment method, which has more members registered as doctors than nurses. As not all professions working in ED were included (eg, physiotherapy, psychology) it is possible that additional themes or differences might have been missed.

The geographical spread reflects a broad reach; however, there was a preponderance towards the South West, where the research was conducted. While none of the interviewees were known to the research team, those in the South West may have been more exposed to recruitment drives through mutual connections.

The development and testing of leadership training and packages should be a priority for professional bodies and at organisational level. This should take account of the overlapping and competing competencies required of ED leadership, including managerial, administrative and clinical components and the high-pressured context within which these skills are required.

This study identified key themes in understanding workplace concerns in the ED, and their associated barriers and opportunities for change. Leadership in EM should now be a primary focus, with further investment and support to target the development of leadership skills early on in training and provide protected time to refine these leadership skills and qualities across the working lifetime. This will serve to harness the pivotal influence of leadership in EM, which, if properly supported, holds the potential to act as a conduit for change across all areas of focus.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by University of Bath Psychology Research Ethics Committee (22-039). The Health Research Authority toolkit confirmed further approval was not required. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The study authors would like to extend thanks to all who contributed to this project including participants and the clinical advisory group. The authors would also like to acknowledge and thank RCEM President (AB) and policy advisor (SMcI) who advised on the policy priorities of RCEM and wellbeing clinical leads (Dr Jo Poitier, Consultant Clinical Psychologist at Alder Hey Children's NHS Foundation Trust; Dr Olivia Donnelly, Consultant Clinical Psychologist at North Bristol NHS Trust) who were consulted on their respective areas of expertise. They also thank Rita De Nicola for help in preparing the manuscript.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Data supplement 1
Data supplement 2
Data supplement 3

Handling editor Caroline Leech

Twitter @drjodaniels

Contributors The original concept for the paper was developed by JD and shaped in consultation with EC and the RCEM President AB. JD was the primary contributor, guarantor and lead for the content and refinement of the paper. EJ gave expert methodological advice and contributed to the reporting and refinement of results. ER and JD performed the analysis, both contributing to the reporting of the results. ER prepared the manuscript for publication. EC gave expert advice on all aspects of the study from an Emergency Medicine standpoint and also contributed to the write-up of the paper. All authors contributed to the final version of the paper and approved for publication.

Funding This research has been carried out through funding from the UK Research and Innovation Policy (UKRI) Support Fund. The funder did not provide a grant number for this project, it is part of block 'UKRI Policy Support' funding from UKRI directly to Universities who distribute within their institutions. The funders had no role in considering the study design or in the collection, analysis or interpretation of data; the writing of the report or the decision to submit the article for publication.

Competing interests None declared.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Afr J Emerg Med
v.7(3); 2017 Sep

A hands-on guide to doing content analysis

Christen erlingsson.

a Department of Health and Caring Sciences, Linnaeus University, Kalmar 391 82, Sweden

Petra Brysiewicz

b School of Nursing & Public Health, University of KwaZulu-Natal, Durban 4041, South Africa

Associated Data

There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including the emergency care context in Africa. Novice qualitative researchers are often daunted by the prospect of qualitative data analysis and thus may experience much difficulty in the data analysis process. Our objective with this manuscript is to provide a practical hands-on example of qualitative content analysis to aid novice qualitative researchers in their task.

African relevance

• Qualitative research is useful to deepen the understanding of the human experience.
• Novice qualitative researchers may benefit from this hands-on guide to content analysis.
• Practical tips and data analysis templates are provided to assist in the analysis process.

Introduction

There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including emergency care research. An increasing number of health researchers are currently opting to use various qualitative research approaches in exploring and describing complex phenomena, providing textual accounts of individuals’ “life worlds”, and giving voice to vulnerable populations our patients so often represent. Many articles and books are available that describe qualitative research methods and provide overviews of content analysis procedures [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] . Some articles include step-by-step directions intended to clarify content analysis methodology. What we have found in our teaching experience is that these directions are indeed very useful. However, qualitative researchers, especially novice researchers, often struggle to understand what is happening on and between steps, i.e., how the steps are taken.

As research supervisors of postgraduate health professionals, we often meet students who present brilliant ideas for qualitative studies that have potential to fill current gaps in the literature. Typically, the suggested studies aim to explore human experience. Research questions exploring human experience are expediently studied through analysing textual data e.g., collected in individual interviews, focus groups, documents, or documented participant observation. When reflecting on the proposed study aim together with the student, we often suggest content analysis methodology as the best fit for the study and the student, especially the novice researcher. The interview data are collected and the content analysis adventure begins. Students soon realise that data based on human experiences are complex, multifaceted and often carry meaning on multiple levels.

For many novice researchers, analysing qualitative data is found to be unexpectedly challenging and time-consuming. As they soon discover, there is no step-wise analysis process that can be applied to the data like a pattern cutter at a textile factory. They may become extremely annoyed and frustrated during the hands-on enterprise of qualitative content analysis.

The novice researcher may lament, “I’ve read all the methodology but don’t really know how to start and exactly what to do with my data!” They grapple with qualitative research terms and concepts, for example; differences between meaning units, codes, categories and themes, and regarding increasing levels of abstraction from raw data to categories or themes. The content analysis adventure may now seem to be a chaotic undertaking. But, life is messy, complex and utterly fascinating. Experiencing chaos during analysis is normal. Good advice for the qualitative researcher is to be open to the complexity in the data and utilise one’s flow of creativity.

Inspired primarily by descriptions of “conventional content analysis” in Hsieh and Shannon [3] , “inductive content analysis” in Elo and Kyngäs [5] and “qualitative content analysis of an interview text” in Graneheim and Lundman [1] , we have written this paper to help the novice qualitative researcher navigate the uncertainty in-between the steps of qualitative content analysis. We will provide advice and practical tips, as well as data analysis templates, to attempt to ease frustration and hopefully, inspire readers to discover how this exciting methodology contributes to developing a deeper understanding of human experience and our professional contexts.

Overview of qualitative content analysis

Synopsis of content analysis.

A common starting point for qualitative content analysis is often transcribed interview texts. The objective in qualitative content analysis is to systematically transform a large amount of text into a highly organised and concise summary of key results. Analysis of the raw data from verbatim transcribed interviews to form categories or themes is a process of further abstraction of data at each step of the analysis; from the manifest and literal content to latent meanings ( Fig. 1 and Table 1 ).

An external file that holds a picture, illustration, etc.
Object name is gr1.jpg

Example of analysis leading to higher levels of abstraction; from manifest to latent content.

Glossary of terms as used in this hands-on guide to doing content analysis. *

The initial step is to read and re-read the interviews to get a sense of the whole, i.e., to gain a general understanding of what your participants are talking about. At this point you may already start to get ideas of what the main points or ideas are that your participants are expressing. Then one needs to start dividing up the text into smaller parts, namely, into meaning units. One then condenses these meaning units further. While doing this, you need to ensure that the core meaning is still retained. The next step is to label condensed meaning units by formulating codes and then grouping these codes into categories. Depending on the study’s aim and quality of the collected data, one may choose categories as the highest level of abstraction for reporting results or you can go further and create themes [1] , [2] , [3] , [5] , [8] .

Content analysis as a reflective process

You must mould the clay of the data , tapping into your intuition while maintaining a reflective understanding of how your own previous knowledge is influencing your analysis, i.e., your pre-understanding. In qualitative methodology, it is imperative to vigilantly maintain an awareness of one’s pre-understanding so that this does not influence analysis and/or results. This is the difficult balancing task of keeping a firm grip on one’s assumptions, opinions, and personal beliefs, and not letting them unconsciously steer your analysis process while simultaneously, and knowingly, utilising one’s pre-understanding to facilitate a deeper understanding of the data.

Content analysis, as in all qualitative analysis, is a reflective process. There is no “step 1, 2, 3, done!” linear progression in the analysis. This means that identifying and condensing meaning units, coding, and categorising are not one-time events. It is a continuous process of coding and categorising then returning to the raw data to reflect on your initial analysis. Are you still satisfied with the length of meaning units? Do the condensed meaning units and codes still “fit” with each other? Do the codes still fit into this particular category? Typically, a fair amount of adjusting is needed after the first analysis endeavour. For example: a meaning unit might need to be split into two meaning units in order to capture an additional core meaning; a code modified to more closely match the core meaning of the condensed meaning unit; or a category name tweaked to most accurately describe the included codes. In other words, analysis is a flexible reflective process of working and re-working your data that reveals connections and relationships. Once condensed meaning units are coded it is easier to get a bigger picture and see patterns in your codes and organise codes in categories.

Content analysis exercise

The synopsis above is representative of analysis descriptions in many content analysis articles. Although correct, such method descriptions still do not provide much support for the novice researcher during the actual analysis process. Aspiring to provide guidance and direction to support the novice, a practical example of doing the actual work of content analysis is provided in the following sections. This practical example is based on a transcribed interview excerpt that was part of a study that aimed to explore patients’ experiences of being admitted into the emergency centre ( Fig. 2 ).

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Excerpt from interview text exploring “Patient’s experience of being admitted into the emergency centre”

This content analysis exercise provides instructions, tips, and advice to support the content analysis novice in a) familiarising oneself with the data and the hermeneutic spiral, b) dividing up the text into meaning units and subsequently condensing these meaning units, c) formulating codes, and d) developing categories and themes.

Familiarising oneself with the data and the hermeneutic spiral

An important initial phase in the data analysis process is to read and re-read the transcribed interview while keeping your aim in focus. Write down your initial impressions. Embrace your intuition. What is the text talking about? What stands out? How did you react while reading the text? What message did the text leave you with? In this analysis phase, you are gaining a sense of the text as a whole.

You may ask why this is important. During analysis, you will be breaking down the whole text into smaller parts. Returning to your notes with your initial impressions will help you see if your “parts” analysis is matching up with your first impressions of the “whole” text. Are your initial impressions visible in your analysis of the parts? Perhaps you need to go back and check for different perspectives. This is what is referred to as the hermeneutic spiral or hermeneutic circle. It is the process of comparing the parts to the whole to determine whether impressions of the whole verify the analysis of the parts in all phases of analysis. Each part should reflect the whole and the whole should be reflected in each part. This concept will become clearer as you start working with your data.

Dividing up the text into meaning units and condensing meaning units

You have now read the interview a number of times. Keeping your research aim and question clearly in focus, divide up the text into meaning units. Located meaning units are then condensed further while keeping the central meaning intact ( Table 2 ). The condensation should be a shortened version of the same text that still conveys the essential message of the meaning unit. Sometimes the meaning unit is already so compact that no further condensation is required. Some content analysis sources warn researchers against short meaning units, claiming that this can lead to fragmentation [1] . However, our personal experience as research supervisors has shown us that a greater problem for the novice is basing analysis on meaning units that are too large and include many meanings which are then lost in the condensation process.

Suggestion for how the exemplar interview text can be divided into meaning units and condensed meaning units ( condensations are in parentheses ).

Formulating codes

The next step is to develop codes that are descriptive labels for the condensed meaning units ( Table 3 ). Codes concisely describe the condensed meaning unit and are tools to help researchers reflect on the data in new ways. Codes make it easier to identify connections between meaning units. At this stage of analysis you are still keeping very close to your data with very limited interpretation of content. You may adjust, re-do, re-think, and re-code until you get to the point where you are satisfied that your choices are reasonable. Just as in the initial phase of getting to know your data as a whole, it is also good to write notes during coding on your impressions and reactions to the text.

Suggestions for coding of condensed meaning units.

Developing categories and themes

The next step is to sort codes into categories that answer the questions who , what , when or where? One does this by comparing codes and appraising them to determine which codes seem to belong together, thereby forming a category. In other words, a category consists of codes that appear to deal with the same issue, i.e., manifest content visible in the data with limited interpretation on the part of the researcher. Category names are most often short and factual sounding.

In data that is rich with latent meaning, analysis can be carried on to create themes. In our practical example, we have continued the process of abstracting data to a higher level, from category to theme level, and developed three themes as well as an overarching theme ( Table 4 ). Themes express underlying meaning, i.e., latent content, and are formed by grouping two or more categories together. Themes are answering questions such as why , how , in what way or by what means? Therefore, theme names include verbs, adverbs and adjectives and are very descriptive or even poetic.

Suggestion for organisation of coded meaning units into categories and themes.

Some reflections and helpful tips

Understand your pre-understandings.

While conducting qualitative research, it is paramount that the researcher maintains a vigilance of non-bias during analysis. In other words, did you remain aware of your pre-understandings, i.e., your own personal assumptions, professional background, and previous experiences and knowledge? For example, did you zero in on particular aspects of the interview on account of your profession (as an emergency doctor, emergency nurse, pre-hospital professional, etc.)? Did you assume the patient’s gender? Did your assumptions affect your analysis? How about aspects of culpability; did you assume that this patient was at fault or that this patient was a victim in the crash? Did this affect how you analysed the text?

Staying aware of one’s pre-understandings is exactly as difficult as it sounds. But, it is possible and it is requisite. Focus on putting yourself and your pre-understandings in a holding pattern while you approach your data with an openness and expectation of finding new perspectives. That is the key: expect the new and be prepared to be surprised. If something in your data feels unusual, is different from what you know, atypical, or even odd – don’t by-pass it as “wrong”. Your reactions and intuitive responses are letting you know that here is something to pay extra attention to, besides the more comfortable condensing and coding of more easily recognisable meaning units.

Use your intuition

Intuition is a great asset in qualitative analysis and not to be dismissed as “unscientific”. Intuition results from tacit knowledge. Just as tacit knowledge is a hallmark of great clinicians [11] , [12] ; it is also an invaluable tool in analysis work [13] . Literally, take note of your gut reactions and intuitive guidance and remember to write these down! These notes often form a framework of possible avenues for further analysis and are especially helpful as you lift the analysis to higher levels of abstraction; from meaning units to condensed meaning units, to codes, to categories and then to the highest level of abstraction in content analysis, themes.

Aspects of coding and categorising hard to place data

All too often, the novice gets overwhelmed by interview material that deals with the general subject matter of the interview, but doesn’t seem to answer the research question. Don’t be too quick to consider such text as off topic or dross [6] . There is often data that, although not seeming to match the study aim precisely, is still important for illuminating the problem area. This can be seen in our practical example about exploring patients’ experiences of being admitted into the emergency centre. Initially the participant is describing the accident itself. While not directly answering the research question, the description is important for understanding the context of the experience of being admitted into the emergency centre. It is very common that participants will “begin at the beginning” and prologue their narratives in order to create a context that sets the scene. This type of contextual data is vital for gaining a deepened understanding of participants’ experiences.

In our practical example, the participant begins by describing the crash and the rescue, i.e., experiences leading up to and prior to admission to the emergency centre. That is why we have chosen in our analysis to code the condensed meaning unit “Ambulance staff looked worried about all the blood” as “In the ambulance” and place it in the category “Reliving the rescue”. We did not choose to include this meaning unit in the categories specifically about admission to the emergency centre itself. Do you agree with our coding choice? Would you have chosen differently?

Another common problem for the novice is deciding how to code condensed meaning units when the unit can be labelled in several different ways. At this point researchers usually groan and wish they had thought to ask one of those classic follow-up questions like “Can you tell me a little bit more about that?” We have examples of two such coding conundrums in the exemplar, as can be seen in Table 3 (codes we conferred on) and Table 4 (codes we reached consensus on). Do you agree with our choices or would you have chosen different codes? Our best advice is to go back to your impressions of the whole and lean into your intuition when choosing codes that are most reasonable and best fit your data.

A typical problem area during categorisation, especially for the novice researcher, is overlap between content in more than one initial category, i.e., codes included in one category also seem to be a fit for another category. Overlap between initial categories is very likely an indication that the jump from code to category was too big, a problem not uncommon when the data is voluminous and/or very complex. In such cases, it can be helpful to first sort codes into narrower categories, so-called subcategories. Subcategories can then be reviewed for possibilities of further aggregation into categories. In the case of a problematic coding, it is advantageous to return to the meaning unit and check if the meaning unit itself fits the category or if you need to reconsider your preliminary coding.

It is not uncommon to be faced by thorny problems such as these during coding and categorisation. Here we would like to reiterate how valuable it is to have fellow researchers with whom you can discuss and reflect together with, in order to reach consensus on the best way forward in your data analysis. It is really advantageous to compare your analysis with meaning units, condensations, coding and categorisations done by another researcher on the same text. Have you identified the same meaning units? Do you agree on coding? See similar patterns in the data? Concur on categories? Sometimes referred to as “researcher triangulation,” this is actually a key element in qualitative analysis and an important component when striving to ensure trustworthiness in your study [14] . Qualitative research is about seeking out variations and not controlling variables, as in quantitative research. Collaborating with others during analysis lets you tap into multiple perspectives and often makes it easier to see variations in the data, thereby enhancing the quality of your results as well as contributing to the rigor of your study. It is important to note that it is not necessary to force consensus in the findings but one can embrace these variations in interpretation and use that to capture the richness in the data.

Yet there are times when neither openness, pre-understanding, intuition, nor researcher triangulation does the job; for example, when analysing an interview and one is simply confused on how to code certain meaning units. At such times, there are a variety of options. A good starting place is to re-read all the interviews through the lens of this specific issue and actively search for other similar types of meaning units you might have missed. Another way to handle this is to conduct further interviews with specific queries that hopefully shed light on the issue. A third option is to have a follow-up interview with the same person and ask them to explain.

Additional tips

It is important to remember that in a typical project there are several interviews to analyse. Codes found in a single interview serve as a starting point as you then work through the remaining interviews coding all material. Form your categories and themes when all project interviews have been coded.

When submitting an article with your study results, it is a good idea to create a table or figure providing a few key examples of how you progressed from the raw data of meaning units, to condensed meaning units, coding, categorisation, and, if included, themes. Providing such a table or figure supports the rigor of your study [1] and is an element greatly appreciated by reviewers and research consumers.

During the analysis process, it can be advantageous to write down your research aim and questions on a sheet of paper that you keep nearby as you work. Frequently referring to your aim can help you keep focused and on track during analysis. Many find it helpful to colour code their transcriptions and write notes in the margins.

Having access to qualitative analysis software can be greatly helpful in organising and retrieving analysed data. Just remember, a computer does not analyse the data. As Jennings [15] has stated, “… it is ‘peopleware,’ not software, that analyses.” A major drawback is that qualitative analysis software can be prohibitively expensive. One way forward is to use table templates such as we have used in this article. (Three analysis templates, Templates A, B, and C, are provided as supplementary online material ). Additionally, the “find” function in word processing programmes such as Microsoft Word (Redmond, WA USA) facilitates locating key words, e.g., in transcribed interviews, meaning units, and codes.

Lessons learnt/key points

From our experience with content analysis we have learnt a number of important lessons that may be useful for the novice researcher. They are:

• A method description is a guideline supporting analysis and trustworthiness. Don’t get caught up too rigidly following steps. Reflexivity and flexibility are just as important. Remember that a method description is a tool helping you in the process of making sense of your data by reducing a large amount of text to distil key results.
• It is important to maintain a vigilant awareness of one’s own pre-understandings in order to avoid bias during analysis and in results.
• Use and trust your own intuition during the analysis process.
• If possible, discuss and reflect together with other researchers who have analysed the same data. Be open and receptive to new perspectives.
• Understand that it is going to take time. Even if you are quite experienced, each set of data is different and all require time to analyse. Don’t expect to have all the data analysis done over a weekend. It may take weeks. You need time to think, reflect and then review your analysis.
• Keep reminding yourself how excited you have felt about this area of research and how interesting it is. Embrace it with enthusiasm!
• Let it be chaotic – have faith that some sense will start to surface. Don’t be afraid and think you will never get to the end – you will… eventually!

Peer review under responsibility of African Federation for Emergency Medicine.

Appendix A Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.afjem.2017.08.001 .

Appendix A. Supplementary data

This paper is in the following e-collection/theme issue:

Published on 25.3.2024 in Vol 8 (2024)

Community Members’ Perceptions of a Resource-Rich Well-Being Website in California During the COVID-19 Pandemic: Qualitative Thematic Analysis

Authors of this article:

Original Paper

MarySue V Heilemann 1 , PhD ;
Jianchao Lai 2 , MSW, PhD ;
Madonna P Cadiz 2 , MSW ;
Jocelyn I Meza 3 , PhD ;
Daniela Flores Romero 4 , BA ;
Kenneth B Wells 4 , MPH, MD

1 School of Nursing, University of California, Los Angeles, Los Angeles, CA, United States

2 Department of Social Welfare, Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States

3 Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States

4 Research Center for Health Services and Society, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States

Corresponding Author:

MarySue V Heilemann, PhD

School of Nursing

University of California, Los Angeles

700 Tiverton Avenue

Los Angeles, CA, 90095-6919

United States

Phone: 1 310 206 4735

Fax:1 310 206 3241

Email: [email protected]

Background: To address needs for emotional well-being resources for Californians during the COVID-19 pandemic, the Together for Wellness/Juntos por Nuestro Bienestar (T4W/Juntos) website was developed in collaboration with multiple community partners across California, funded by the California Department of Health Care Services Behavioral Health Division federal emergency response.

Objective: This qualitative study was designed to explore and describe the perspectives of participants affiliated with California organizations on the T4W/Juntos website, understand their needs for web-based emotional health resources, and inform iterative website development.

Methods: After providing informed consent and reviewing the website, telephone interviews were conducted with 29 participants (n=21, 72% in English and n=8, 28% in Spanish) recruited by partnering community agencies (October 2021-February 2022). A 6-phase thematic analysis was conducted, enhanced using grounded theory techniques. The investigators wrote reflexive memos and performed line-by-line coding of 12 transcripts. Comparative analyses led to the identification of 15 overarching codes. The ATLAS.ti Web software (ATLAS.ti Scientific Software Development GmbH) was used to mark all 29 transcripts using these codes. After examining the data grouped by codes, comparative analyses led to the identification of main themes, each with a central organizing concept.

Results: Four main themes were identified: (1) having to change my coping due to the pandemic, (2) confronting a context of shifting perceptions of mental health stigma among diverse groups, (3) “Feels like home”—experiencing a sense of inclusivity and belonging in T4W/Juntos, and (4) “It’s a one-stop-shop”—judging T4W/Juntos to be a desirable and useful website. Overall, the T4W/Juntos website communicated support and community to this sample during the pandemic. Participants shared suggestions for website improvement, including adding a back button and a drop-down menu to improve functionality as well as resources tailored to the needs of groups such as older adults; adolescents; the lesbian, gay, bisexual, transgender, and queer community; police officers; and veterans.

Conclusions: The qualitative findings from telephone interviews with this sample of community members and service providers in California suggest that, during the COVID-19 pandemic, the T4W/Juntos website was well received as a useful, accessible tool, with some concerns noted such as language sometimes being too “professional” or “clinical.” The look, feel, and content of the website were described as welcoming due to pictures, animations, and videos that showcased resources in a personal, colorful, and inviting way. Furthermore, the content was perceived as lacking the stigma typically attached to mental health, reflecting the commitment of the T4W/Juntos team. Unique features and diverse resources, including multiple languages, made the T4W/Juntos website a valuable resource, potentially informing dissemination. Future efforts to develop mental health websites should consider engaging a diverse sample of potential users to understand how to tailor messages to specific communities and help reduce stigma.

Introduction

The COVID-19 pandemic brought unexpected difficulties related to activities of daily living for people worldwide. In addition to affecting physical health, COVID-19 also threatened emotional health. Diverse groups were impacted, including people of different ages [ 1 - 4 ], gender identities [ 5 - 9 ], races and ethnicities [ 10 , 11 ], and geographic locations [ 12 - 15 ]. Those who faced financial concerns [ 16 , 17 ] or lost their jobs [ 8 , 18 ] and those caring for children at home [ 5 ] struggled with a variety of additional pressures. For workers [ 19 , 20 ], including health care workers [ 21 , 22 ] and community-based service providers [ 23 - 26 ] in the United States, attempts to serve clients or patients were confounded by pandemic-related challenges such as concerns about infection, reductions in staffing, and transitions to remote care, making their jobs even more complex and challenging.

As stay-at-home orders proliferated, people began to look for ways to strengthen their ability to cope emotionally during the pandemic. Many turned to media outlets such as television, radio, and social media for news and information [ 27 ]. Unfortunately, the news often led to increased fear and worries about the COVID-19 virus, illness, death, loss of jobs, economic concerns, and more. Many used Twitter to read the views of others and express their own negative sentiments about the pandemic [ 28 ]. Social media use contributed to experiences of stress [ 27 ], whereas engagement in self-care activities such as being able to access and use personal support resources helped protect against mental health distress [ 29 ]. Consequently, researchers [ 27 , 30 ] called for creative developments to connect individuals with social support and mental health services. To overcome stigma and other barriers, researchers and developers turned to web-based digital tools to make resources for coping and information on emotional well-being more accessible.

In this context, the Together for Wellness/Juntos por Nuestro Bienestar (T4W/Juntos) website was developed in collaboration with multiple community partners across California, funded by the California Department of Health Care Services Behavioral Health Division federal emergency response, to directly address needs for free web-based emotional well-being resources for Californians during the pandemic. The purpose of this paper is to report the findings of a qualitative study on the perspectives of a sample of participants affiliated with California organizations who engaged with the T4W/Juntos website.

Development of the T4W/Juntos Website

The T4W/Juntos website was developed as part of the Federal Emergency Management Agency and Substance Abuse and Mental Health Services Administration crisis counseling contract with California. The goals for T4W/Juntos were developed with a multidisciplinary team of researchers, clinicians, digital resource development experts, and staff from community-based agencies in California. Goals centered on creating inclusive and accessible resources that would provide evidence-informed and evidence-based information to Californians to ease the stress experienced during the pandemic [ 31 ].

Meetings were held via Zoom (Zoom Video Communications) with the large collaborative team (4 to 18 members per meeting) to maximize input from community members. The community and study team members made decisions collaboratively about which types of resources to include on the website. The priority was to feature resources that facilitated learning about COVID-19 or offered ways to address anxiety and stress (eg, web-based meditations, breathing exercises, and direct links to warmlines and hotlines), strengthen resilience, cope with grief due to a recent loss, connect with other people (such as through web-based support groups), or support social justice (eg, antiracism and reducing hate crime). Resources included links to web-based toolkits, websites, videos, web-based applications, articles, and downloadable pamphlets. Some resources were available in multiple (up to 10) languages. Community partners’ emphasis on using neutral, nonclinical language to increase comprehension and relatability and reduce stigma led to a monitoring of the length and complexity of messaging for the website. In response to the community partner prioritization of videos to engage users, the team created videos of community members speaking about the website’s purpose and features in English and Spanish [ 31 ].

A previous paper related to T4W/Juntos described the process of website development [ 31 ]. A second paper described the results of an analysis of quantitative data from an electronically administered web-based survey that were collected at 2 time points (approximately 6 wk apart) from English- and Spanish-speaking adult participants. Of the 366 eligible participants, 315 (86.1%) completed the baseline survey and 193 (61.3%) completed the follow-up survey, with baseline results showing substantial diversity in gender, gender identity, and race and ethnicity and 32.7% (103/315) having moderate depression or anxiety (2-item Patient Health Questionnaire or 2-item Generalized Anxiety Disorder score of ≥3) [ 32 , 33 ]. Significant predictors of baseline website engagement were Hispanic versus other race or ethnicity and COVID-19–related behavior changes. The use of the T4W/Juntos website during the month before the follow-up survey was significantly associated with a pretest-posttest reduction in depression (2-item Patient Health Questionnaire score), and greater website engagement at baseline predicted reduced hotline use before follow-up [ 34 ]. An analysis of short qualitative answers that 199 (63.2%) out of 315 participants typed into textboxes in response to open-ended questions in the previously described web-based survey led to insights into safety concerns and fears during the pandemic and perceived benefits from and suggestions for improving the website [ 35 ].

Research Aims

With the goal of supplementing the quantitative results, the aim of this qualitative study was to describe the perspectives of a diverse subset of participants associated with various California community organizations who completed the baseline surveys regarding their experiences with the T4W/Juntos website. We also focused on participants’ needs for website resources that could support emotional well-being for themselves, their families, their clients, or their community. Finally, we sought insight to inform iterative website development in the future.

Recruitment

The larger sample described previously was recruited during the pandemic through invitations that were sent primarily by email, while stay-at-home orders were in effect, from 11 community partner agencies throughout the state of California to their affiliated community members with information about the website, its purpose, and the research study. Each of the 315 participants who consented and completed the baseline survey was given the option to indicate their interest in participating in a potential future telephone interview by clicking a box at the end of the survey. In total, 73.9% (233/315) of the participants clicked on the box to indicate their interest in being interviewed. Inclusion criteria were being aged ≥18 years, having access to the internet, having already completed the baseline web-based survey in English or Spanish, and agreeing to provide contact information. Using convenience sampling, participants who spoke English or Spanish were contacted via telephone by research staff approximately 2 weeks after completion of the baseline survey, starting with those who were the first to finish the survey, to offer an interview, confirm availability, and set a date and time for the interview. After 15 interviews were conducted, purposive sampling was used to maximize diversity in race and ethnicity, gender, and age. The final sample comprised 29 participants. The interviews (in English or Spanish) were conducted between October 2021 and February 2022.

Ethical Considerations

This study was reviewed and approved by the University of California, Los Angeles, Institutional Review Board of UCLA’s Human Research Protection Program (20-002163-AM-00008). After reading the web-based consent document, each participant clicked to give consent at the time of enrollment in the larger survey study, which included consent to a future potential interview. Our team only contacted individuals who agreed to be contacted for interviews using the contact information they provided. Participants reconfirmed their approval to participate and be audio recorded at the time of the interview. To protect the privacy and confidentiality of participants, the list of the names of the participants and their assigned codes was kept in a password-protected file available only to the principal investigator and project director. Their confidential contact and personal information were kept separate from all other data. Any potentially identifying information was deidentified on the transcriptions of audio-recorded interviews, including any names or descriptors that could possibly identify a participant; all names were changed to code numbers that were used instead of names by the researchers during data analysis. Participants received a US $25 e-gift card after completing the interview.

Data Collection

Demographic data were retrieved from the baseline survey for each of the 29 interview participants. A semistructured interview guide in English and Spanish that was developed by a multidisciplinary team was subsequently used by 2 research team members to conduct all interviews via telephone. Interview questions were designed to explore participants’ perceptions of any aspect of the T4W/Juntos website; gain insight into participants’ needs for support in relation to the resources available via the website for themselves and their families, clients, or communities; and obtain guidance on further development of the website. Audio recordings of interviews in Spanish were professionally translated into English, and all interviews were professionally transcribed verbatim and checked for accuracy. As already noted, identifiers were removed, and code numbers were used instead of names to label transcripts and organize the data.

Data Analysis

Demographic data were analyzed for frequencies using Stata/MP (version 17; StataCorp LLC) [ 36 ] for the sample of 29 participants. For the thematic analysis of the 29 transcripts, the study team was guided by a modification of the 6-phase process outlined by Braun and Clarke [ 37 , 38 ]. First, the study team familiarized themselves with the data in all transcripts. Second, the team engaged in initial coding using techniques from grounded theory methodology to enrich our approach [ 39 ]. Thus, most codes were developed using the gerund form of verbs, known as process codes, to heighten our focus on the actions taken by participants, as shown in the data [ 39 , 40 ]. To create process codes, coders used heuristic questions to ask What is happening here? and What are they doing here? This allowed coders to get closer to the participants’ point of view while reducing the tendency to prematurely project their own interpretations onto the data [ 39 ]. In the third and fourth phases, coders scrutinized the first 12 coded transcripts to identify the most frequently occurring and significant codes and, through discussion and debate, identified a total of 15 overarching codes. Then, the data from all 29 transcripts were imported into ATLAS.ti Web (version 22.1.5; ATLAS.ti Scientific Software Development GmbH) [ 41 ] and coded based on the 15 overarching codes. In the fifth phase of analysis, data reports were created using ATLAS.ti Web [ 41 ] based on each of the 15 overarching codes. These were exported to Microsoft Excel (Microsoft Corp) so that the data in each code group could be further examined. Using constant comparison, we sifted, sorted, combined, and collapsed the data in the 15 groupings to form 4 themes, each with a central organizing concept that provided a clear definition of the theme [ 38 ]. We continued to compare data with data to develop the properties for each of the 4 themes. Finally, in the sixth phase, each theme was named, and its properties were refined. With a focus on the research aims, the research team then produced a written report interpreting the meaning of each theme.

The overall process of data collection and analysis was influenced by the team’s commitment to social justice and to the goal of understanding the data of each participant while considering their context. Thus, at various points during the research process, each member of the 5-member analysis team engaged in dialogue together and in individual writing of reflexive memos to name any judgments (positive and negative) or concerns that were felt while engaged in the research process, with the goal of reducing the influence of bias on the collection and interpretation of data [ 38 , 39 ].

Participant Characteristics and Sample Demographics

The demographics of our sample of 29 participants are presented in Table 1 . Of the 29 participants, 16 (55%) voluntarily shared that they were employed in peer support, hospice care, or health care sales or at a community agency doing health-related work. A total of 72% (21/29) of the interviews were conducted in English, and 28% (8/29) were conducted in Spanish. The duration of the Spanish interviews ranged from 22 to 72 (mean 32, SD 16.61) minutes, and that of the English interviews ranged from 15 to 85 (mean 38, SD 15.49) minutes.

a GED: General Educational Development.

b JD: Juris Doctor.

c PhD: Doctor of Philosophy.

d MD: Medical Doctor.

e GAD-2: 2-item Generalized Anxiety Disorder scale. A GAD-2 score of 3 is the recommended cutoff point for identifying possible cases of generalized anxiety disorder.

f PHQ-2: Patient Health Questionnaire–2. A PHQ-2 score of 3 is the recommended cutoff point for identifying possible cases of depression.

Qualitative Thematic Analysis Results

Thematic analysis of the qualitative data led to the identification of four themes: (1) having to change my coping due to the pandemic, (2) confronting shifting perceptions of diverse groups on mental health stigma, (3) “Feels like home”—experiencing a sense of inclusivity and belonging in T4W/Juntos, and (4) “It’s a one-stop-shop”—judging T4W/Juntos to be a desirable and useful website.

Theme 1: Having to Change My Coping Due to the Pandemic

Participants shared that, during the pandemic, they had to change the way in which they coped with daily life stressors. This was represented by 5 properties: increased use of technology to connect with others on the internet, intentionally identifying self-care tips and techniques, coping by helping other people, relying on in-home socialization, and drawing on spiritually oriented coping ( Figure 1 ).

Increased Use of Technology to Connect

Participants found that their use of technology increased during the pandemic, and they had to learn to accept their increased reliance on the internet to be connected in various ways. For example, they used technology to connect with information on a variety of topics and be able to accomplish work for their jobs. They used technology to connect with other people for social reasons; this included using Zoom to connect with friends, family, and their faith communities. They also joined web-based support groups and community groups where they could engage in dialogue with others. One participant described how they felt more “comfortable” having conversations on the internet:

I think I’ve become more dependent on the internet. I also found that not having to deal with people face to face most of the time makes me feel more comfortable. Honestly, it’s easier for me to have a chat on the computer than in real life.

Participants said that they relied on technology to meet their therapeutic needs more than before the pandemic. They found web-based resources to be “easier to forward” and share with others. They described resources on the internet to be “more documented” and viewable. They reported how they learned to click links to use web-based resources, which, for many, was a new behavior.

Intentionally Identifying Self-Care Tips and Techniques

Participants helped themselves by seeking out practical approaches, including self-care tips and techniques. This meant that they were using technology to meet their therapeutic needs, something they had not necessarily done before. They used warmlines, crisis lines, and teletherapy to meet their needs. They learned about meditation and breathing exercises, which they found especially desirable because the stress of the pandemic was experienced as personally difficult. However, participants found it challenging to find “accurate” resources. This put them on a quest to find “reliable” web-based resources they could use to reduce stress. Participants explained that they continued searching on the internet even if their immediate need was resolved because they wanted to have resources ready just in case they needed them in the future. In addition, some used art to self-soothe during the pandemic, whereas others sought “self-improvement” strategies.

Coping by Helping Other People

When asked to say more about how they handled their own stressful experiences during the pandemic, participants repeatedly spoke of helping other people in their personal lives and on the job. It seemed that helping other people was itself a strategy they used to cope. A participant spoke of others who felt “invisible” and as if other “people don’t respect them” and how difficult it was for them because “they’ve lost their purpose because they can’t go to work or can’t do the job they used to” do. They noted how important it was to share with others that “there’s hope...that there are people out there trying to make a difference, trying to help, trying to listen . ” Participants’ efforts were extended to various types of people, including family members, friends, and coworkers, and those who self-identified as health workers reported that they helped both individual clients and families. Experiences of helping others stood out to them; they felt a sense of “satisfaction” from their helping work. One participant said the following:

And the reason I liked it is—and the reason is the feedback, the participation and everything, is actually one of the ways that it makes me feel like not only I’m sharing a resource, but I’m sharing a resource that I know is good—I hate sharing things that I know are not good—and I wanted to like what I’m doing here.

Relying on in-Home Socialization

Finding others to fulfill social needs and desires was mainly limited to whoever was in the home. Participants relied on their family members or roommates for dialogue, socialization, and friendship during the pandemic. However, pets also played an important role as they provided “joy, stress relief, and companionship.” Many described the importance of going on walks with their dogs as it brought about daily exercise and also could open up dialogue with neighbors, which was highly valued at this time of social isolation. One participant shared how meaningful it was to live with their 2 dogs and son. They said the following:

If I had to live by myself, I don’t know how I would get through this. I really mean it. I’m being perfectly honest.

Drawing on a Spiritually Oriented Approach to Cope

With few options for socialization, participants shared that they turned to spiritually oriented routes for coping. They turned to “God” and relied on their faith to keep them going. One participant stated that they could not imagine how anyone could “get through” the pandemic “without God.” For some, listening to religious radio programs filled a crucial need in their daily lives during the pandemic. Others reported using prayer or reading scriptures. A participant described how religion provided “guidance” on daily life:

[Faith in God] helps me first and foremost, that helps me not to have fear. I think that a lot of people now are controlled by fear. And so that helps me, y’know; that strengthens me and gives peace to my heart. I feel secure with my health habits, with my diet, well, because I’m connected to God, and because I get my health practices from the Bible. So, I feel that all around, my mental, my physical, my emotional, all my wellbeing, I dedicate that to God for his blessing. And so, y’know, I think that’s the biggest part of it.

Theme 2: Confronting a Context of Shifting Perceptions of Mental Health Stigma Among Diverse Groups

When discussing T4W/Juntos and its purpose in helping with emotional well-being, participants were concerned about the context during the pandemic related to public views on mental health. They raised the issue of shifting views on mental health stigma within different communities and how it impacts people. They reported how stigma differs based on age or generation, race, or ethnicity and how the T4W/Juntos website would be received within a context of overarching stigma in various communities. They also suggested ways to reduce stigma. This is reflected in 3 properties ( Figure 2 ).

Experiences With Mental Health Stigma Vary Across Generations

Mental health stigma was perceived as an issue for all generations. Participants explained that stigma itself was the backdrop that set the stage for how the T4W/Juntos website would or would not be received. This could have an impact on whether community members would embrace the website.

Participants explained that there was a difference in how older and younger generations experienced mental health stigma. Older adults who grew up during a time when mental illness was considered “bad” and “dangerous” were described as rejecting the possibility of seeking treatment for their mental health problems. They described a dual process in which the older generation felt too stigmatized to seek mental health support, but at the same time, they perpetuated the stigma surrounding mental health within their communities. Older adults were perceived as having negative beliefs about mental health concerns, sometimes viewing them as a personal “weakness” or a result of lack of religious practice (eg, “devil’s work”) rather than a psychological condition. They were seen as discouraging other people from reaching out for such treatment, help, or resources. One participant shared how older people spoke about mental health treatment:

Looking for help, like, with a therapist...they shouldn’t be sharing their opinions. That’s what I’ve heard, “Why would you see a therapist, if they’re not God?”...that’s what I’ve heard. Like, “Why should you go around telling them your problems?”

Isolation was perceived as a major contributor to older adults’ mental health issues, especially for those who lived in residential settings. This raised concerns during the pandemic because participants reported that resources tailored to older adults living in such settings were not available, especially for those who were “losing loved ones” due to COVID-19. Participants worried about older adults “not being able to see their families” during the COVID-19 pandemic and how they would cope as stigma could be a barrier to obtaining the help they needed.

Younger generations were perceived as having grown up with greater awareness of mental health and, therefore, were affected by stigma in a different way. They were thought to be “more empowered” to openly discuss mental health issues. In particular, participants noted a more welcoming conversation about mental health on social media among younger generations, including during the pandemic. The differences between generations were described by one participant as follows:

I feel like that [the older generation] was like, “We’re not telling anyone our business.” And “This is family business, keep it to yourself.” Whereas like the mid-30s and maybe late 20s, they’re like, “You know what? Let’s talk to somebody, let’s get help, let’s like—We’re not going to suffer in silence.”

Stigma Is Experienced in Unique Ways in Different Racial and Ethnic Communities

Mental health stigma was perceived as experienced variably based on race and ethnicity, which had implications for the context of the pandemic. Participants shared examples of how Black, Latine, Asian, and other minoritized communities faced more mental health stigma in general compared with other communities due to a combination of societal and cultural factors. One Latine participant said the following:

Because I grew up culturally Latino, so, there is a huge stigma around mental health where you couldn’t just say, “Oh, I’m feeling anxious,” or “I’m feeling a little depressed.”

Another participant said that, in their Asian American community, mental health was “heavily stigmatized” and people “don’t tend to like to ask for help.”

The words participants heard being used to stigmatize mental health or people with emotional challenges, such as “crazy” and “weak,” were similar across racial and ethnic minoritized groups. While all people were seen as actively avoiding being labeled as having a mental health issue, those from minoritized communities were perceived as especially cautious because such negative mental health labels could be used as “leverage” against them. Therefore, a winning strategy used by community-based health workers in our sample was to “give them the information without having to use that word [mental health].” Others described avoiding being labeled by addressing physical rather than psychological symptoms. This provided a perspective for addressing mental health by taking care of physical health. One participant explained this as follows:

I think if the focus is not so much on mental illness but mental wellness, mental health, and that connection between the mind and the body, and that it’s all important, and addresses the person as a whole.

Recommending Effective Ways to Combat Mental Health Stigma

Participants recommended providing accessible educational resources on mental health to the public. One suggested that stigma could be reduced if mental health was discussed in the same way in which health providers engaged in “teaching someone how cancer works . ” Other suggestions included the strategy of individuals openly sharing their personal stories of mental health struggles to dismantle stigma and encourage help-seeking behaviors. One participant explained that we need “to recognize that we all have trauma, and to set the example ourselves.”

In terms of sharing the T4W/Juntos website and other resources, participants suggested that, rather than just directing people where to go, we should share our experiences honestly. One participant recommended saying things such as “This happened to me and I went here to get help. That helped me a lot because I did this” or “I also went through the same situation.”

Participants encouraged efforts to create safe and supportive spaces, especially for marginalized individuals who may face additional stressors, such as “LGBTQAI+ students,” adolescents, and older adults. Participants reported that they would feel more comfortable recommending a warmline where individuals could connect with trained volunteers, therapists, or peers who could listen and provide support rather than “an Excel [sheet] of resources.” While participants endorsed efforts such as the T4W/Juntos website, they recommended investing in “more intersectional conversations” where leaders “that really represent a community” could share things such as “this is me, and this is what I’m going through. This is what I do to deal with it. This is where I go for help . ” This approach was perceived to increase “comfort” and “acceptance,” unlike “faceless” things, because it could reduce stigma and negative perceptions of help seeking.

Theme 3 “Feels Like Home”: Experiencing a Sense of Inclusivity and Belonging in T4W/Juntos

Participants provided robust reports of feeling welcomed and included when visiting the T4W/Juntos website. This sentiment is reflected in 4 properties: feeling included due to welcoming tone and visuals; feeling included due to the substantial, diverse, and quality resources on T4W/Juntos; many languages making T4W/Juntos “more accessible”; and recommending ways to increase inclusivity on T4W/Juntos ( Figure 3 ).

Feeling Included Due to Welcoming Tone and Diverse Visuals

The images and overall tone of T4W/Juntos gave participants a sense of belonging when using the website. They described the site’s imagery as “cheerful,” “friendly,” “bright,” “happy,” “fun,” “calming,” and “light and airy.” One participant explained the following:

...the color scheme and the font, it’s just very inviting and not intimidating. ‘Cause I think finding like health resources or mental health services or any of these topics, they’re very heavy. So, having a page that’s bright and makes it simple and has the cute little icon next to each topic makes it a little more digestible.

Other participants focused on T4W/Juntos’ esthetics. One participant indicated that it was “well-balanced” with “just enough seriousness.” Another participant highlighted the site’s “high production value” in terms of visual and auditory content. The quality of the content was valued, including the mix of both cartoon and real images, the “scenery” in graphics, and the quality of the spoken Spanish in videos.

Participants noted the importance of diversity in T4W/Juntos’ images in helping them feel included. They appreciated seeing the “authentic representation” of various ages, gender identities, abilities, sexualities, and races and ethnicities, among other characteristics, in “these beautiful faces” they saw on the website. Participants indicated that T4W/Juntos “feels like home” because the images were specifically representative of California’s population. One participant stated the following:

I feel like it covered populations and community members across California who would be possibly using the website. And also, just showing that diversity. So, I think that creates a welcoming environment as well if people can see themselves represented in some capacity on the website, especially on the front page.

Unlike other mental health websites that participants described as judgmental or exclusionary, participants felt that T4W/Juntos was not overly “clinical” or “bashing you with some mental illness stigma.” Furthermore, T4W/Juntos’ diverse representation was different from that of other sites where “only one type of person” was represented, which meant that visitors to T4W/Juntos would not “feel like they’re an outsider,” as one participant succinctly explained:

...if there’s nobody on the website that I can identify with, maybe it doesn’t...doesn’t apply to me kind of thing. There’s plenty of opportunity, I think, for anyone to feel like they fit [on T4W /Juntos ].

Feeling Included Due to the Substantial, Diverse, and Quality Resources on T4W/Juntos

The quality, quantity, and variety of resources present on the T4W/Juntos site greatly contributed to participants feeling welcome and included. Some expressed appreciation in broad strokes, noting that there were resources for “every ethnicity” and “different ages” and that the T4W/Juntos team “took many different things into account.” Other participants valued the inclusion of resources for specific groups, such as African American individuals, American Indian and Alaska Native individuals, the “LGBTQIA2+ community,” parents, children, and people living with disabilities. Knowing that the resources were intentionally selected for T4W/Juntos mattered. One participant noted that “...the thought put into making [T4W/Juntos] usable or worthwhile to a number of different communities was made and paid attention to . ” Another interviewee found it “refreshing to realize that the [T4W/Juntos] project...had equity kinda built from the top up.” One participant encapsulated this by saying the following:

It just felt like I was coming to a buffet, a big place to finally like heal, y’know? It was like “Oh, I don’t even have to eat this. There’s like a little bit of that, more of that.” And there’s really—the variety of choices, and the way that it was put, it was very inviting. Also, it was very welcoming, and I left feeling satisfied, but also, I left—like there was stuff that I could share with people. And I did.

“This Is Kinda Cool”: Many Languages Make T4W/Juntos “More Accessible”

Linguistic accessibility was another important aspect of the T4W/Juntos site that made participants feel included. Because of California’s multicultural population, participants believed that T4W/Juntos needed to be offered in multiple languages, especially Spanish and Vietnamese, for it to be considered “culturally appropriate” and widely accessible. One participant stated that “I love that the website already has a few options in different languages...Just thinking about the different audiences [will] make it that much more accessible . ” The creation and availability of a Spanish translation contributed to another participant’s feelings of inclusion:

So, it made me welcome and then it also made me understand more, like I said, because it was in Spanish and English.

Others noted the need for content in more languages; some specified a desire for content in Vietnamese, Indigenous languages (broadly), and Mixtec due to the large population of Mixtec-speaking migrant workers in California.

In addition to being offered in multiple languages, many participants lauded the site for being written in “everyday” and “plain” language that was “easy to understand” and “basic.” Another participant expressed their thought process upon first hearing about T4W/Juntos:

I was like, “This is kinda cool. Let’s check this out.” It wasn’t something like, “Oh, wait, this is way beyond my expertise, or this is something I don’t fully understand.”

Although many found T4W/Juntos easy to understand, a few respondents said that the language was inaccessible or overly professional. For instance, one person commented that the “writing [in T4W/Juntos] was too academic,” and therefore, it “wasn’t an easy read. It was like reading a textbook or a law book . ” Others noted that terms such as “resilience” or “anxiety” made the site feel overly clinical and not intended for the average user.

There was concern that some users with low computer literacy may not be able to use the site. For example, one participant cautioned that some Spanish speakers may not necessarily know the word for “link” in English or Spanish ( enlace ). Furthermore, several participants who worked with immigrant communities indicated that many in these groups cannot read or write in Spanish or English, which precludes them from making use of the T4W/Juntos site.

Recommending Ways to Increase Inclusivity on T4W/Juntos

Some participants advocated for changes or additions that would further widen the net of inclusivity. For example, participants suggested adding information on mental health symptoms and treatment options; trauma and its potential effects; support for basic needs such as housing, rental assistance, and financial support; information about civic engagement (eg, how to register to vote and contacting local officials); and recreational activities such as art classes and book clubs.

Although many participants found the wide breadth of representation on T4W/Juntos to be quite impressive, some wanted even more diverse visual representation. Some perceived a few groups to be conspicuously absent on the site, such as the lack of representation of older adults. One participant stated the following:

...older adults have really had a hard time with isolation and access and I don’t really see older adults represented, at all—at all, at all, at all, like, at all, in this whole thing. Not just the graphics, but the people you’ve chosen to be on the little videos, the images, the content, there’s nothing about older adults, that I found.

Another participant specified the need for more youth representation:

...make sure that we have as many opportunities for youth to be able to see themselves talking, working with each other, reaching out, but really knowing that we’re all here for them. And I think that was one of the things...that I would really suggest, is that opportunity.

Other suggestions included adding more representation of men of color, including “African American men, Latino men and Armenian men,” as well as police officers and military veterans as these groups tend to avoid seeking treatment due to stigma against mental health. Others suggested that representation be enhanced with more images or voices of people with disabilities; individuals from the lesbian, gay, bisexual, transgender, and queer community; and actual community members.

Theme 4 “It’s a One-Stop-Shop”: Judging T4W/Juntos to Be a Desirable and Useful Website

Participants judged the website as a hub that brought many things together in one place, making it a “one-stop-shop.” For this reason, most described it as a desirable website. Their perceptions reflect 4 main ways in which they experienced the website: perceiving T4W/Juntos as trustworthy, being equipped with a “first step” tool to use and share, finding navigation to be simple and clear, and easily accessing useful information ( Figure 4 ).

Perceiving T4W/Juntos as Trustworthy

Participants valued the website due to the reliable information on it that had been curated from credible sources, which was able to combat misinformation. One participant reported the following:

I feel like it’s done by UCLA, UC Davis, all names that I really trust...it’s a name that people recognize and that you can trust. So, I have no reservations whatsoever about this website...I know that when I scroll all the way at the end with the different sponsors or collaborations made it legit.

Seeing the real faces and hearing the real voices of community members in the videos plus the logos of respected agencies on the website enhanced the sense that it was “legitimate.” Trustworthy information was especially desirable to combat the misinformation they reported hearing about in many communities.

Being Equipped With a “First Step” Tool to Use and Share

Participants felt equipped because the website gave them tools necessary not just for learning about resources but also for sharing with clients, friends, and family members. One participant noted how T4W/Juntos was something they had been searching for but had not found:

And it just was primarily what it felt like, a resource portal. And so, it felt like the right door to go get help, rather than the wrong door. So that’s a good way of putting it. It felt like I had finally opened the right door that I’d been looking for.

As a tool, T4W/Juntos was judged as helpful because it allowed participants to gain access to needed mental health resources during the pandemic, with options so that clients could start “where they want to” with the goal of receiving help. They liked having access to specialized information that addressed a wide range of topics, such as resources on grief for pregnant women or up-to-date COVID-19 information. They found it desirable because it was designed for the “average person,” so it was useful as a “first step” even for those with no previous knowledge about mental health resources.

Navigating Is Simple and Clear

Participants perceived T4W/Juntos to have appealing features such as inviting colors and fonts, which made the content “more digestible.” The technology functioned smoothly, including the hyperlinks and videos. One participant said the following:

And it’s really easy to go on there, navigate, and look for information. And it’s also a good way to empower the clients I work with, so they can go and do their own research about any resources they may need in regards to mental health.

The process of searching for information was clear even when working with groups lacking digital literacy, which they noted was required for some websites. One participant noted the following:

Honestly, this is one of the pages that I remember my mom and I—even though we were dazed and emotionally exhausted—we were able to understand and get the information because it was all so simple. That was the only thing I can tell you. Despite everything that happened, we saw that if we were going from one place to another looking for information or trying to analyze, sometimes it was very elaborate. We needed something like they say in English, “short and sweet,” not fancy or too negative. Something within the positive and informative things but without being research papers that we had to be reviewing and analyzing, because we didn’t have the capacity to do that. We needed simple and easy to understand information.

Easily Accessing Useful Information

The ease of using T4W/Juntos to access information was a valued feature. Participants found the website to be a user-friendly “one-stop-shop,” a place where they could find plenty of useful resources to choose from all at once. They preferred this to having to type specific topic words into a search engine to find needed resources individually. Some especially endorsed the feature that allowed them to receive immediate active help through a direct crisis number, whereas others favored the option to receive informational help by downloading materials to read. While participants overall considered T4W/Juntos to be easy to use, some suggested making the pages more “scroll-friendly...like Instagram,” and another participant suggested adding an “emergency exit” button so that users could quickly switch to a different site if needed for safety reasons.

Principal Findings

The qualitative findings from telephone interviews as a complement to quantitative surveys suggest that, during the COVID-19 pandemic, the T4W/Juntos website was well received by both community health service providers and community members in the interview sample as a useful, accessible tool, with some concerns noted such as language sometimes being too “professional.” Our findings further suggest that the pandemic catalyzed significant changes in the way people coped, which fueled a shift to digital solutions when other options were suddenly off-limits due to stay-at-home orders. Our participants tended to their own emotional well-being personally and assisted their friends and families, and some also engaged in trying to help clients or patients as well. Their pivot to reliance on technology during the pandemic ranged from finding new techniques for soothing stress to connecting with others in a meaningful way via the internet to meet socialization and support needs. Roommates were crucial for socializing, as was also suggested by Shigeto et al [ 42 ], because social distancing limited social contact. Similar to other studies [ 43 - 45 ], our participants found pets to provide companionship and effective ways to cope with the isolation of stay-at-home orders. Notably, as other researchers found, being able to help other people during the pandemic in and of itself gave participants a mood boost [ 43 , 46 ]; this made the T4W/Juntos website even more valuable because participants could share it with others.

Similar to the findings of other studies [ 47 - 49 ], our participants shared that mental health stigma and taboo attitudes had often thwarted attempts to access needed mental health care, and this was especially the case for those from ethno-racially minoritized communities and older adults. However, participants did not perceive the T4W/Juntos site as invoking stigma, judgment, or condescension. They were particularly cognizant of the efforts of the development team to create a site that was neither intimidating nor shaming. They found it to be a digital space that successfully communicated that someone was out there trying to help others in a world that was otherwise shut down due to COVID-19. The collaborative approach to the development of the website may have been why the written text and verbal communication in the site’s videos were described as an example of a positive way to talk about mental health.

To combat stigma through a website, input from potential users, such as our participants, is crucial for design enhancement. As already noted, during the development of T4W/Juntos, input from various members of diverse California communities addressed the making of the website, including the goal of reducing stigma related to mental health [ 31 ]. Efforts to reduce stigma require sensitivity to the language used; with T4W/Juntos, we intentionally used neutral, nonclinical language so that experiences such as stress, anxiety, depression, and grief were addressed as normal aspects of life that many of us deal with [ 31 ]. Our participants recommended featuring pictures, animations, and videos to showcase resources in a personal way that is colorful and inviting without stigma and that reflects the commitment of the T4W/Juntos team. In addition, several short videos in English and Spanish were created to introduce each section of the T4W/Juntos website with the goal of making users feel more comfortable with the topics; volunteers from diverse California communities served as relatable actors who were filmed during the pandemic via Zoom in their homes. The diverse representation is likely why participants said that the website felt comfortable. We also included links to active warmlines and hotlines so communication with an actual human being was possible through telephone and texting [ 31 , 34 , 35 ]. However, consistent attention to making these links convenient and prominent on the website is needed over time to maintain a steady focus on reducing stigma. Additional efforts could be made in the future to link to more and different venues available on the internet where diverse community leaders talk about their own emotional health concerns or share what they have found helpful. In addition to the immediate sense of welcome, participants found the extent and variety of the content on T4W/Juntos (ie, the plentiful links to various resources) to communicate supply rather than unmet demand. The sense of options for resources was understood as high accessibility, which somehow also reduced stigma. Participants seemed to relish what they perceived to be a bounty of ready resource links, including content in multiple languages. This, during the pandemic, was appreciated because it was a time when avenues for the typical sources of useful or desirable material were severely reduced.

The sense of belonging reported by participants suggests the profound impact of a culturally competent design in enhancing user engagement with and experience on the website. For example, the colorful look of T4W/Juntos was developed in collaboration with community members [ 31 ]. The decisions to give T4W/Juntos an upbeat feel, feature relatable people from diverse California communities in the videos, and provide options in diverse languages were all made collaboratively with community partners. The team’s intentional efforts and commitment to convey diversity resonated with participants, making the website “feel like home.” Furthermore, as the partnering organizations behind the creation of the website were clearly listed on the site for the purpose of transparency regarding who was behind the website, participants perceived it to be a trustworthy tool.

Overall, our findings highlight that the T4W/Juntos website functions as a comprehensive, inclusive, and user-friendly platform for coping with mental health challenges, particularly during the pandemic. It was a web-based “one-stop-shop” due to the culmination of several integrated features that generated positive regard. However, despite its many strengths, there were also suggestions for improvements to the website to further enhance inclusivity. As was suggested by some participants, more resources, pictures, stories, and testimonials are needed to reduce stigma, specifically for older adults; adolescents; lesbian, gay, bisexual, transgender, and queer communities; police officers; and veterans. In terms of functionality, certain adjustments were requested, including a “back” button and a drop-down menu for a better user experience.

Limitations

Some aspects of this study were restrained due to the pandemic. For example, we were only able to interview English- and Spanish-speaking adults. In addition, we used convenience sampling with recruitment based on invites from clients and community partners of staff and providers of community partner agencies. While this approach resulted in a diverse sample, it included community-based health and wellness workers and is not necessarily representative of California residents. Nonetheless, during the COVID-19 pandemic, these voices were extremely valuable and garnered important insights. While 55% (16/29) of the participants indicated that they worked in community-based support or health care, we did not collect specific data on employment status or occupation. We can only assume based on education and other factors that approximately half of our sample were community members not employed in health care. Thus, future research should systematically collect employment data as context for participants’ level of familiarity with health-related resources.

Relatedly, the COVID-19 pandemic put limits on potential participants’ ability to engage in a study when they were dealing with other worries. Thus, our sample was diverse in some ways but could have been more reflective of California’s population. For example, we were successful in recruiting 7% (2/29) of participants who were Southeast Asian; however, no participants self-identified as being from South Asian or East Asian communities despite the large numbers in California. Future research should expand recruitment efforts to be inclusive of the many subgroups in the state to bring insight from a more diverse sample.

Conclusions and Future Implications

Our results complement the findings of the quantitative evaluation that showed engagement in website use and an association with reduced depression over time [ 34 ]. The results underscore the value of collaborating with members of the target community to have a meaningful impact when designing a digital tool for the public. Specific partner website design suggestions to include videos; language accessibility; diverse representation; and colorful, cheerful visuals contributed to the positive reception of this website. The findings suggest that, while T4W/Juntos has been effective in addressing diverse needs, there are ongoing opportunities to maximize inclusivity and user experience.

First, future studies on mental health website development would benefit from engaging with a diverse sample of the target group and conducting pilot tests to learn more specifically what accessibility means to potential users. Second, the results showed that mental health stigma continues to be an issue, especially among minoritized communities. Hence, resources tailored to such groups must consider what stigma looks like to members of each group and how to address it in the specific context of minoritized communities so that valuable information about mental health will be received and accepted. Finally, participants indicated that the T4W/Juntos website was useful for their personal needs, sharing with loved ones, or incorporating into their work with minoritized communities. The website’s unique features—especially its diverse representation and availability in multiple languages—make it a valuable addition to the mental health resource landscape, and thus, it may be recommended for dissemination throughout the state of California, especially when including input from other diverse populations.

Acknowledgments

The authors would like to acknowledge the community partner agencies in the state of California who collaborated in the development of the Together for Wellness/Juntos por Nuestro Bienestar website and assisted in disseminating information that led to the recruitment of our sample, including African Communities Public Health Coalition, Boat People SOS, Cal Voices, California LGBTQ Health and Human Services Network, Healthy African American Families II, Health Education Council, National Alliance on Mental Illness California, United Parents, and Visión y Compromiso, and our collaborators at Chorus Innovations and University of California, Davis. Funding was obtained from a Federal Emergency Management Agency and Substance Abuse and Mental Health Services Administration grant (which was managed by the California Mental Health Services Authority through the California Department of Health Care Services for COVID-19 crisis intervention prevention; 700-FEMA-2021-UCLA), a California Health Care Foundation grant (award G-31202), and an award from the California Department of Health Care Services (award 21-10458).

Data Availability

The data sets generated during and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

MVH, KBW, and DFR conceived and designed the study. MVH analyzed and interpreted the data and drafted and revised the manuscript. JL, MPC, and JIM analyzed and interpreted the data and made substantial contributions to drafting and revising the manuscript. DFR collected and participated in the analysis and interpretation of the data. KBW also critically revised the draft for important intellectual content. All authors granted final approval for the version to be published.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 14.12.23; peer-reviewed by C Watfern, D Levine; comments to author 15.01.24; revised version received 23.01.24; accepted 31.01.24; published 25.03.24.

©MarySue V Heilemann, Jianchao Lai, Madonna P Cadiz, Jocelyn I Meza, Daniela Flores Romero, Kenneth B Wells. Originally published in JMIR Formative Research (https://formative.jmir.org), 25.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

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18 Content Analysis

Lindsay Prior, School of Sociology, Social Policy, and Social Work, Queen's University

Published: 04 August 2014
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In this chapter, the focus is on ways in which content analysis can be used to investigate and describe interview and textual data. The chapter opens with a contextualization of the method and then proceeds to an examination of the role of content analysis in relation to both quantitative and qualitative modes of social research. Following the introductory sections, four kinds of data are subjected to content analysis. These include data derived from a sample of qualitative interviews (N = 54), textual data derived from a sample of health policy documents (N = 6), data derived from a single interview relating to a “case” of traumatic brain injury, and data gathered from 54 abstracts of academic papers on the topic of “well-being.” Using a distinctive and somewhat novel style of content analysis that calls upon the notion of semantic networks, the chapter shows how the method can be used either independently or in conjunction with other forms of inquiry (including various styles of discourse analysis) to analyze data, and also how it can be used to verify and underpin claims that arise out of analysis. The chapter ends with an overview of the different ways in which the study of “content”—especially the study of document content—can be positioned in social scientific research projects.

What is Content Analysis?

In his 1952 text on the subject of content analysis, Bernard Berelson traces the origins of the method to communication research and then lists what he calls six distinguishing features of the approach. As one might expect, the six defining features reflect the concerns of social science as taught in the 1950s, an age in which the calls for an “objective,” “systematic,” and “quantitative” approach to the study of communication data were first heard. The reference to the field of “communication” was of course nothing less than a reflection of a substantive social scientific interest over the previous decades in what was called public opinion, and specifically attempts to understand why and how a potential of source of critical, rational judgement on political leaders (i.e., the views of the public) could be turned into something to be manipulated by dictators and demagogues. In such a context, it is perhaps not so surprising that in one of the more popular research methods texts of the decade, the terms content analysis and communication analysis are used interchangeably (see Goode & Hatt, 1952 :325).

Academic fashions and interests naturally change with available technology, and these days we are more likely to focus on the individualization of communications through Twitter and the like, rather than of mass newspaper readership or mass radio audiences, yet the prevailing discourse on content analysis has remained much the same as it was in Berleson’s day. Thus Neuendorf (2002 :1), for example, continues to define content analysis as “the systematic, objective, quantitative analysis of message characteristics.” Clearly the centrality of communication as a basis for understanding and using content analysis continues to hold, but in this article I will try to show that, rather than locate the use of content analysis in disembodied “messages” and distantiated “media,” we would do better to focus on the fact that communication is a building block of social life itself and not merely a system of messages that are transmitted—in whatever form—from sender to receiver. To put that statement in another guise, we need to note that communicative action (to use the phraseology of Habermas, 1987 ) rests at the very base of the lifeworld, and one very important way of coming to grips with that world is to study the content of what people say and write in the course of their everyday lives.

My aim is to demonstrate various ways in which content analysis (henceforth CTA) can be used and developed to analyze social scientific data as derived from interviews and documents. It is not my intention to cover the history of CTA or to venture into forms of literary analysis or to demonstrate each and every technique that has ever been deployed by content analysts. (Many of the standard textbooks deal with those kinds of issues much more fully than is possible here. See, for example, Babbie, 2013 ; Berelson, 1952 ; Bryman, 2008 , Krippendorf, 2004 ; Neuendorf, 2002 ; and Weber, 1990 ). Instead I seek to recontextualize the use of the method in a framework of network thinking and to link the use of CTA to specific problems of data analysis. As will become evident, my exposition of the method is grounded in real world problems. Those problems are drawn from my own research projects and tend to reflect my particular academic interests—which are almost entirely related to the analysis of the ways in which people talk and write about aspects of health, illness, and disease. However, lest the reader be deterred from going any further, I should emphasise that the substantive issues that I elect to examine are secondary if not tertiary to my main objective—which is to demonstrate how CTA can be integrated into a range of research designs and add depth and rigour to the analysis of interview and inscription data. To that end, in the next section I aim to clear our path to analysis by dealing with some issues that touch on the general position of CTA in the research armory, and especially its location in the schism that has developed between quantitative and qualitative modes of inquiry.

The Methodological Context of Content Analysis

Content analysis is usually associated with the study of inscription contained in published reports, newspapers, adverts, books, web pages, journals, and other forms of documentation. Hence, nearly all of Berelson’s (1952) illustrations and references to the method relate to the analysis of written records of some kind, and where speech is mentioned it is almost always in the form of broadcast and published political speeches (such as State of the Union addresses). This association of content analysis with text and documentation is further underlined in modern textbook discussions of the method. Thus Bryman (2008) for example, defines content analysis as “an approach to the analysis of documents and texts , that seek to quantify content in terms of pre-determined categories” (2008:274, emphasis in original), while Babbie (2013) states that content analysis is “the study of recorded human communications” (2013:295), and Weber refers to it as a method to make “valid inferences from text” (1990:9). It is clear then that CTA is viewed as a text-based method of analysis, though extensions of the method to other forms of inscriptional material are also referred to in some discussions. Thus Neuendorf (2002) , for example, rightly refers to analyses of film and television images as legitimate fields for the deployment of CTA, and by implication analyses of still—as well as moving—images such as photographs and billboard adverts. Oddly, in the traditional or standard paradigm of content analysis, the method is solely used to capture the “message” of a text or speech; it is not used for the analysis of a recipient’s response to or understanding of the message (which is normally accessed via interview data and analyzed in other and often less rigorous ways; see, e.g., Merton, 1968 ). So in this article I suggest that we can take things at least one small step further by using CTA to analyse speech (especially interview data) as well as text.

Standard textbook discussions of CTA usually refer to it as a “non-reactive” or “unobtrusive” method of investigation (see, e.g., Babbie, 2013 :294), and a large part of the reason for that designation is due to its focus on already existing text (i.e., text gathered without intrusion into a research setting). More importantly, however, (and to underline the obvious) CTA is primarily a method of analysis rather than of data collection. Its use therefore has to be integrated into wider frames of research design that embrace systematic forms of data collection as well as forms of data analysis. Thus routine strategies for sampling data are often required in designs that call upon CTA as a method of analysis. These latter can either be built around random sampling methods, or even techniques of “theoretical sampling” ( Glaser & Strauss, 1967 ) so as to identify as suitable range of materials for content analysis. CTA can also be linked to styles of ethnographic inquiry and to the use of various purposive or non-random sampling techniques. For an example, see Altheide (1987) .

Of course, the use of CTA in a research design does not preclude the use of other forms of analysis in the same study, for it is a technique that can be deployed in parallel with other methods or with other methods sequentially. For example, and as I will demonstrate in the following sections, one might use CTA as a preliminary analytical strategy to get a grip on the available data before moving into specific forms of discourse analysis. In this respect it can be as well to think of using CTA in, say, the frame of a priority/sequence model of research design as described by Morgan (1998) .

As I shall explain, there is a sense in which content analysis rests at the base of all forms of qualitative data analysis, yet the paradox is that the analysis of content is usually considered to be a quantitative (numerically based) method. In terms of the qualitative/quantitative divide, however, it is probably best to think of CTA as a hybrid method, and some writers have in the past argued that it is necessarily so ( Kracauer, 1952 ). That was probably easier to do in an age when many recognised the strictly drawn boundaries between qualitative and quantitative styles of research to be inappropriate. Thus in their widely used text on “ Methods in Social Research ,” Goode and Hatt (1952 :313), for example, asserted that, “[M]odern research must reject as a false dichotomy the separation between ‘qualitative’ and ‘quantitative’ studies, or between the ‘statistical’ and the ‘non-statistical’ approach.” It was a position advanced on the grounds that all good research must meet adequate standards of validity and reliability whatever its style, and it is a message well worth preserving. However, there is a more fundamental reason why it is nonsensical to draw a division between the qualitative and the quantitative. It is simply this: all acts of social observation depend on the deployment of qualitative categories—whether gender, class, race, or even age; there is no descriptive category in use in the social sciences that connects to a world of “natural kinds.” In short, all categories are made, and therefore when we seek to count “things” in the world, we are dependent on the existence of socially constructed divisions. How the categories take the shape that they do—how definitions are arrived at, how inclusion and exclusion criteria are decided upon, and how taxonomic principles are deployed—constitute interesting research questions in themselves. From our starting point, however, we need only note that “sorting things out” (to use a phrase from Bowker & Star, 1999 ) and acts of “counting”—whether it be of chromosomes or people ( Martin and Lynch, 2009 )—are activities that connect to the social world of organized interaction rather than to unsullied observation of the external world.

Of course, some writers deny the strict division between the qualitative and quantitative on grounds of empirical practice rather than of ontological reasoning. For example, Bryman (2008) argues that qualitative researchers also call upon quantitative thinking but tend to use somewhat vague, imprecise terms rather than numbers and percentages—referring to frequencies via the use of phrases such as “more than” and “less then.” Kracauer (1952) advanced various arguments against the view that CTA was strictly a quantitative method, suggesting that very often we wished to assess content as being negative or positive with respect to some political, social, or economic thesis and that such evaluations could never be merely statistical. He further argued that we often wished to study “underlying” messages or latent content of documentation and that in consequence we needed to interpret content as well as count items of content. Morgan (1993) has argued that, given the emphasis that is placed on “coding” in almost all forms of qualitative data analysis, the deployment of counting techniques is essential and that we ought therefore to think in terms of what he calls qualitative as well as quantitative content analysis. Naturally, some of these positions create more problems than they seemingly solve (as is the case with considerations of “latent content”), but given the twentieth-first-century predilection for “mixed-methods” research ( Creswell, 2007 ), it is clear that CTA has a role to play in integrating quantitative and qualitative modes of analysis in a systematic rather than merely an ad hoc and piecemeal fashion. In the sections that follow, I will provide some examples of the ways in which “qualitative” analysis can be combined with systematic modes of counting. First, however, we need to focus on what is analyzed in CTA.

Units of analysis

So what is the unit of analysis in CTA? A brief answer to that question is that analysis can be focused on words, sentences, grammatical structures, tenses, clauses, ratios (of say, nouns to verbs), or even “themes.” Berelson (1952) gives some examples of all of the above and also recommends a form of thematic analysis (c.f., Braun and Clarke, 2006 ) as a viable option. Other possibilities include counting column length (of speeches and newspaper articles), amounts of (advertising) space, or frequency of images. For our purposes, however, it might be useful to consider a specific (and somewhat traditional) example. Here it is. It is an extract from what has turned out to be one of the most important political speeches of the current century.

Iraq continues to flaunt its hostility toward America and to support terror. The Iraqi regime has plotted to develop anthrax and nerve gas and nuclear weapons for over a decade. This is a regime that has already used poison gas to murder thousands of its own citizens, leaving the bodies of mothers huddled over their dead children. This is a regime that agreed to international inspections then kicked out the inspectors. This is a regime that has something to hide from the civilized world. States like these, and their terrorist allies, constitute an axis of evil, arming to threaten the peace of the world. By seeking weapons of mass destruction, these regimes pose a grave and growing danger. They could provide these arms to terrorists, giving them the means to match their hatred. They could attack our allies or attempt to blackmail the United States. In any of these cases, the price of indifference would be catastrophic.” —George W. Bush, State of the Union address, January 29, 2002

A number of possibilities arise for analysing the content of a speech such as the one above. Clearly, words and sentences must play a part in any such analysis, but in addition to words there are structural features of the speech that could also figure. For example, the extract takes the form of a simple narrative—pointing to a past, a present, and an ominous future (catastrophe)—and could therefore be analysed as such. There are, in addition, a number of interesting oppositions in the speech (such as those between “regimes” and the “civilised” world), as well as a set of interconnected present participles such as “plotting,” “hiding,” “arming,” and “threatening” that are associated both with Iraq and with other states that “constitute an axis of evil.” Evidently, simple word counts would fail to capture the intricacies of a speech of this kind. Indeed, our example serves another purpose—to highlight the difficulty that often arises in dissociating content analysis from discourse analysis (of which narrative analysis and the analysis of rhetoric and trope are subspecies). So how might we deal with these problems?

One approach that can be adopted is to focus on what is referenced in text and speech. That is, to concentrate on the characters or elements that are recruited into the text and to examine the ways in which they are connected or co-associated. I shall provide some examples of this form of analysis shortly. Let us merely note for the time being that in the previous example we have a speech in which various “characters”—including weapons in general, specific weapons (such as nerve gas), threats, plots, hatred, evil and mass destruction—play a role. Be aware that we need not be concerned with the veracity of what is being said—whether it is true or false—but simply with what is in the speech and how what is in there is associated. (We may leave the task of assessing truth and falsity to the jurists). Be equally aware that it is a text that is before us and not an insight into the ex-President’s mind, nor his thinking, nor his beliefs, nor any other subjective property that he may have possessed.

In the introductory paragraph, I made brief reference to some ideas of the German philosopher Jűrgen Habermas (1987) . It is not my intention here to expand on the detailed twists and turns of his claims with respect to the role of language in the “lifeworld” at this point. However, I do intend to borrow what I regard as some particularly useful ideas from his work. The first, is his claim—influenced by a strong line of twentieth-century philosophical thinking—that language and culture are constitutive of the lifeworld (1987:125), and in that sense we might say that things (including individuals and societies) are made in language. That of course is a simple justification for focusing on what people say rather than what they “think” or “believe” or “feel” or “mean” (all of which have been suggested at one time or another as points of focus for social inquiry and especially qualitative forms of inquiry). Second, Habermas argues that speakers and therefore hearers (and one might add writers and therefore readers), in what he calls their speech acts, necessarily adopt a pragmatic relation to one of three worlds: entities in the objective world, things in the social world, and elements of a subjective world. In practice, Habermas (1987 :120) suggests all three worlds are implicated in any speech act but that there will be a predominant orientation to one of these. To rephrase this in a crude form, when speakers engage in communication, they refer to things and facts and observations relating to external nature, to aspects of interpersonal relations, and to aspects of private inner subjective worlds (thoughts, feelings, beliefs, etc.). One of the problems with locating CTA in “communication research” has been that the communications referred to are but a special and limited form of action (often what Habermas would call strategic acts). In other words, television, newspaper, video, and internet communications are just particular forms (with particular features) of action in general. Again we might note in passing that the adoption of the Habermassian perspective on speech acts implies that much of qualitative analysis in particular has tended to focus only on one dimension of communicative action—the subjective and private. In this respect, I would argue that it is much better to look at speeches such as George W Bush’s 2002 State of the Union address as an “account” and to examine what has been recruited into the account; and how what has been recruited is connected or co-associated rather than to use the data to form insights into his (or his adviser’s) thoughts, feelings, and beliefs.

In the sections that follow, and with an emphasis on the ideas that I have just expounded, I intend to demonstrate how CTA can be deployed to advantage in almost all forms of inquiry that call upon either interview (or speech-based) data or textual data. In my first example, I will show how CTA can be used to analyze a group of interviews. In the second example, I will show how it can be used to analyze a group of policy documents. In the third, I shall focus on a single interview (a “case”), and in the fourth and final example, I will show how CTA can be used to track the biography of a concept. In each instance, I shall briefly introduce the context of the “problem” on which the research was based, outline the methods of data collection, discuss how the data were analyzed and presented, and underline the ways in which content analysis has sharpened the analytical strategy.

Analyzing a Sample of Interviews: Looking at Concepts and Their Co-Associations in a Semantic Network

My first example of using CTA is based on a research study that was initially undertaken in the early 2000s. It was a project aimed at understanding why older people might reject the offer to be immunized against influenza (at no cost to them). The ultimate objective was to improve rates of immunization in the study area. The first phase of the research was based on interviews with 54 older people in South Wales. The sample included people who had never been immunized, some who had refused immunization, and some who had accepted immunization. Within each category, respondents were randomly selected from primary care physician patient lists, and the data were initially analyzed “thematically” and published accordingly ( Evans, Prout, Prior, et al., 2007 ). A few years later, however, I returned to the same data set to look at a different question—how (older) lay people talked about colds and flu, especially how they distinguished between the two illnesses and how they understood the causes of the two illnesses (see Prior, Evans, & Prout, 2011 ). Fortunately, in the original interview schedule, we had asked people about how they saw the “differences between cold and flu” and what caused flu, so it was possible to reanalyze the data with such questions in mind. In that frame, the example that follows demonstrates not only how CTA might be used on interview data, but also how it might be used to undertake a secondary analysis of a pre-existing data set ( Bryman, 2008 ).

As with all talk about illness, talk about colds and flu is routinely set within a mesh of concerns—about causes, symptoms, and consequences. Such talk comprises the base elements of what has at times been referred to as the “explanatory model” of an illness ( Kleinman, Eisenberg, & Good, 1978 ). In what follows, I shall focus almost entirely on issues of causation as understood from the viewpoint of older people; the analysis is based on the answers that respondents made in response to the question, “How do you think people catch flu?”

Semi-structured interviews of the kind undertaken for a study such as this are widely used and are often characterized as akin to “a conversation with a purpose” ( Kahn & Cannell, 1957 :97). One of the problems of analyzing the consequent data is that, although the interviewer holds to a planned schedule, the respondents often reflect in a somewhat unstructured way about the topic of investigation, so it is not always easy to unravel the web of talk about, say, “causes” that occurs in the interview data. In this example, causal agents of flu, inhibiting agents, and means of transmission were often conflated by the respondents. Nevertheless, in their talk people did answer the questions that were posed, and in the study referred to here, that talk made reference to things such as “bugs” (and “germs”) as well as viruses; but the most commonly referred to causes were “the air” and the “atmosphere.” The interview data also pointed toward means of transmission as “cause”—so coughs and sneezes and mixing in crowds figured in the causal mix. Most interesting perhaps was the fact that lay people made a nascent distinction between facilitating factors (such as bugs and viruses) and inhibiting factors (such as being resistant, immune, or healthy), so that in the presence of the latter, the former are seen to have very little effect. Here are some shorter examples of typical question-response pairs from the original interview data.

(R:32): “How do you catch it [the flu]? Well, I take it its through ingesting and inhaling bugs from the atmosphere. Not from sort of contact or touching things. Sort of airborne bugs. Is that right?” (R:3): “I suppose it’s [the cause of flu] in the air. I think I get more diseases going to the surgery than if I stayed home. Sometimes the waiting room is packed and you’ve got little kids coughing and spluttering and people sneezing, and air conditioning I think is a killer by and large I think air conditioning in lots of these offices”. (R:46): “I think you catch flu from other people. You know in enclosed environments in air conditioning which in my opinion is the biggest cause of transferring diseases is air conditioning. Worse thing that was ever invented that was. I think so, you know. It happens on aircraft exactly the same you know.”

Alternatively, it was clear that for some people being cold, wet, or damp could also serve as a direct cause of flu; thus:

Interviewer: “OK, good. How do you think you catch the flu?” (R:39): “Ah. The 65 dollar question. Well, I would catch it if I was out in the rain and I got soaked through. Then I would get the flu. I mean my neighbour up here was soaked through and he got pneumonia and he died. He was younger than me: well, 70. And he stayed in his wet clothes and that’s fatal. Got pneumonia and died, but like I said, if I get wet, especially if I get my head wet, then I can get a nasty head cold and it could develop into flu later.”

As I suggested earlier, despite the presence of bugs and germs, viruses, the air, and wetness or dampness, “catching” the flu is not a matter of simple exposure to causative agents. Thus some people hypothesized that within each person there is a measure of immunity or resistance or healthiness that comes into play and that is capable of counteracting the effects of external agents. For example, being “hardened” to germs and harsh weather can prevent a person getting colds and flu. Being “healthy” can itself negate the effects of any causative agents, and healthiness is often linked to aspects of “good” nutrition and diet and not smoking cigarettes. These mitigating and inhibiting factors can either mollify the effects of infection or prevent a person “catching” the flu entirely. Thus (R:45) argued that it was almost impossible for him to catch flu or cold “[c]os I got all this resistance.” Interestingly respondents often used possessive pronouns in their discussion of immunity and resistance (“my immunity” and “my resistance”)—and tended to view them as personal assets (or capital) that might be compromised by mixing with crowds.

By implication, having a weak immune system can heighten the risk of contracting cold and flu and might therefore spur one on to take preventive measures such as accepting a flu jab. There are some, of course, who believe that it is the flu jab that can cause the flu and other illnesses. An example of what might be called lay “epidemiology” ( Davison, Davey-Smith, & Frankel, 1991 ) is evident in the following extract.

(R:4): “Well, now it’s coincidental you know that [my brother] died after the jab, but another friend of mine, about 8 years ago, the same happened to her. She had the jab and about six months later, she died, so I know they’re both coincidental, but to me there’s a pattern.”

Normally, results from studies such as this are presented in exactly the same way as has just been set out. Thus the researcher highlights given themes that are said to have emerged out of the data and then provides appropriate extracts from the interviews to illustrate and substantiate the relevant themes. However, one very reasonable question that any critic might ask about the selected data extracts concerns the extent to which they are “representative” of the material in the data set as a whole. Maybe, for example, the author has been unduly selective in his or her use of both themes and quotations. Perhaps, as a consequence, the author has ignored or left out talk that does not fit their arguments or extracts that might be considered dull and uninteresting compared to more exotic material. And these kinds of issues and problems are certainly common to the reporting of almost all forms of qualitative research. However, the adoption of CTA techniques can help to mollify such problems. This is so because by using CTA we can indicate the extent to which we have used all or just some of the data, and we can provide a view of the content of the entire sample of interviews rather than just the content and flavor of merely one or two interviews. In this light, we need to consider Figure 18.1 . The figure is based on counting the number of references in the 54 interviews to the various “causes” of the flu, though references to the flu jab (i.e., inoculation) as a cause of flu have been ignored for the purpose of this discussion). The node sizes reflect the relative importance of each cause as determined by the concept count (frequency of occurrence). The links between nodes reflect the degree to which causes are co-associated in interview talk and are calculated according to a co-occurrence index (see, e.g., SPSS, 2007 :183).

Given this representation, we can immediately assess the relative importance of the different causes as referred to in the interview data. Thus we can see that such things as (poor) “hygiene” and “foreigners” were mentioned as a potential cause of flu—but mention of hygiene and foreigners was nowhere near so important as references to “the air” or to “crowds” or to “coughs and sneezes.” In addition, we can also determine the strength of the connections that interviewees made between one cause and another. Thus there are relatively strong links between “resistance” and “coughs and sneezes,” for example.

In fact, Figure 18.1 divides causes into the “external” and the “internal,” or the facilitating and the impeding (lighter and darker nodes). Among the former I have placed such things as crowds, coughs, sneezes, and the air while among the latter I have included “resistance,” “immunity,” and “health.” That division, of course, is a product of my conceptualizing and interpreting the data, but whichever way we organize the findings, it is evident that talk about the causes of flu belongs in a web or mesh of concerns that would be difficult to represent by the use of individual interview extracts alone. Indeed, it would be impossible to demonstrate how the semantics of causation belong to a culture (rather than to individuals) in any other way. In addition I would argue that the counting involved in the construction of the diagram functions as a kind of check on researcher interpretations and provides a source of visual support for claims that an author might make about, say, the relative importance of “damp” and “air” as perceived causes of disease. Finally, the use of CTA techniques allied with aspects of conceptualization and interpretation has enabled us to approach the interview data as a set and to consider the respondents as belonging to a community rather than regarding them merely as isolated and disconnected individuals, each with their own views. It has also enabled us to squeeze some new findings out of old data, and I would argue that it has done so with advantage. There are of course other advantages to using CTA to explore data sets, which I highlight in the next section.

What causes flu? A lay perspective. Factors listed as causes of colds and flu in 54 interviews. Node size is proportional to number of references “as causes.” Line thickness is proportional to co-occurrence of any two “causes” in the set of interviews.

Analyzing a Sample of Documents: Using Content Analysis to Verify Claims

Policy analysis is a difficult business. For a start, it is never entirely clear where (social, health, economic, environmental) policy actually is. Is it in documents (as published by governments, think tanks, and research centres), in action (what people actually do), or in speech (what people say)? Perhaps it rests in a mixture of all three realms. Yet wherever it may be, it is always possible, at the very least, to identify a range of policy texts and to focus on the conceptual or semantic webs in terms of which government officials and other agents (such as politicians) talk about the relevant policy issues. Furthermore, in so far as policy is recorded—in speeches, pamphlets, and reports—we may begin to speak of specific policies as having a history or a pedigree that unfolds through time (think, e.g., of US or UK health policies during the Clinton years or the Obama years). And in so far as we consider “policy” as having a biography or a history, we can also think of studying policy narratives.

Though firmly based in the world of literary theory, narrative method has been widely used for both the collection and the analysis of data concerning ways in which individuals come to perceive and understand various states of health, ill health, and disability ( Frank, 1995 ; “ Hydén, 1997 ). Narrative techniques have also been adapted for use in clinical contexts and allied to concepts of healing ( Charon, 2006 ). In both social scientific and clinical work, however, the focus is invariably on individuals and on how individuals “tell” stories of health and illness. Yet narratives can also belong to collectives—such as political parties and ethnic and religious groups—just as much as to individuals, and in the latter case there is a need to collect and analyse data that are dispersed across a much wider range of materials than can be obtained from the personal interview. In this context, Roe (1994) has demonstrated how narrative method can be applied to an analysis of national budgets, animal rights, and environmental policies.

An extension of the concept of narrative to policy discourse is undoubtedly useful ( Newman & Vidler, 2006 ), but how might such narratives be analyzed? What strategies can be used to unravel the form and content of a narrative, especially in circumstances where the narrative might be contained in multiple (policy) documents, authored by numerous individuals, and published across a span of time rather than in a single, unified text such as a novel? Roe (1994) , unfortunately, is not in any way specific about analytical procedures apart from offering the useful rule to “never stray too far from the data” (1994:xii). So in this example I will outline a strategy for tackling such complexities. In essence, it is a strategy that combines techniques of linguistically (rule) based content analysis with a theoretical and conceptual frame that enables us to unraveland identify the core features of a policy narrative. My substantive focus is on documents concerning health service delivery policies published 2000–2009 in the constituent countries of the UK (that is, England, Scotland, Wales, and Northern Ireland—all of which have different political administrations).

Narratives can be described and analyzed in various ways, but for our purposes we can say that they have three key features: they point to a chronology, they have a plot and they contain “characters.”

Chronology : All narratives have beginnings; they also have middles and endings, and these three stages are often seen as comprising the fundamental structure of narrative text. Indeed, in his masterly analysis of time and narrative, Ricoeur (1984) argues that it is in the unfolding chronological structure of a narrative that one finds its explanatory (and not merely descriptive) force. By implication, one of the simplest strategies for the examination of policy narratives is to locate and then divide a narrative into its three constituent parts—beginning, middle, and end.

Unfortunately, while it can sometimes be relatively easy to locate or choose a beginning to a narrative, it can be much more difficult to locate an end point. Thus in any illness narrative, a narrator might be quite capable of locating the start of an illness process (in an infection, accident, or other event) but unable to see how events will be resolved in an ongoing and constantly unfolding life. As a consequence, both narrators and researchers usually find themselves in the midst of an emergent present—a present without a known and determinate end (see, e.g., Frank, 1995 ). Similar considerations arise in the study of policy narratives where chronology is perhaps best approached in terms of (past) beginnings, (present) middles, and projected futures.

Plot : According to Ricoeur (1984) , our basic ideas about narrative are best derived from the work and thought of Aristotle who in his Poetics sought to establish “first principles” of composition. For Ricoeur, as for Aristotle, plot ties things together. It “brings together factors as heterogeneous as agents, goals, means, interactions, circumstances, unexpected results” (1984:65) into the narrative frame. For Aristotle, it is the ultimate untying or unraveling of the plot that releases the dramatic energy of the narrative.

Character : Characters are most commonly thought of as individuals, but they can be considered in much broader terms. Thus the French semiotician A. J. Greimas (1970) , for example, suggested that, rather than think of characters as people, it would be better to think in terms of what he called “actants” and of the functions that such actants fulfill within a story. In this sense geography, climate, and capitalism can be considered as characters every bit as much as aggressive wolves and Little Red Riding Hood. Further, he argued that the same character (actant) can be considered to fulfill many functions and the same function performed by many characters. Whatever else, the deployment of the term actant certainly helps us to think in terms of narratives as functioning and creative structures. It also serves to widen our understanding of the ways in which concepts, ideas, and institutions, as well “things” in the material world can influence the direction of unfolding events every bit as much as conscious human subjects. Thus, for example, the “American people,” “the nation,” “the constitution,” “ the West,” “tradition,” and “Washington” can all serve as characters in a policy story.

As I have already suggested, narratives can unfold across many media and in numerous arenas—speech and action, as well as text. Here, however, my focus is solely on official documents—all of which are UK government policy statements as listed in Table 18.1 . The question is how might CTA help us unravel the narrative frame?

It might be argued that a simple reading of any document should familiarize the researcher with elements of all three policy narrative components (plot, chronology, and character). However, in most policy research, we are rarely concerned with a single and unified text as is the case with a novel, but rather with multiple documents written at distinctly different times by multiple (usually anonymous) authors that notionally can range over a wide variety of issues and themes. In the full study, some 19 separate publications were analyzed across England, Wales, Scotland, and Northern Ireland.

Naturally, to list word frequencies—still less to identify co-occurrences and semantic webs in large data sets (covering hundreds of thousand of words and footnotes)—cannot be done manually but rather requires the deployment of complex algorithms and text-mining procedures. To this end I analyzed the 19 documents using “Text Mining for Clementine” ( SPSS, 2007 ).

Text-mining procedures begin by providing an initial list of concepts based on the lexicon of the text but which can be weighted according to word frequency and which take account of elementary word associations. For example, learning disability, mental health, and performance management indicate three concepts, not six words. Using such procedures on the aforementioned documents gives the researcher an initial grip on the most important concepts in the document set of each country. Note that this is much more than a straightforward concordance analysis of the text and is more akin to what Ryan & Bernard (2000) have referred to as “semantic analysis” and Carley (1993) has referred to as “concept” and “mapping” analysis.

So the first task was to identify and then extract the core concepts, thus identifying what might be called “key” characters or actants in each of the policy narratives. For example, in the Scottish documents such actants included “Scotland” and the “Scottish people,” as well as “health” and the “NHS,” among others; while in the Welsh documents it was “the people of Wales” and “Wales” that figured largely—thus emphasizing how national identity can play every bit as important a role in a health policy narrative as concepts such as “health,” “hospitals,” and “wellbeing.”

Having identified key concepts it was then possible to track concept clusters in which particular actants or characters are embedded. Such cluster analysis is dependent on the use of co-occurrence rules and the analysis of synonyms, whereby it is possible to get a grip on the strength of the relationships between the concepts, as well as the frequency with which the concepts appear in the collected texts. In Figure 18.2 , I provide an example of a concept cluster. The diagram indicates the nature of the conceptual and semantic web in which various actants are discussed. The diagrams further indicate strong (solid line) and weaker (dotted line) connections between the various elements in any specific mix, and the numbers indicate frequency counts for the individual concepts. Using Clementine , the researcher is unable to specify in advance which clusters will emerge from the data. One cannot, for example, choose to have an NHS cluster. In that respect, these diagrams not only provide an array in terms of which concepts are located, but also serve as a check on and to some extent validation of the interpretations of the researcher. Of course none of this tells us what the various narratives contained within the documents might be. They merely point to key characters and relationships both within and between the different narratives. So having indicated the techniques used to identify the essential parts of the four policy narratives, it is now time to sketch out their substantive form.

It may be useful to note that Aristotle recommended brevity in matters of narrative —deftly summarising the whole of the Odyssey in just seven lines. In what follows, I attempt—albeit somewhat weakly—to emulate that example by summarising a key narrative of English health services policy in just four paragraphs. The citations are of Department of Health publications (by year) as listed in Table 18.1 . Note how the narrative unfolds in relation to the dates of publication. In the English case (though not so much in the other UK countries), it is a narrative that is concerned to introduce market forces into what is and has been a state-managed health service. Market forces are justified in terms of improving opportunities for the consumer (i.e., the patients in the service), and the pivot of the newly envisaged system is something called “patient choice” or “choice.” This is how the story unfolds as told through the policy documents between 2000–2008 (see Table 18.1 ).

The advent of the NHS in 1948 was a “seminal event” (2000:8), but under successive Conservative administrations the NHS was seriously underfunded (2006:3). The (New Labour) government will invest (2000) or already has (2003:4) invested extensively in infrastructure and staff, and the NHS is now on a “journey of major improvement” (2004:2). But “more money is only a starting point” (2000:2), and the journey is far from finished. Continuation requires some fundamental changes of “culture” (2003:6). In particular, the NHS remains unresponsive to patient need, and “[a]ll too often, the individual needs and wishes are secondary to the convenience of the services that are available. This ‘one size fits all’ approach is neither responsive, equitable nor person-centred” (2003:17). In short, the NHS is a 1940s system operating in a twenty-first-century world (2000:26). Change is therefore needed across the “whole system” (2005:3) of care and treatment.

Above all, we have to recognize that we “live in a consumer age” (2000:26). People’s expectations have changed dramatically (2006:129), and people want more choice, more independence, and more control (2003:12) over their affairs. Patients are no longer, and should not be considered as, “passive recipients” of care (2003:62), but wish to be and should be (2006:81) actively “involved” in their treatments (2003:38, 2005:18)—indeed, engaged in a partnership (2003:22) of respect with their clinicians. Furthermore, most people want a personalized service “tailor made to their individual needs” (2000:17, 2003:15, 2004:1, 2006:83)—“[a] service which feels personal to each and every individual within a framework of equity and good use of public money” (2003:6).

To advance the necessary changes, “patient choice” needs to be and “will be strengthened” (2000:89). “Choice” must be made to “happen” (2003), and it must be “real” (2003:3, 2004:5, 2005:20, 2006:4). Indeed, it must be “underpinned” (2003:7) and “widened and deepened” (2003:6) throughout the entire system of care.

If “we” expand and underpin patient choice in appropriate ways and engage patients in their treatment systems, then levels of patient satisfaction will increase (2003:39), and their choices will lead to a more “efficient” (2003:5, 2004:2, 2006:16) and effective (2003:62, 2005:8) use of resources. Above all, the promotion of choice will help to drive up “standards” of care and treatment (2000:4, 2003:12, 2004:3, 2005:7, 2006:3). Furthermore, the expansion of choice will serve to negate the effects of the “inverse care law,” whereby those who need services most tend to get catered for the least (2000:107, 2003:5, 2006:63), and it will thereby help in moderating the extent of health inequalities in the society in which we live. “The overall aim of all our reforms,” therefore, “is to turn the NHS from a top down monolith into a responsive service that gives the patient the best possible experience. We need to develop an NHS that is both fair to all of us, and personal to each of us” (2003:5).

Concept cluster for “care” in six English policy documents, 2000–2007. Line thickness is proportional to the strength co-occurrence co-efficient. Node size reflects relative frequency of concept, and (numbers) refer to the frequency of concept. Solid lines indicate relationships between terms within the same cluster, and dotted lines indicate relationships between terms in different clusters.

We can see how most—though not all—of the elements of this story are represented in Figure 18.2 . In particular we can see strong (co-occurrence) links between “care” and “choice” and how partnership, performance, control, and improvement have a prominent profile. There are of course some elements of the web that have a strong profile (in terms of node size and links) but to which we have not referred; access, information, primary care, and waiting times are four. As anyone well versed in English health care policy would know, these have important roles to play in the wider, consumer-driven narrative. However, by rendering the excluded as well as included elements of that wider narrative visible, the concept web provides a degree of verification on the content of the policy story as told herein and on the scope of its “coverage.”

In following through on this example, we have of course moved from content analysis to a form of discourse analysis (in this instance narrative analysis). That shift underlines aspects of both the versatility of CTA and some of its weaknesses—versatility in the sense that CTA can be readily combined with other methods of analysis and in the way in which the results of the CTA help us to check and verify the claims of the researcher. The weakness of the diagram compared to the narrative is that CTA on its own is a somewhat one-dimensional and static form of analysis, and while it is possible to introduce time and chronology into the diagrams, the diagrams themselves remain lifeless in the absence of some form of discursive overview. (For a fuller analysis of these data see, Prior, Hughes, & Peckham, 2012 ).

Analyzing a Single Interview: The Role of Content Analysis in a Case Study

So far I have focused on using content analysis on a sample of interviews and on a sample of documents. In the first instance, I recommended CTA for its capacity to tell us something about what is seemingly central to interviewees and for demonstrating how what is said is linked (in terms of a concept network). In the second instance, I reaffirmed the virtues of co-occurrence and network relations, but this time in the context of a form of discourse analysis. I also suggested that CTA can serve an important role in the process of verification of a narrative and its academic interpretation. In this section, however, I am going to link the use of CTA to another style of research—case study—to show how CTA might be used to analyze a single “case.”

Case study is a term used in multiple and often ambiguous ways. However, Gerring (2004 :342) defines it as “an intensive study of a single unit for the purpose of understanding a larger class of (similar) units.” As Gerring points out, case study does not necessarily imply a focus on N = 1, although that is indeed the most logical number for case study research ( Ragin & Becker, 1992 ). Naturally, an N of 1 can be immensely informative, and whether we like it or not we often have only one N to study (think, e.g., of the 1986 Challenger shuttle disaster, or of the 9/11 attack on the World Trade Center). In the clinical sciences, of course, case studies are widely used to represent the “typical” features of a wider class of phenomena, and often used to define a kind or syndrome (as is in the field of clinical genetics). Indeed, at the risk of mouthing a tautology, one can say that the distinctive feature of case study is its focus on a case in all of its complexity—rather than on individual variables and their inter-relationships, which tends to be a point of focus for large N research.

There was a time when case study was central to the science of psychology. Breuer and Freud’s (2001) famous studies of “hysteria” (orig. 1895) provide an early and outstanding example of the genre in this respect, but as with many of the other styles of social science research, the influence of case studies waned with the rise of much more powerful investigative techniques—including experimental methods—driven by the deployment of new statistical technologies. Ideographic studies consequently gave way to the current fashion for statistically driven forms of analysis that focus on causes and cross-sectional associations between variables rather than ideographic complexity.

In the example that follows, we will look at the consequences of a traumatic brain injury (TBI) on just one individual. The analysis is based on an interview with a person suffering from such an injury, and it was one of 32 interviews carried out with people who had experienced a TBI. The objective of the original research was to develop an outcome measure for TBI that was sensitive to the sufferer’s (rather than the health professional’s) point of view. In our original study (see Morris, Prior, Deb et al., 2005 ), interviews were also undertaken with 27 carers of the injured with the intention of comparing their perceptions of TBI to those of the people for which they cared. A sample survey was also undertaken to elicit views about TBI from a much wider population of patients than was studied via interview.

In the introduction, I referred to Habermas and the concept of the “lifeworld.” Lifeworld ( Lebenswelt ) is a concept that first arose out of twentieth-century German philosophy. It constituted a specific focus for the work of Alfred Schutz (see, e.g., Schutz and Luckman, 1974 ). Schutz described the lifeworld as “that province of reality which the wide-awake and normal adult simply takes-for-granted in an attitude of common sense” (1974:3). Indeed, it was the routine and taken-for-granted quality of such a world that fascinated Schutz. As applied to the worlds of those with head injuries, the concept has particular resonance because head injuries often result in that taken-for-granted quality being disrupted and fragmented, ending in what Russian neuropsychologist A.R. Luria once described as “shattered” worlds ( Luria, 1975 ). As well as providing another excellent example of a case study, Luria’s work is also pertinent because he sometimes argued for a “romantic science” of brain injury—that is, a science that sought to grasp the world view of the injured patient by paying attention to an unfolding and detailed personal “story” of the head injured as well as to the neurological changes and deficits associated with the injury itself. In what follows, I shall attempt to demonstrate how CTA might be used to underpin such an approach.

In the original research, we began analysis by a straightforward reading of the interview transcripts. Unfortunately, a simple reading of a text or an interview can, strangely, mislead the reader into thinking that some issues or themes are actually more important than is warranted by the actual contents of the text. How that comes about is not always clear, but it probably has something to do with a desire to develop “findings” and our natural capacity to overlook the familiar in favor of the unusual. For that reason alone, it is always useful to subject any text to some kind of concordance analysis—that is, generating a simple frequency list of words used in an interview or text. Given the current state of technology, one might even speak these days of using text-mining procedures such as the aforementioned Clementine to undertake such a task. By using Clementine, and as we have seen, it is also possible to measure the strength of co-occurrence links between elements (i.e., words and concepts) in the entire data set (in this example, 32 interviews), though for a single interview these aims can just as easily be achieved using much simpler, low-tech strategies.

By putting all 32 interviews into the database, a number of common themes emerged. For example, it was clear that “time” entered into the semantic web in a prominent manner, and it was clearly linked to such things as “change,” “injury,” “the body,” and what can only be called the “I was.” Indeed, time runs through the 32 stories in many guises, and the centrality of time is of course a reflection of storytelling and narrative recounting in general—chronology, as we have noted, being a defining feature of all story telling ( Ricoeur, 1984 ). Thus sufferers recounted both the events surrounding their injury and provided accounts as to how the injuries affected their present life and future hopes. As to time present, much of the patient story circled around activities of daily living—walking, working, talking, looking, feeling, remembering, and so forth.

Understandably, the word and the concept of “injury” featured largely in the interviews, though it was a word most commonly associated with discussions of physical consequences of injury. There were many references in that respect to injured arms, legs, hands, and eyes. There were also references to “mind”—though with far lesser frequency than with references to the body and to body parts. Perhaps none of this is surprising. However, one of the most frequent concepts in the semantic mix was the “I was” (716 references). The statement “I was,” or “I used to” was in turn strongly connected to terms such as “the accident” and “change.” Interestingly, the “I was” overwhelmingly eclipsed the “I am” in the interview data (the latter with just 63 references). This focus on the “I was” appears in many guises. For example, it is often associated with the use of the passive voice: “I was struck by a car;” “I was put on the toilet;” “I was shipped from there then, transferred to [Cityville];” “I got told that I would never be able...;” “I was sat in a room,” and so forth. In short, the “I was” is often associated with things, people, and events acting upon the injured person. More importantly, however, the appearance of the “I was” is often used to preface statements signifying a state of loss or change in the person’s course of life—that is, as an indicator for talk about the patient’s shattered world. For example, Patient 7122 stated, “The main (effect) at the moment is I’m not actually with my children, I can’t really be their mum at the moment. I was a caring Mum, but I can’t sort of do the things that I want to be able to do like take them to school. I can’t really do a lot on my own. Like crossing the roads.”

Another patient stated, “Everything is completely changed. The way I was... I can’t really do anything at the moment. I mean my German, my English, everything’s gone. Job possibilities is out the window. Everything is just out of the window... I just think about it all the time actually every day you know. You know it has destroyed me anyway, but if I really think about what has happened I would just destroy myself.”

Each of these quotations in its own way serves to emphasize how life has changed and how the patient’s world has changed. In that respect, we can say that one of the major outcomes arising from TBI may be substantial “biographical disruption” ( Bury, 1982 ), whereupon key features of an individual’s life course are radically altered forever. Indeed, as Becker (1997 :37) argues in relation to a wide array of life events, “When their health is suddenly disrupted, people are thrown into chaos. Illness challenges one’s knowledge of one’s body. It defies orderliness. People experience the time before their illness and its aftermath as two separate entities.” Indeed, this notion of a cusp in personal biography is particularly well illustrated by Luria’s patient Zasetsky; the latter often refers to being a “newborn creature” ( Luria, 1975 :24, 88), a shadow of a former self (1975;25), and as having his past “wiped out” (1975: 116).

However, none of this tells us about how these factors come together in the life and experience of one individual. When we focus on an entire set of interviews, we necessarily lose the rich detail of personal experience and tend instead to rely on a conceptual rather than a graphic description of effects and consequences (to focus on, say, “memory loss,” rather than loss of memory about family life). The contents of Figure 18.3 attempt to correct that vision. It records all of the things that a particular respondent (Patient 7011 )used to do and liked doing. It records all of the things that he says that can no longer do (at one year after injury), and it records all of the consequences that he suffered from his head injury at the time of interview. Thus we see references to epilepsy (his “fits”), paranoia (the patient spoke of his suspicions concerning other people, people scheming behind his back, and his inability to trust others), deafness, depression, and so forth. Note that, although I have inserted a future tense into the web (“I will”), such a statement never appeared in the transcript. I have set it there for emphasis and to show how for this person the future fails to connect to any of the other features of his world except in a negative way. Thus he states at one point that he cannot think of the future because it makes him feel depressed (see Fig. 18.3). The line thickness of the arcs reflect the emphasis that the subject placed on the relevant “outcomes” in relation to the “I was” and the “now” during the interview. Thus we see that factors affecting his concentration and balance loom large but that he is also concerned about his being dependent on others, his epileptic fits, and his being unable to work and drive a vehicle. The schism in his life between what he used to do, what cannot now do, and his current state of being is nicely represented in the CTA diagram.

What have we gained from executing this kind of analysis? For a start, we have moved away from a focus on variables, frequencies, and causal connections (e.g., a focus on the proportion of people with TBI who suffer from memory problems or memory problems and speech problems) and refocused on how the multiple consequences of a TBI link together in one person. In short, instead of developing a narrative of acting variables, we have emphasized a narrative of an acting individual ( Abbott, 1992 :62). Second, it has enabled us to see how the consequences of a TBI connect to an actual lifeworld (and not simply an injured body). So the patient is not viewed just as having a series of discrete problems such as balancing, or staying awake, which is the usual way of assessing outcomes, but is seen as someone struggling to come to terms with an objective world of changed things, people, and activities (missing work is not, for example, routinely considered an “outcome” of head injury). Third, by focusing on what the patient was saying, we gain insight into something that is simply not visible by concentrating on single outcomes or symptoms alone—namely, the void that rests at the center of the interview, what I have called the “I was.” Fourth, we have contributed to understanding a type, for the case that we have read about is not simply a case of “John” or “Jane” but a case of TBI, and in that respect it can add to many other accounts of what it is like to experience head injury—including one of the most well documented of all TBI cases, that of Zatetsky. Finally, we have opened up the possibility of developing and comparing cognitive maps ( Carley, 1993 ) for different individuals, and thereby gained insight into how alternative cognitive frames of the world arise and operate.

The shattered world of patient 7011. Thickness of lines (arcs) are proportional to the frequency of reference to the “outcome” by the patient during interview.

Tracing the biography of a concept

In the previous sections, I emphasised the virtues of CTA for its capacity to link into a data set in its entirety—and how the use of CTA can counter any tendency of a researcher to be selective and partial in the presentation and interpretation of information contained in interviews and documents. However, that does not mean that we always have to take an entire document or interview as the data source. Indeed, it is possible to select (on rational and explicit grounds) sections of documentation and to conduct the CTA on the chosen portions. In the example that follows, I do just that. The sections that I chose to concentrate on are titles and abstracts of academic papers—rather than the full texts. The research on which the following is based is concerned with a biography of a concept and is being conducted in conjunction with a PhD student of mine, Joanne Wilson. Joanne thinks of this component of the study more in terms of a “scoping study” than of a biographical study, and that too is a useful framework for structuring the context in which CTA can be used. Scoping studies ( Arksey & O’Malley, 2005 ) are increasingly used in health related research to “map the field” and to get a sense of the range of work that has been conducted on a given topic. Such studies can also be used to refine research questions and research designs. In our investigation the scoping study was centred on the concept of “well-being.” During the past decade or so, “well-being” has emerged as an important research target for governments and corporations as well as for academics, yet it is far from clear to what the term refers. Given the ambiguity of meaning, it is clear that a scoping review, rather than either a systematic review or a narrative review of available literature, would be best suited to our goals.

The origins of the concept of well-being can be traced at least as far back as the fourth century B.C., when philosophers produced normative explanations of the good life (e.g., eudaimonia, hedonia, and harmony). However, contemporary interest in the concept seemed to have been regenerated by the concerns of economists and most recently psychologists. These days governments are equally concerned with measuring well-being to inform policy and conduct surveys of well-being to assess that state of the nation (see, e.g., Office for National Statistics [ONS], 2012 )—but what are they assessing?

We adopted a two-step process to address the research question, “What is the meaning of ‘well-being’ in the context of public policy?” First, we explored the existing thesauri of eight databases to establish those higher-order headings (if any) under which articles with relevance to well-being might be catalogued. Thus we searched the following databases: Cumulative Index of Nursing and Allied Health Literature [CINAHL], EconLit, Health Management Information Consortium [HMIC], MEDLINE, Philosopher’s Index, PsycINFO, Sociological Abstracts, and Worldwide Political Science Abstracts (WPSA). Each of these databases adopts keyword-controlled vocabularies. In other words, they use inbuilt statistical procedures to link core terms to a set lexis of phrases that depict the concepts contained in the database. Table 18.2 shows each database and its associated taxonomy. The contents of the table point toward a linguistic infrastructure in terms of which academic discourse is conducted, and our task was to extract from this infrastructure the semantic web wherein the concept of “well-being” is situated. We limited the thesaurus terms to “well-being” and its variants (i.e., wellbeing or well being). If the term was returned, it was then exploded to identify any associated terms.

CINAHL = Cumulative Index of Nursing and Allied Health Literature; HMIC = Health Management Information Consortium; WPSA = Worldwide Political Science Abstracts.

To develop the conceptual map, we conducted a free-text search for well-being and its variants within the context of public policy across the same databases. We orchestrated these searches across five separate timeframes: January 1990 to December 1994, January 1995 to December 1999, January 2000 to December 2004, January 2005 to December 2009, and January 2010 to October 2011. Naturally, different disciplines use different words to refer to well-being, each of which may wax and wane in usage over time. The searches thus sought to quantitatively capture any changes in the use and subsequent prevalence of well-being and any referenced terms (i.e., to trace a biography).

It is important to note that we did not intend to provide an exhaustive, systematic search of all the relevant literature. Rather we wanted to establish the prevalence of well-being and any referenced (i.e., allied) terms within the context of public policy. This has the advantage of ensuring that any identified words are grounded in the literature (i.e., they represent words actually used by researchers to talk and write about well-being in policy settings). The searches were limited to abstracts to increase specificity, albeit at some expense to sensitivity, with which we could identify relevant articles.

We also employed inclusion/exclusion criteria to facilitate the process by which we selected articles, thereby minimizing any potential bias arising from our subjective interpretations. We included independent, standalone investigations relevant to the study’s objectives (i.e., concerned with well-being in the context of public policy), which focused on well-being as a central outcome or process and which made explicit reference to “well-being” and “public policy” in either the title or the abstract. We excluded articles that were irrelevant to the study’s objectives, used noun adjuncts to focus on the well-being of specific populations (i.e., children, elderly, women) and contexts (e.g., retirement village), or that focused on deprivation or poverty unless poverty indices were used to understand well-being as opposed to social exclusion. We also excluded book reviews and abstracts describing a compendium of studies.

Using these criteria, Joanne Wilson conducted the review and recorded the results on a template developed specifically for the project, organized chronologically across each database and timeframe. Results were scrutinized by two other colleagues to ensure the validity of the search strategy and the findings. Any concerns regarding the eligibility of studies for inclusion were discussed amongst the research team. I then analyzed the co-occurrence of the key terms in the database. The resultant conceptual map is shown in Figure 18.4 .

The diagram can be interpreted as a visualization of a conceptual space. So when academics write about “well-being” in the context of public policy, they tend to connect the discussion to the other terms in the matrix. “Happiness,” “health,” “economic,” and “subjective,” for example, are relatively dominant terms in the matrix. The node size of these words suggest that references to such entities is only slightly less than reference to well-being itself. However, when we come to analyse how well-being is talked about in detail, we see specific connections come to the fore. Thus the data imply that talk of “subjective well-being” far outweighs discussion of “social well-being,” or “economic well-being.” Happiness tends to act as an independent node (there is only one occurrence of happiness and well-being), probably suggesting that “happiness” is acting as a synonym for wellbeing. Quality of life (QoL) is poorly represented in the abstracts, and its connection to most of the other concepts in the space is very weak—confirming, perhaps, that QoL is unrelated to contemporary discussions of well-being and happiness. The existence of “measures” points to a distinct concern to assess and to quantify expressions of happiness, well-being, economic growth, and gross domestic product. More important and underlying this detail, there are grounds for suggesting that there are in fact a number of tensions in the literature on well-being.

On one hand, the results point toward an understanding of well-being as a property of individuals—as something that they feel or experience. Such a discourse is reflected through the use of words like “happiness,” “subjective,” and “individual.” This individualistic and subjective frame has grown in influence over the past decade in particular, and one of the problems with it is that it tends toward a somewhat content-free conceptualisation of well-being. To feel a sense of well-being one merely states that one is in a state of well-being; to be happy, one merely proclaims that one is happy (cf. ONS, 2012 ). It is reminiscent of the conditions portrayed in Aldous Huxley’s Brave New World , wherein the rulers of a closely managed society gave their priority to maintaining order and ensuring the happiness of the greatest number—in the absence of attention to justice or freedom of thought or any sense of duty and obligation to others, many of whom were systematically bred in “the hatchery” as slaves.

The position of a concept in a network—a study of “wellbeing.” Node size is proportional to the frequency of terms in 54 selected abstracts. Line thickness is proportional to the co-occurrence of two terms in any phrase of three words (e.g., subjective well-being, economics of well-being, well-being and development).

On the other hand, there is some intimation in our web that the notion of well-being cannot be captured entirely by reference to individuals alone and that there are other dimensions to the concept—that well-being is the outcome or product of, say, access to reasonable incomes, to safe environments, to “development,” and to health and welfare. It is a vision hinted at by the inclusion of those very terms in the network. These different concepts necessarily give rise to important differences concerning how well-being is identified and measured and therefore what policies are most likely to advance well-being. In the first kind of conceptualization, we might improve well-being merely by dispensing what Huxley referred to as “soma” (a super drug that ensured feelings of happiness and elation); in the other case, however, we would need to invest in economic, human, and social capital as the infrastructure for well-being. In any event and even at this nascent level, we can see how content analysis can begin to tease out conceptual complexities and theoretical positions in what is otherwise routine textual data.

Putting the Content of Documents in Their Place

I suggested in my introduction that CTA was a method of analysis—not a method of data collection nor a form of research design. As such, it does not necessarily inveigle us into any specific forms of either design or of data collection, though designs and methods that rely on quantification are dominant. In this closing section, however, I want to raise the issue as to how we should position a study of content in our research strategies as a whole. For we need to keep in mind that documents and records always exist in a context, and that while what is “in” the document may be considered central, a good research plan can often encompass a variety of ways of looking at how content links to context. Hence in what follows I intend to outline how an analysis of content might be combined with other ways of looking at a record or text, and even how the analysis of content might even be positioned as secondary to an examination of a document or record. The discussion calls upon a much broader analysis as presented in Prior (2011) .

I have already stated that basic forms of CTA can serve as an important point of departure for many different types of data analysis—for example, as discourse analysis. Naturally, whenever “discourse” is invoked, there is at least some recognition of the notion that words might actually play a part in structuring the world rather than merely reporting on it or describing it (as is the case with the 2002 State of the Nation address that was quoted in Section “Units of Analysis”). Thus, for example, there is a considerable tradition within social studies of science and technology for examining the place of scientific rhetoric in structuring notions of “nature” and the position of human beings (especially as scientists) within nature (see, e.g., work by Bazerman, 1988 ); Gilbert & Mulkay, 1984 ; and Kay, 2000 ). Nevertheless, little if any of that scholarship situates documents as anything other than as inert objects, either constructed by or waiting patiently to be activated by scientists.

However, in the tradition of the ethnomethodologists ( Heritage, 1991 ) and some adherents of discourse analysis, it is also possible to argue that documents might be more fruitfully approached as a “topic” ( Zimmerman and Pollner; 1971 ) rather than a “resource” (to be scanned for content), in which case the focus would be on the ways in which any given document came to assume its present content and structure. In the field of documentation, these latter approaches are akin to what Foucault (1970) might have called an “archaeology of documentation” and are well represented in studies of such things as how crime, suicide, and other statistics and associated official reports and policy documents are routinely generated. That too is a legitimate point of research focus, and it can often be worth examining the genesis of, say, suicide statistics or statistics about the prevalence of mental disorder in a community as well as using such statistics as a basis for statistical modeling.

Unfortunately, the distinction between topic and resource is not always easy to maintain—especially in the hurly-burly of doing empirical research (see, e.g., Prior, 2003 ). Putting an emphasis on “topic,” however, can open up a further dimension of research, and that concerns the ways in which documents function in the everyday world. And as I have already hinted, when we focus on function, it becomes apparent that documents serve not merely as containers of content but very often as active agents in episodes of interaction and schemes of social organization. In this vein, one can begin to think of an ethnography of documentation. Therein, the key research questions revolve around the ways in which documents are used and integrated into specific kinds of organizational settings, as well as with how documents are exchanged and how they circulate within such settings. Clearly, documents carry content—words, images, plans, ideas, patterns, and so forth—but the manner in which such material is actually called upon and manipulated, and the way in which it functions, cannot be determined (though it may be constrained) by an analysis of content. Thus, Harper’s (1998) study of the use of economic reports inside the International Monetary Fund provides various examples of how “reports” can function to both differentiate and cohere work groups. In the same way. Henderson (1995) illustrates how engineering sketches and drawings can serve as what she calls conscription devices on the workshop floor.

Of course, documents constitute a form of what Latour (1986) would refer to as “immutable mobiles,” and with an eye on the mobility of documents, it is worth noting an emerging interest in histories of knowledge that seek to examine how the same documents have been received and absorbed quite differently by different cultural networks (see, e.g., Burke, 2000 ). A parallel concern has arisen with regard to the newly emergent “geographies of knowledge” (see, e.g., Livingstone, 2005 ). In the history of science, there has also been an expressed interest in the biography of scientific objects ( Latour, 1987 :262) or of “epistemic things” ( Rheinberger, 2000 )—tracing the history of objects independent of the “inventors” and “discoverers” to which such objects are conventionally attached. It is an approach that could be easily extended to the study of documents and is partly reflected in the earlier discussion concerning the meaning of the concept of well-being. Note how in all of these cases a key consideration is how words and documents as “things” circulate and translate from one culture to another; issues of content are secondary.

Clearly, studying how documents are used and how they circulate can constitute an important area of research in its own right. Yet even those who focus on document use can be overly anthropocentric and subsequently overemphasize the potency of human action in relation to written text. In that light, it is interesting to consider ways in which we might reverse that emphasis and instead to study the potency of text and the manner in which documents can influence organizational activities as well as reflect them. Thus Dorothy Winsor (1999) has, for example, examined the ways in which work orders drafted by engineers not only shape and fashion the practices and activities of engineering technicians but construct “two different worlds” on the workshop floor.

In light of this, I will suggest a typology (Table 18.3 ) of the ways in which documents have come to be and can be considered in social research.

While accepting that no form of categorical classification can capture the inherent fluidity of the world, its actors, and its objects, Table 18.3 aims to offer some understanding of the various ways in which documents have been dealt with by social researchers. Thus approaches that fit into cell 1 have been dominant in the history of social science generally. Therein documents (especially as text) have been analyzed and coded for what they contain in the way of descriptions, reports, images, representations, and accounts. In short, they have been scoured for evidence. Data-analysis strategies concentrate almost entirely on what is in the “text” (via various forms of content analysis). This emphasis on content is carried over into cell 2 type approaches with the key differences that analysis is concerned with how document content comes into being. The attention here is usually on the conceptual architecture and socio-technical procedures by means of which written reports, descriptions, statistical data, and so forth are generated. Various kinds of discourse analysis have been used to unravel the conceptual issues, while a focus on socio-technical and rule-based procedures by means of which clinical, police, social work, and other forms of records and reports are constructed has been well represented in the work of ethnomethodologists ( see Prior, 2011 ). In contrast, and in cell 3, the research focus is on the ways in which documents are called upon as a resource by various and different kinds of “user.” Here concerns with document content or how a document has come into being are marginal, and the analysis concentrates on the relationship between specific documents and their use or recruitment by identifiable human actors for purposeful ends. I have already pointed to some studies of the latter kind in earlier paragraphs (e.g., Henderson, 1995 ). Finally, the approaches that fit into cell 4 also position content as secondary. The emphasis here is on how documents as “things” function in schemes of social activity and with how such things can drive, rather than be driven by, human actors. In short, the spotlight is on the vita activa of documentation, and I have provided numerous example of documents as actors in other publications (see Prior, 2003 ; 2008 ; 2011 ).

Content analysis was a method originally developed to analyze mass media “messages” in an age of radio and newspaper print, and well before the digital age. Unfortunately, it struggles to break free of its origins and continues to be associated with the quantitative analysis of “communication.” Yet as I have argued, there is no rational reason why its use has to be restricted to such a narrow field, for it can be used to analyze printed text and interview data (as well as other forms of inscription) in various settings. What it cannot overcome is the fact that it is a method of analysis and not a method of data collection. However, as I have shown, it is an analytical strategy that can be integrated into a variety of research designs and approaches—cross-sectional and longitudinal survey designs, ethnography and other forms of qualitative design, and secondary analysis of pre-existing data sets. Even as a method of analysis it is flexible and can be used either independent of other methods or in conjunction with them. As we have seen, it is easily merged with various forms of discourse analysis and can be used as an exploratory method or as a means of verification. Above all, perhaps, it crosses the divide between “quantitative” and “qualitative” modes of inquiry in social research and offers a new dimension to the meaning of mixed-methods research. I recommend it.

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Community College Psychology Students’ Cooperative Learning Experiences----A Qualitative Analysis By Year In College

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The goal of the study was to assess the effects of year in college on students’ perceptions of the cooperative learning process. Ninety-six college students completed 5 open-ended questions that asked students about their preferences for cooperative learning activities. Forty-nine first-year students and 47 second-year students participated in the study. A qualitative research design was used. Qualitative analyses compared---by year in college---the 5 open-ended questions. The principal investigator qualitatively analyzed the data for themes and subthemes, high frequency responses, and percentage of response. Some tentative qualitative findings were that first- and second-year students preferred the same types of group work and both groups had overlapping ideas on ways to make group work more enjoyable.

Keywords : cooperative learning, college, year in college

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This paper is in the following e-collection/theme issue:

Published on 29.3.2024 in Vol 26 (2024)

#ProtectOurElders: Analysis of Tweets About Older Asian Americans and Anti-Asian Sentiments During the COVID-19 Pandemic

Authors of this article:

Original Paper

Reuben Ng 1, 2 , PhD ;
Nicole Indran 1 , BSocSci (Hons)

1 Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore

2 Lloyd's Register Foundation Institute for the Public Understanding of Risk, National University of Singapore, Singapore, Singapore

Corresponding Author:

Reuben Ng, PhD

Lee Kuan Yew School of Public Policy

National University of Singapore

469C Bukit Timah Road

Singapore, 259772

Phone: 65 66013967

Email: [email protected]

Background: A silver lining to the COVID-19 pandemic is that it cast a spotlight on a long-underserved group. The barrage of attacks against older Asian Americans during the crisis galvanized society into assisting them in various ways. On Twitter, now known as X, support for them coalesced around the hashtag #ProtectOurElders. To date, discourse surrounding older Asian Americans has escaped the attention of gerontologists—a gap we seek to fill. Our study serves as a reflection of the level of support that has been extended to older Asian Americans, even as it provides timely insights that will ultimately advance equity for them.

Objective: This study explores the kinds of discourse surrounding older Asian Americans during the COVID-19 crisis, specifically in relation to the surge in anti-Asian sentiments. The following questions guide this study: What types of discourse have emerged in relation to older adults in the Asian American community and the need to support them? How do age and race interact to shape these discourses? What are the implications of these discourses for older Asian Americans?

Methods: We retrieved tweets (N=6099) through 2 search queries. For the first query, we collated tweets with the hashtag #ProtectOurElders. For the second query, we collected tweets with an age-based term, for example, “elderly” or “old(er) adults(s)” and either the hashtag #StopAAPIHate or #StopAsianHate. Tweets were posted from January 1, 2020, to August 1, 2023. After applying the exclusion criteria, the final data set contained 994 tweets. Inductive and deductive approaches informed our qualitative content analysis.

Results: A total of 4 themes emerged, with 50.1% (498/994) of posts framing older Asian Americans as “vulnerable and in need of protection” (theme 1). Tweets in this theme either singled them out as a group in need of protection because of their vulnerable status or discussed initiatives aimed at safeguarding their well-being. Posts in theme 2 (309/994, 31%) positioned them as “heroic and resilient.” Relevant tweets celebrated older Asian Americans for displaying tremendous strength in the face of attack or described them as individuals not to be trifled with. Tweets in theme 3 (102/994, 10.2%) depicted them as “immigrants who have made selfless contributions and sacrifices.” Posts in this section referenced the immense sacrifices made by older Asian Americans as they migrated to the United States, as well as the systemic barriers they had to overcome. Posts in theme 4 (85/994, 8.5%) venerated older Asian Americans as “worthy of honor.”

Conclusions: The COVID-19 crisis had the unintended effect of garnering greater support for older Asian Americans. It is consequential that support be extended to this group not so much by virtue of their perceived vulnerability but more so in view of their boundless contributions and sacrifices.

Introduction

Not unlike other public health crises, the COVID-19 pandemic brought with it a disconcerting onslaught of racism and xenophobia [ 1 ]. The number of anti-Asian hate crimes in the United States quadrupled in 2021, escalating from the already significant uptick it experienced in 2020, when the COVID-19 outbreak was declared a global pandemic [ 2 ]. In the Asian American and Pacific Islanders (AAPI) community, those aged 60 years or older accounted for 7.3% of the 2808 self-reported incidents in 2020 [ 3 ]. Though not a particularly large figure, underreporting in this community is fairly common [ 4 ]. Moreover, older adults have reported being physically assaulted and having to deal with civil rights violations more than the general AAPI community [ 3 ]. When the crisis first emerged, older Asian Americans were beleaguered by increased economic insecurity [ 5 ] and poorer health outcomes [ 6 ] due to a confluence of structural inequities [ 5 ].

A silver lining to the COVID-19 pandemic is that it cast a spotlight on a long-underserved group. The barrage of attacks against older Asian Americans galvanized both individuals and organizations into assisting them in various ways, such as by distributing safety whistles and meal vouchers [ 7 ]. On Twitter, now known as X, support for them coalesced around the hashtag #ProtectOurElders [ 4 ]. The objective of this study is to explore the kinds of discourse surrounding older Asian Americans during the COVID-19 crisis, specifically in relation to the surge in anti-Asian sentiments.

Dating back to the nineteenth century, one of the most pervasive stereotypes of Asian Americans is that they are a high-achieving demographic [ 8 ]. While seemingly innocuous, this myth of them as a “model minority” has been criticized as highly problematic. Not only does it run counter to their lived realities—plenty of evidence has exposed the widespread inequalities confronted by various subgroups within the community [ 8 , 9 ]—it also delegitimizes their struggles and feeds the misconception that they require no assistance whatsoever [ 5 ].

Racial discrimination is well known to be a key social determinant of health [ 6 , 10 ]. Among Asian Americans in the United States, experiences of discrimination are linked to poorer mental health outcomes, including anxiety, depression, hypertension, and elevated blood pressure [ 10 ]. Racism may exacerbate health issues brought about by the aging process, such as the onset of chronic diseases or functional impairment [ 11 ], rendering older Asian Americans more susceptible to detrimental health outcomes.

Studies have indicated that social support has a positive impact on both the mental and physical health of older adults [ 12 ]. Social support likewise serves as a protective buffer against the negative effects of racial discrimination on one’s health [ 13 , 14 ]. The role of social support may be especially critical for Asian Americans. Although the Asian American populace includes a diverse array of ethnicities, cultures, and languages, collectivism appears to be a cultural orientation shared among many Asian American groups [ 15 ]. Evidence revealed that social support improved health outcomes among Asian Americans during the start of the pandemic, when anti-Asian sentiments were rampant [ 14 ].

It is widely acknowledged that in Asian societies, attitudes toward older adults are typically informed by values of respect and filial piety [ 11 , 16 ]. Old age bespeaks knowledge and wisdom, and younger people are expected to honor and respect their older counterparts [ 11 ]. Despite concerns that such values have eroded, there is evidence that they continue to resonate with Asian Americans [ 17 ]. One study concluded that Asian Americans are twice as likely as the general population to care for their parents [ 18 ]. Even so, ageism has been discovered to be pan-cultural [ 19 ]. A meta-analysis comparing Western and Eastern attitudes toward older adults revealed that Easterners actually harbored more negative views of older adults than Westerners [ 20 ]. In this analysis, Western countries included anglophone countries in the West such as Australia, Canada, the United Kingdom, and the United States, as well as Western European countries like Switzerland and France. Eastern countries covered countries in different regions of Asia, such as East Asia, South Asia, and Southeast Asia [ 20 ].

First proposed in 2002, the stereotype content model maintains that people stereotype others on the basis of warmth and competence [ 21 ]. The dimension of warmth includes qualities such as friendliness and sincerity, while the dimension of competence includes traits such as intelligence and skillfulness [ 21 ]. According to the stereotype content model, perceptions of social groups can be categorized into four clusters: (1) warm and competent, (2) incompetent and cold, (3) competent and cold, and (4) warm and incompetent. These 4 combinations of stereotypes produce distinct emotional responses among those who hold them. Groups stereotyped as warm and competent elicit admiration. Those evaluated as incompetent and cold elicit contempt. Groups stereotyped as competent and cold evoke envy. Those evaluated as warm but incompetent evoke pity [ 21 ].

A large body of work has evinced that older adults are generally stereotyped as warm but incompetent [ 21 ]. Although they elicit feelings of admiration occasionally, they predominantly evoke pity. Evidence attests to the universality of these stereotypes in both individualistic and collectivistic societies [ 19 ]. The evaluation of older adults as warm but lacking in competence may lend itself to benevolent ageism—a paternalistic form of prejudice founded on the assumption that older adults are helpless or pitiful [ 22 ]. Benevolent ageism has intensified over the course of the pandemic owing to recurring depictions of older adults as an at-risk group [ 23 ].

Asian Americans—older or otherwise—are one of the most underresearched ethnic groups in peer-reviewed literature [ 24 , 25 ]. In spite of the discomfiting rise in violence directed at them during the COVID-19 outbreak, discourse surrounding older adults from the Asian American community has escaped the attention of gerontologists. Most social media analyses conducted before and during the pandemic have focused on the discursive construction of the older population as a whole [ 26 - 28 ]. Other social media analyses have concentrated on the general Asian American population [ 29 - 31 ]. This study is therefore conceptually significant in that it is the first to dissect the content of tweets posted about older Asian Americans during the COVID-19 crisis.

At the heart of the concept of intersectionality is the notion that various social positions—such as race, age, gender, and socioeconomic status—interact to shape the types of biases one confronts [ 32 ]. From an intersectional standpoint, age and race may converge in ways that worsen the experience of discrimination for older Asian Americans [ 33 ]. In addition to being part of a racial group that faces more systemic challenges compared to White people, older Asian Americans also face age-related hurdles [ 34 ]. In terms of practical significance, this study serves as a reflection of the level of support being extended to older Asian Americans, even as it provides timely insights that will ultimately advance equity for them.

This study pivots around the following questions: What types of discourse have emerged in relation to older Asian Americans and the need to support them? How do age and race interact to shape these discourses? What are the implications of these discourses for older Asian Americans?

We retrieved the data using version 2 of Twitter’s application programming interface (API) [ 35 ], which was accessed through Twitter’s academic research product track [ 36 ]. Compared to what was achievable with the standard version 1.1 API, the version 2 API grants users a higher monthly tweet cap and access to more precise filters [ 37 ].

To build an extensive data set, we collected the tweets using 2 search queries. For both queries, “retweets” were excluded, and only English tweets posted from January 1, 2020, to August 1, 2023, were collated. We excluded retweets to avoid including duplicate content in the data set, which could skew the significance of particular topics. Tweets collected through the first query (n=1549) contained the hashtag #ProtectOurElders. For the second query (n=4550), we gathered tweets that met the following inclusion criteria: (1) contained either the hashtag #StopAAPIHate or #StopAsianHate; (2) included “elder,” “elderly,” “old(er) adult(s),” “old(er) people,” “old(er) person(s),” “senior(s),” “aged,” “old folk(s),” “grandparent(s),” “grandfather(s),” “grandmother(s),” “grandpa,” or “grandma.” The 2 queries yielded a total of 6099 tweets.

We removed posts that were (1) contextually irrelevant, that is, discussed content not pertaining to anti-Asian attacks, such as tweets related to getting vaccinated to protect older people, or tweets related to protecting older adults from cybercrime (n=1384); (2) repeated in the 2 queries (n=20); (3) incorrectly retrieved by the API, that is, they did not fulfill the inclusion criteria of either search query (n=258); and (4) informative, factual, or descriptive (eg, tweets that were newspaper headlines) or that brought up the older person in a tangential fashion (eg, tweets that mentioned older Asian Americans alongside several other groups; n=3443). After applying the aforementioned exclusion criteria, the data set consisted of 994 tweets. Figure 1 provides a flowchart of the data collection process.

Tweet Content Coding

Consistent with past research [ 27 , 38 - 41 ], the codebook was designed through both deductive and inductive modes of reasoning [ 42 ]. Analyses led by a directed or deductive approach begin with the identification of an initial set of codes based on previous literature [ 43 ]. Conversely, in inductive content analyses, codes are derived directly from the data [ 43 ]. We used both deductive and inductive approaches to make sure certain pertinent assumptions guided the analysis while also being aware that new categories would surface inductively [ 42 ].

To create a preliminary codebook, we first identified a set of categories based on previous literature regarding the perceptions of older adults in Asia [ 44 ]. The content analysis was subsequently conducted in several stages, with each tweet read twice by 2 researchers trained in gerontology to ensure familiarity with and immersion in the data [ 43 ]. The goal of the first reading was to ascertain the validity of the initial set of categories as well as to generate codes systematically across the whole data set. Each researcher modified the codebook independently until all variables were refined and clearly defined. During this first reading, a new category was added whenever a post featured a particular trait that could not be suitably coded into any of the existing categories and which was recurrent in the data. During the second reading, the 2 coders had frequent discussions where any discrepancies were reviewed and adjudicated to ensure rigor in the analysis. At this point, both coders discussed what the codes meant, confirmed the relevance of the codes to the research question, and identified areas of significant overlap to finalize the coding rubric.

The percentage agreement between the 2 raters was 92.5% with a weighted Cohen κ of 0.89 (P<.001), indicating high interrater reliability. A total of 4 themes emerged from the whole process. The frequency of each theme was identified after the analysis. As mentioned in past scholarship, categories in a content analysis need not be mutually exclusive, although they should be internally homogeneous (ie, coherent within themes) and externally heterogeneous (ie, distinct from each other) as far as possible [ 27 , 45 ].

Ethical Considerations

Ethical approval was not deemed necessary for this study, as all the data used were publicly available and anonymized.

Summary of Insights From Content Analysis of Tweets

A total of 4 themes emerged from our content analysis of 994 tweets. Half of the posts (498/994, 50.1%) were filed under the theme “vulnerable and in need of protection” (theme 1). Tweets in this theme either singled out older Asian Americans as a group in need of protection because of their vulnerable status or discussed initiatives aimed at safeguarding their well-being. The theme “heroic and resilient” (theme 2) was present in 31.1% (309/994) of the posts. Relevant tweets celebrated older Asian Americans for displaying tremendous strength in the face of attack or described them as individuals not to be trifled with. The theme “immigrants who have made selfless contributions and sacrifices” (theme 3) appeared in 10.2% (102/994) of the posts. Posts in this section referenced the immense sacrifices made by older Asian Americans as they migrated to the United States, as well as the systemic barriers they had to overcome. Theme 4 “worthy of honor” (85/994, 8.5%) consisted of tweets that venerated older Asian Americans. Textbox 1 provides a summary of the themes.

Vulnerable and in need of protection (498/994, 50.1%)

“Isn't it so cowardly that they attack the elderly mostly? Not that violence is acceptable for any age, but to hurt the defenseless only means they got loose screws. #StopAsianHate”
“Conducting walking patrols everyday to protect our elders and community #StopAAPIHate #HateisaVirus #StopAsianHate #SFChinatown #SafeNeighborhood #ProtectOurElders #TogetherWeCan”

Heroic and resilient (309/994, 31.1%)

“Underestimating the terror wrought by old Chinese ladies with sticks was his first mistake #grannygoals #StopAsianHate”
“Don't mess with Asian grandmas. But also sad this is happening. #StopAsianHate #StopAAPIHate”

Immigrants who have made selfless contributions and sacrifices (102/994, 10.2%)

“Come to America they said..

It's the land of Opportunities they said...

Feeling so sad seeing this video 2 underage over privileged girls get to do this to a man ,a father ,a grandfather and not even have their identities revealed ...devastating

#MuhammadAnwar #StopAsianHate”

“These are my grandparents. They came to America to build a new life. (That's my dad on the right wearing a tie.) My grandfather was a very well respected doctor in the Chinese community. America is built on the backbone of hard-working immigrants. #StopAsianHate”

Worthy of honor (85/994, 8.5%)

“What's been shocking to me about these increased attacks on #AAPI is how often the elderly have been the focus. It’s such a shock because one thing that has been common amongst #AAPI culture is the reverence/respect of elders. #StopAAPIHate #StopAsianHate”
“It really makes me weak and cry seeing videos of those elderly being hit and hurt. We, Asians, value and esteem our elderly. We even live with them in the same house, take care of them. I can't imagine how someone can simply push them. Just like that. #StopAsianHate”

Theme 1: Vulnerable and in Need of Protection

The vulnerability of older adults was a throughline in this category (498/994, 50.1%). Although concern was directed at the entire Asian American population, older adults were singled out as deserving of more sympathy because of their advanced age. Adjectives commonly used to frame them include “infirm,” “weak,” “defenseless,” and “powerless.” A person described them as lacking “the strength to even unclasp a grip.” Sympathy for older adults was magnified in view of other challenges they had been confronting since the outbreak of COVID-19. For instance, one poster expressed sorrow over how older Asian Americans had to grapple with the “fear of getting attacked” on top of “already [being] really afraid of COVID-19 because it disproportionately affects” them.

What made the act “especially egregious” in the eyes of many was the fact that assailants targeted older adults of all people. Users lambasted attackers for their “coward[ice],” asserting that they should have “picked on someone [their] own size” instead of attacking “people who can’t even defend themselves.” Several posters insisted that it was incumbent upon society to “be watchdogs” for older adults since they are more vulnerable.

A large number of tweets featured a call-to-action aimed at mobilizing members of the Twitter community to assist older Asian Americans. Fundraising campaigns were conducted to raise money for “alarms and pepper spray” for older Asian Americans. Others lobbied for donations to causes that deliver food to this group. The following tweet is one such example: “Wondering how you can support elderly Asians and show you will not tolerate #Asianhate? Join me in making a contribution to @heartofdinner, which brings food to elderly Asians in NYC so they can eat safely in their homes #StopAsianHateCrimes #StopAAPIHate.” The Twitter audience was also invited to escort older persons who walk alone: “United Peace Collaborative protects the #SF Chinatown community with daily walking patrols, providing protection & assistance to the elderly & residents. Please join us & volunteer!”

There were many tweets concerning the suite of initiatives aimed at supporting older Asian Americans. The Yellow Whistle—a campaign involving the distribution of free whistles for Asian Americans to signal danger in the event of an assault—was held up as one such example to “keep older Asian Americans safe.” Select community partners also received plaudits for their “wonderful work in distributing and training use of the alarms to” older persons.

Theme 2: Heroic and Resilient

Tweets in this theme (309/994, 31.1%) mainly revolved around a high-profile incident in San Francisco in which Xiao Zhen Xie, an older woman of Asian descent, put her assailant on a stretcher in an unexpected turn of events. She earned kudos from the Twitter community for “hold[ing] her ground,” “fighting back,” and sending him “to the hospital with his face bloodied.” Many saluted her for being “feisty,” “resilient,” and “[as] tough as nails,” dubbing her a “hero” who made them feel “#HonoredToBeAsian.” One user used the hashtag “#GrannyGoals,” quipping that the attacker made a “mistake” “underestimating the terror” that “old Chinese ladies” could wreak. Xiao Zhen Xie was also applauded for “refusing to be a statistic” as well as for defying the image of older adults as a group most expect “not to fight back.”

This episode involving Xiao Zhen Xie set in motion a series of tweets in which users warned others not to get on their grandparents’ bad side. A user cautioned that the incident was a lesson to everyone not to “mess with ahjummas, lolas, and all the elderly Asian women.” Another claimed that Asian grandmothers possess a special kind of “Asian grandma strength.” Some took the opportunity to underline the importance of not belittling older adults, with one in particular commenting on how his or her grandparents embodied grit and “toughness” because they “lived through war.”

Besides Xiao Zhen Xie, a few other older Asian Americans were celebrated for their resilience. A Filipina immigrant, Vilma Kari, was lauded for saying she “forgives” and “prays” for her attacker. A handful of tweets focused on a group of older Asian Americans who made the headlines for having filmed a music video in which they condemned the racially motivated acts of violence targeting their community.

Theme 3: Immigrants Who Have Made Selfless Contributions and Sacrifices

Members of the Twitter community frequently shared stories of their grandparents’ immigration (102/994, 10.2%). A common thread running through these posts was that their forefathers made immense sacrifices, uprooting themselves to move to the United States in order that their children might receive “the best education they can get” and “enjoy a “better future.” A user portrayed his or her grandmother as a “fighter” who “worked two to three jobs” while struggling to acculturate in a new society at a time when she knew “very little English.”

Attention was drawn to how the string of attacks against Asian Americans was ironic given the national ethos of the country commonly touted as the “American dream.” A few posters implied that labeling the United States as a “land of opportunity” was a misnomer: “Come to America,’ they said... ‘It’s the land of opportunities,’ they said...” A user said that the Asian “elderly did not escape communism” only to become a target of racism.

Tweets in this theme also discussed the burden of racism that older Asian Americans had endured before the COVID-19 pandemic. Users commented on their grandparents’ day-to-day experiences of racial discrimination. A handful were dismayed by how their grandparents were survivors of “prejudice and xenophobia” during World War II when they were forcibly relocated to Japanese internment camps. Others bemoaned that their older family members were “imprisoned for being the wrong-colored Americans.” One user deplored the fact that his or her grandfather “could not come to [the United States] because of his race” due to the Chinese Exclusion Act of 1882, a law that suspended Chinese immigration for 10 years and declared Chinese immigrants ineligible for naturalization. Another poster pinpointed how his or her grandfather felt compelled to dress in an “extremely patriotic” manner in order to camouflage his Asian identity and better assimilate into America.

Users considered older Asian Americans as foundational to the growth of America and foregrounded the need to acknowledge that “America is built on the backbone of hardworking immigrants,” who “made 90%” of what society has. Examples of contributions made by those of Asian ancestry include how they “oversaw” the construction of the transcontinental railroad in the “Old West” as well as their “service in the #442RCT (442nd Infantry Regiment)”—a highly decorated infantry regiment that mainly comprised second-generation American soldiers of Japanese descent who served in World War II. One user mentioned Chien-Shiung Wu, a groundbreaking Chinese American physicist whose scientific accomplishments were a core part of “U.S. WW II efforts” and that “helped win Nobel Prizes for Americans,” without which the “country would be so much worse off.” Artworks inspired by “hustling, elderly Asian folks” were also broadcasted under a hashtag that deified them as “#ChinatownGods.”

Several attempts were made to deconstruct the myth of the model minority. Individuals were aggrieved at how the looming specter of anti-Asian violence compounded the plight of older Asian Americans, who had already been dealt multiple blows during the COVID-19 crisis. These posters raised awareness of how many of them are in “precarious living situations” or “working in low-wage jobs.” Some pleaded for the Asian American community to be seen and understood, as captured in the following tweet: “See what’s happening to our elderly and community. Understand us. Understand why no matter how model of a minority we seem to be... we are just like you. #StopAsianHate #StopAAPIHate #StandWithAsians.”

Theme 4: Worthy of Honor

Many users (85/994, 8.5%) were outraged at how older adults appeared to be prime targets of violence against the Asian American community, perceiving these acts as a flagrant transgression of Asian cultural mores that “revere” them as “the most important people” in society. Some tweets exalted them as wellsprings of “wisdom” and “thoughtful guidance”—one user even likened them to “gold”—to “value and esteem.” Tweets in this theme also alluded to how deference to the older community was practically nonnegotiable in the Asian household. A poster tweeted, “No one should be assaulted, especially the elderly. I grew up respecting my elders. You never even argued with them ... They pass on wisdom.”

Values of collectivism were prized by certain users. These posters made reference to the notion of intergenerational reciprocity by stressing that younger people had an obligation to “protect” the older generation in return. The idea of solidarity was also raised. For instance, some viewed the attack of an older adult—related or otherwise—as an affront to the entire Asian community: “Many are saying ‘she could've been MY grandma.’ To that I say, she is ALL OUR GRANDMAS. Fight hate, love justice, stand with our elders always. #ForTheLoveOfLolas #StopAsianHate #StopAAPIHate #StopAsianHateCrimes.”

This study serves as a substantive first step in understanding discourses surrounding older Asian Americans. In our content analysis of tweets posted about the rash of attacks targeting them during the COVID-19 crisis, 4 main discourses surfaced. The first positioned them as “vulnerable and in need of protection” (theme 1). The second characterized them as “heroic and resilient” (theme 2). The third portrayed them as “immigrants who have made selfless contributions and sacrifices” (theme 3), and the fourth extolled them as “worthy of honor” (theme 4).

Our findings demonstrate an outpouring of support for the older Asian American community, which manifested itself in various local initiatives such as the distribution of safety whistles and the delivery of food. Scholars have drawn attention to how social support is particularly crucial for those in their later years [ 12 ] as well as those who experience racial discrimination [ 13 , 14 ]. The fact that older Asian Americans are finally being given support and assistance is therefore a step in the right direction.

However, even well-intentioned acts may be met with negative repercussions. In the wake of the COVID-19 crisis, older adults were reduced to a uniform group of at-risk individuals [ 46 ]. Assumptions of their vulnerability led to paternalistic behaviors, which denied them their autonomy [ 23 ]. Our results indicate that the rise in violence toward older Asian Americans sparked much-needed dialogue regarding their everyday struggles. Nevertheless, an unfortunate corollary is that this may have predisposed them to being recipients of benevolent prejudice on the basis of both age and race. Older Asian Americans may have been viewed as especially defenseless or vulnerable, perhaps more so than the general older population. This was made amply clear in the findings, where half of the tweets branded older Asian Americans as “weak” and “powerless.”

Notwithstanding concerns that Asian values of respect and filial piety have become irrelevant in the face of modernization [ 17 ], findings from themes 2-4 show emphatically that older adults retain their revered status, at least among some in the Asian American community. Tweets in theme 2 featured users enthusing over the way Xiao Zhen Xie held her ground when she was attacked in San Francisco, which led to deliberations on the strength and tenacity of older Asian women in general. Discourses of gratitude emerged in theme 3 as users ruminated over the sacrifices their forefathers had made in migrating to the United States, as well as the attendant systemic challenges they had to navigate. Posts in theme 4 indicate that users perceived the violence against older Asian Americans as a contravention of cultural norms, which emphasize the importance of honoring older adults. These provide a countervailing force to the various ageist tropes that came to the fore during the COVID-19 pandemic, such as the #BoomerRemover hashtag, which saw the lives of older people being discounted [ 27 , 28 ].

Theoretical Contribution and Implications

Findings from this study show that during the COVID-19 pandemic, age and race interfaced in complex ways to shape discourses on older Asian Americans. Specifically, our content analysis demonstrates that the stereotypes of warmth and incompetence, which are often thought to shape evaluations of older adults, cannot be applied indiscriminately to older Asian Americans as a subcategory of the older demographic. Theme 1, which positions older Asian Americans as vulnerable and in need of protection, does indeed align with traditional evaluations of older adults as warm and incompetent. However, the remaining themes celebrate older Asian Americans for their numerous contributions to society, the sacrifices they have made, and their unwavering resilience during the pandemic, all of which challenge the stereotype of incompetence under the stereotype content model. These findings add complexity to the commonly held notion of older adults as a pitiful social group by highlighting that older Asian Americans evoke not just pity but also admiration. The stereotype content model should therefore be expanded or modified in a way that accounts for attitudes toward older adults of different ethnicities.

Additionally, gerontological scholarship would benefit from a cross-cultural analysis of benevolent ageism. At present, little is known about how displays of benevolent ageism are affected by cultural norms of parental respect and filial piety and the extent to which these norms affect one’s perception of an older adult’s competence. Several studies have been conducted to make sense of ageism in different cultures [ 47 , 48 ], but there has been limited research on the cross-cultural differences in benevolent ageism specifically. The ways in which evaluations of older Asian Americans may be complicated by the deeply ingrained myth of the model minority as well as the pandemic-induced rise in anti-Asian hate are important avenues for future study.

This study has a number of implications for policy and practice. First, although care toward one’s parents or grandparents is not the prerogative of Asians [ 49 ], Asia’s adherence to collectivism nonetheless offers a useful learning point for the West. Many of the posters were Asian Americans, who held older adults in high regard, whether related or otherwise. Fostering a cultural emphasis on solidarity and interconnectedness in the West may promote respect not only for one’s parents but also for older adults outside of one’s family [ 44 ]. Second, ongoing efforts to reframe aging [ 50 ] could highlight the need to respect older adults, not in a way that advances their supremacy or absolves them from wrongdoing, but in a way that teaches society to view them as people whose experience may render them wise and worth learning from. Educators could also incorporate lessons on age-related stereotypes in schools to guard against the formation of ageist beliefs [ 51 ].

Third, current moves to redress the longstanding omission of Asian American history from national curricula [ 52 ] should ensure that students in every state are taught about the sacrifices, struggles, and contributions of older Asian Americans. Public campaigns could be organized as well to raise awareness of the aforementioned. This will help counter the myth of the model minority and get more people to acknowledge older Asian Americans as a significant part of America’s social fabric. Fourth, our findings underscore the need to reflect on the diversity of the older population in terms of socioeconomic status. Older adults—particularly those from the baby boomer generation—have been stereotyped as having made significant financial gains compared to their predecessors, at times even seen as stealing resources from the young [ 53 ]. However, as highlighted by some of the Twitter users as well as scholars, many older Asian Americans are in dire economic straits [ 5 ]. Rectifying the structural inequities that have contributed to their immiseration should hence be a key component of the agenda moving forward.

There are limitations inherent in this study. First, we acknowledge that Twitter users might not be representative of the wider population and that only publicly available tweets were included in the data set. Some of the users whose tweets were included in the study appeared to be Asian Americans, who are likely to be more passionate about supporting individuals in their community. Relatedly, as we did not collect information regarding users’ demographics—not all users publish demographic information, and there are certain limitations to using publicly provided demographic information on social media [ 54 ]—we could not contextualize the motivations of those whose tweets were included in the analysis. Ultimately, social support for older Asian Americans—whether from the Asian American community or society as a whole—has important implications for their well-being [ 14 ]. Subsequent research could focus on conducting interviews among individuals from different ethnic groups to tease out any differences in the level of support extended to older Asian Americans.

Second, we queried the hashtag #StopAAPIHate as a way to understand sentiments toward Asian Americans, even though the term “AAPI” refers to 2 different racial groups: Asian Americans and Pacific Islanders. As the tweets analyzed paid more attention to older Asian Americans, we were not able to offer insight into the types of discourses that emerged in relation to older Pacific Islanders. Future studies are needed to expound on such discourses. Third, it is vital to highlight that both the Asian American community and the older population are heterogeneous. The Asian American community encompasses numerous ethnicities, all with distinct languages, cultures, immigration histories, values, and beliefs [ 34 ]. The older demographic, too, is a diverse group composed of people with vastly different health trajectories [ 55 ]. Given the brevity of the tweets uploaded, we were unable to assess how discourses on older Asian Americans vary across different ethnicities. Finally, we collected only textual data, although tweets often contain visual elements such as photos, videos, and GIFs. This is a drawback that can be overcome in the future when multimodal techniques are developed to analyze both textual and visual content on Twitter.

Another direction for future inquiry involves an assessment of how discourses surrounding older Asian Americans have changed over time. The level of support shown to this group is likely to fluctuate over time, depending on the frequency at which anti-Asian attacks are reported in the news as well as other types of news being covered. Sentiment and narrative analyses [ 56 - 58 ] could be performed to glean such insights.

Even as older Asian Americans contended with a rise in racism alongside other struggles during the COVID-19 pandemic, our findings reveal that the crisis had the unintended effect of garnering greater support for this group. In the future, it is important that support be extended to older Asian Americans not so much by virtue of their perceived vulnerability but more so in view of their boundless contributions and sacrifices.

Acknowledgments

The authors would like to thank L Liu for preprocessing the data. We gratefully acknowledge support from the Social Science Research Council SSHR Fellowship (MOE2018-SSHR-004). The funder had no role in study design, data collection, analysis, writing, or the decision to publish this study.

Data Accessibility

Data are publicly available on Twitter [ 59 ].

Authors' Contributions

RN designed the study, developed the methodology, analyzed the data, wrote the paper, acquired the funding. RN and NI analyzed the data and wrote the paper.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 19.01.23; peer-reviewed by A Atalay, A Bacong; comments to author 22.02.23; revised version received 12.03.23; accepted 14.09.23; published 29.03.24.

©Reuben Ng, Nicole Indran. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Qualitative Content Analysis 101 (+ Examples)
Content analysis is a qualitative analysis method that focuses on recorded human artefacts such as manuscripts, voice recordings and journals. Content analysis investigates these written, spoken and visual artefacts without explicitly extracting data from participants - this is called unobtrusive research. In other words, with content ...
The Practical Guide to Qualitative Content Analysis
Qualitative content analysis is a research method used to analyze and interpret the content of textual data, such as written documents, interview transcripts, or other forms of communication. This guide introduces qualitative content analysis, explains the different types of qualitative content analysis, and provides a step-by-step guide for ...
Content Analysis
Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual: Books, newspapers and magazines. Speeches and interviews. Web content and social media posts. Photographs and films.
Qualitative Content Analysis
Qualitative Content Analysis. Qualitative content analysis is a research method attempt to identify core consistencies and meanings through the systematic classification process of coding and identifying themes or patterns (Hsieh & Shannon, 2005; From: Aggression and Violent Behavior, 2012. View all Topics.
How to plan and perform a qualitative study using content analysis
Abstract. This paper describes the research process - from planning to presentation, with the emphasis on credibility throughout the whole process - when the methodology of qualitative content analysis is chosen in a qualitative study. The groundwork for the credibility initiates when the planning of the study begins.
Content Analysis Method and Examples
Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts.
Qualitative Content Analysis
It is a flexible research method ( Anastas, 1999 ). Qualitative content analysis may use either newly collected data, existing texts and materials, or a combination of both. It may be used in exploratory, descriptive, comparative, or explanatory research designs, though its primary use is descriptive.
Content Analysis
Step 1: Select the content you will analyse. Based on your research question, choose the texts that you will analyse. You need to decide: The medium (e.g., newspapers, speeches, or websites) and genre (e.g., opinion pieces, political campaign speeches, or marketing copy)
Chapter 17. Content Analysis
This is a qualitative research method because the focus is on the meanings and interpretations of that content rather than strictly numerical counts or variables-based causal modeling. [1] ... Qualitative Content Analysis in Practice. Thousand Okas, CA: SAGE. Arguably the most accessible guidebook for QCA, written by a professor based in ...
A hands-on guide to doing content analysis
A common starting point for qualitative content analysis is often transcribed interview texts. The objective in qualitative content analysis is to systematically transform a large amount of text into a highly organised and concise summary of key results. Analysis of the raw data from verbatim transcribed interviews to form categories or themes ...
Content Analysis
In his 1952 text on the subject of content analysis, Bernard Berelson traced the origins of the method to communication research and then listed what he called six distinguishing features of the approach. As one might expect, the six defining features reflect the concerns of social science as taught in the 1950s, an age in which the calls for an "objective," "systematic," and ...
Qualitative Research and Content Analysis
5 Qualitative Research and Content Analysis. Qualitative research is performed to study and understand phenomena in their natural contexts. As such, qualitative research focuses on—and respects—people's experiences and perspectives, neither of which can be described through objective measurements or numbers.
UCSF Guides: Qualitative Research Guide: Content Analysis
"Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts." Source: Columbia Public Health
PDF Qualitative Analysis of Content
quantitative content analysis is deductive, intended to test hypotheses or address questions generated from theories or previous empirical research. By contrast, qualitative content analysis is mainly inductive, grounding the examination of topics and themes, as well as the inferences drawn from them, in the data. In some cases, qualitative content
Demystifying Content Analysis
Qualitative Content Analysis. Content analysis rests on the assumption that texts are a rich data source with great potential to reveal valuable information about particular phenomena. 8 It is the process of considering both the participant and context when sorting text into groups of related categories to identify similarities and differences, patterns, and associations, both on the surface ...
Exploring Content Analysis in Qualitative Research
Qualitative content analysis is a specific type of content analysis that primarily focuses on the interpretation and understanding of textual data. While it shares some similarities with its quantitative counterpart—such as the use of systematic and replicable methods—qualitative content analysis tends to dive deeper into the nuances ...
Qualitative Content Analysis: Theoretical Background and Procedures
The techniques of Qualitative Content Analysis have become a standard procedure of text analysis within the social sciences. In their bibliometrical analysis of the Social Sciences Citation Index (SSCI, 1991-1998), Titscher et al. found Qualitative Content Analysis in seventh place (after Grounded Theory, Ethnography, Standardized Content Analysis, Critical Discourse Analysis, Conversation ...
Content Analysis
Content analysis can be used to study a wide range of topics, including media coverage of social issues, political speeches, advertising messages, and online discussions, among others. It is often used in qualitative research and can be combined with other methods to provide a more comprehensive understanding of a particular phenomenon.
Qualitative Content Analysis
Qualitative content analysis is one of the several qualita-tive methods currently available for analyzing data and inter-preting its meaning (Schreier, 2012). As a research method, it represents a systematic and objective means of describing and quantifying phenomena (Downe-Wamboldt, 1992; Schreier, 2012).
Reflexive Content Analysis: An Approach to Qualitative Data Analysis
Content analysis, initially a quantitative technique for identifying patterns in qualitative data, has evolved into a widely used qualitative method. However, this evolution has resulted in a confu...
(PDF) Qualitative content analysis
Shava et al. (2021) stated that qualitative content analysis is one of the research methods used to analyze text data which extracting objective content from text to examine meanings, themes, and ...
Difficulties and challenges experienced by nurses in eldercare
The audio-recorded interviews were transcribed and subsequently subjected to analysis using Graneheim and Lundman's qualitative conventional content analysis. Data analysis was supported by the qualitative data analysis software MAXQDA 2020. ... The reporting of this study followed the consolidated criteria for reporting qualitative research ...
Using the consolidated Framework for Implementation Research to
Procedure. The procedure for this research is multi-stepped and is summarized in Fig. 1.First, we mapped retrospective qualitative data collected during the development of the SCI-HMT [] against the five domains of the CFIR in order to create a semi-structured interview guide (Step 1).Then, we used this interview guide to collect prospective data from health professionals and people with SCI ...
Perceived barriers and opportunities to improve working conditions and
Directive content analysis was applied to the data.21 This analysis strategy was used to identify common themes from participant responses, using deductive codes by identifying key concepts from existing theory19 and prior research. Two researchers (ER, JD) read through each transcript, highlighting passages that could be categorised in the pre ...
A hands-on guide to doing content analysis
There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including the emergency care context in Africa. ... Our objective with this manuscript is to provide a practical hands-on example of qualitative content analysis to aid novice qualitative researchers in their task. Keywords: ...
JMIR Formative Research
The look, feel, and content of the website were described as welcoming due to pictures, animations, and videos that showcased resources in a personal, colorful, and inviting way. ... Journal of Medical Internet Research 8257 articles ... Qualitative Thematic Analysis Authors of this article: ...
Content Analysis
Abstract. In this chapter, the focus is on ways in which content analysis can be used to investigate and describe interview and textual data. The chapter opens with a contextualization of the method and then proceeds to an examination of the role of content analysis in relation to both quantitative and qualitative modes of social research.
Community College Psychology Students' Cooperative Learning Experiences
Ninety-six college students completed 5 open-ended questions that asked students about their preferences for cooperative learning activities. Forty-nine first-year students and 47 second-year students participated in the study. A qualitative research design was used. Qualitative analyses compared---by year in college---the 5 open-ended questions.
Journal of Medical Internet Research
Inductive and deductive approaches informed our qualitative content analysis. Results: A total of 4 themes emerged, with 50.1% (498/994) of posts framing older Asian Americans as "vulnerable and in need of protection" (theme 1). Tweets in this theme either singled them out as a group in need of protection because of their vulnerable status ...
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Climate change impacts vary wildly across different geographical contexts and their effects are primarily felt on the local level, generating demand for local solutions. The local level plays a key role in the adaptation to climate change. Nevertheless, in most European countries adaptation has yet to be integrated comprehensively into local policy agendas. To further our understanding of this ...

What Is Qualitative Content Analysis?

Overview: Qualitative Content Analysis

1. What is content analysis?

The basics – explicit and implicit content

2. The two types of content analysis

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3. When should you use content analysis?

4. How to conduct a qualitative content analysis

Step 1 – Recap on your research questions

Step 2 – Reflect on your personal perspectives and biases

Step 3 – Operationalise your variables and develop a coding scheme

Step 4 – Code and analyse the data

How to conduct conceptual analysis

How to conduct relational analysis

5. What are the pros & cons of content analysis?

Recap: Qualitative content analysis

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Content Analysis | Guide, Methods & Examples

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1. Select the content you will analyze

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5. Analyze the results and draw conclusions

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Content Analysis | A Step-by-Step Guide with Examples

Table of contents

Prevent plagiarism, run a free check.

Step 1: Select the content you will analyse

Step 2: Define the units and categories of analysis

Step 3: Develop a set of rules for coding

Step 4: Code the text according to the rules

Step 5: Analyse the results and draw conclusions

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Chapter 17. Content Analysis

An Overview of the Content Analysis Process

The Second Step: Collecting and Storing

The Third Step: Analysis

The Fourth and Final Step: The Write-Up

Analyzing Content: Helpful Hints and Pointers

Further Readings

The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

What is meant by content analysis?

Content analysis

Differences from other research methods

Advantages of content analysis

Disadvantages of content analysis

What is qualitative content analysis?

Textual analysis made easy with ATLAS.ti

Defining quantitative content analysis

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Qualitative Content Analysis: Theoretical Background and Procedures

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