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Article Contents

Primacy of the research question, structure of the paper, writing a research article: advice to beginners.

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Thomas V. Perneger, Patricia M. Hudelson, Writing a research article: advice to beginners, International Journal for Quality in Health Care , Volume 16, Issue 3, June 2004, Pages 191–192, https://doi.org/10.1093/intqhc/mzh053

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Writing research papers does not come naturally to most of us. The typical research paper is a highly codified rhetorical form [ 1 , 2 ]. Knowledge of the rules—some explicit, others implied—goes a long way toward writing a paper that will get accepted in a peer-reviewed journal.

A good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper. Whatever relates to the research question belongs in the paper; the rest doesn’t. This is perhaps obvious when the paper reports on a well planned research project. However, in applied domains such as quality improvement, some papers are written based on projects that were undertaken for operational reasons, and not with the primary aim of producing new knowledge. In such cases, authors should define the main research question a posteriori and design the paper around it.

Generally, only one main research question should be addressed in a paper (secondary but related questions are allowed). If a project allows you to explore several distinct research questions, write several papers. For instance, if you measured the impact of obtaining written consent on patient satisfaction at a specialized clinic using a newly developed questionnaire, you may want to write one paper on the questionnaire development and validation, and another on the impact of the intervention. The idea is not to split results into ‘least publishable units’, a practice that is rightly decried, but rather into ‘optimally publishable units’.

What is a good research question? The key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community. The research question should be precise and not merely identify a general area of inquiry. It can often (but not always) be expressed in terms of a possible association between X and Y in a population Z, for example ‘we examined whether providing patients about to be discharged from the hospital with written information about their medications would improve their compliance with the treatment 1 month later’. A study does not necessarily have to break completely new ground, but it should extend previous knowledge in a useful way, or alternatively refute existing knowledge. Finally, the question should be of interest to others who work in the same scientific area. The latter requirement is more challenging for those who work in applied science than for basic scientists. While it may safely be assumed that the human genome is the same worldwide, whether the results of a local quality improvement project have wider relevance requires careful consideration and argument.

Once the research question is clearly defined, writing the paper becomes considerably easier. The paper will ask the question, then answer it. The key to successful scientific writing is getting the structure of the paper right. The basic structure of a typical research paper is the sequence of Introduction, Methods, Results, and Discussion (sometimes abbreviated as IMRAD). Each section addresses a different objective. The authors state: (i) the problem they intend to address—in other terms, the research question—in the Introduction; (ii) what they did to answer the question in the Methods section; (iii) what they observed in the Results section; and (iv) what they think the results mean in the Discussion.

In turn, each basic section addresses several topics, and may be divided into subsections (Table 1 ). In the Introduction, the authors should explain the rationale and background to the study. What is the research question, and why is it important to ask it? While it is neither necessary nor desirable to provide a full-blown review of the literature as a prelude to the study, it is helpful to situate the study within some larger field of enquiry. The research question should always be spelled out, and not merely left for the reader to guess.

Typical structure of a research paper

The Methods section should provide the readers with sufficient detail about the study methods to be able to reproduce the study if so desired. Thus, this section should be specific, concrete, technical, and fairly detailed. The study setting, the sampling strategy used, instruments, data collection methods, and analysis strategies should be described. In the case of qualitative research studies, it is also useful to tell the reader which research tradition the study utilizes and to link the choice of methodological strategies with the research goals [ 3 ].

The Results section is typically fairly straightforward and factual. All results that relate to the research question should be given in detail, including simple counts and percentages. Resist the temptation to demonstrate analytic ability and the richness of the dataset by providing numerous tables of non-essential results.

The Discussion section allows the most freedom. This is why the Discussion is the most difficult to write, and is often the weakest part of a paper. Structured Discussion sections have been proposed by some journal editors [ 4 ]. While strict adherence to such rules may not be necessary, following a plan such as that proposed in Table 1 may help the novice writer stay on track.

References should be used wisely. Key assertions should be referenced, as well as the methods and instruments used. However, unless the paper is a comprehensive review of a topic, there is no need to be exhaustive. Also, references to unpublished work, to documents in the grey literature (technical reports), or to any source that the reader will have difficulty finding or understanding should be avoided.

Having the structure of the paper in place is a good start. However, there are many details that have to be attended to while writing. An obvious recommendation is to read, and follow, the instructions to authors published by the journal (typically found on the journal’s website). Another concerns non-native writers of English: do have a native speaker edit the manuscript. A paper usually goes through several drafts before it is submitted. When revising a paper, it is useful to keep an eye out for the most common mistakes (Table 2 ). If you avoid all those, your paper should be in good shape.

Common mistakes seen in manuscripts submitted to this journal

Huth EJ . How to Write and Publish Papers in the Medical Sciences , 2nd edition. Baltimore, MD: Williams & Wilkins, 1990 .

Browner WS . Publishing and Presenting Clinical Research . Baltimore, MD: Lippincott, Williams & Wilkins, 1999 .

Devers KJ , Frankel RM. Getting qualitative research published. Educ Health 2001 ; 14 : 109 –117.

Docherty M , Smith R. The case for structuring the discussion of scientific papers. Br Med J 1999 ; 318 : 1224 –1225.

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13.1 Formatting a Research Paper

Learning objectives.

• Identify the major components of a research paper written using American Psychological Association (APA) style.
• Apply general APA style and formatting conventions in a research paper.

In this chapter, you will learn how to use APA style , the documentation and formatting style followed by the American Psychological Association, as well as MLA style , from the Modern Language Association. There are a few major formatting styles used in academic texts, including AMA, Chicago, and Turabian:

• AMA (American Medical Association) for medicine, health, and biological sciences
• APA (American Psychological Association) for education, psychology, and the social sciences
• Chicago—a common style used in everyday publications like magazines, newspapers, and books
• MLA (Modern Language Association) for English, literature, arts, and humanities
• Turabian—another common style designed for its universal application across all subjects and disciplines

While all the formatting and citation styles have their own use and applications, in this chapter we focus our attention on the two styles you are most likely to use in your academic studies: APA and MLA.

If you find that the rules of proper source documentation are difficult to keep straight, you are not alone. Writing a good research paper is, in and of itself, a major intellectual challenge. Having to follow detailed citation and formatting guidelines as well may seem like just one more task to add to an already-too-long list of requirements.

Following these guidelines, however, serves several important purposes. First, it signals to your readers that your paper should be taken seriously as a student’s contribution to a given academic or professional field; it is the literary equivalent of wearing a tailored suit to a job interview. Second, it shows that you respect other people’s work enough to give them proper credit for it. Finally, it helps your reader find additional materials if he or she wishes to learn more about your topic.

Furthermore, producing a letter-perfect APA-style paper need not be burdensome. Yes, it requires careful attention to detail. However, you can simplify the process if you keep these broad guidelines in mind:

• Work ahead whenever you can. Chapter 11 “Writing from Research: What Will I Learn?” includes tips for keeping track of your sources early in the research process, which will save time later on.
• Get it right the first time. Apply APA guidelines as you write, so you will not have much to correct during the editing stage. Again, putting in a little extra time early on can save time later.
• Use the resources available to you. In addition to the guidelines provided in this chapter, you may wish to consult the APA website at http://www.apa.org or the Purdue University Online Writing lab at http://owl.english.purdue.edu , which regularly updates its online style guidelines.

General Formatting Guidelines

This chapter provides detailed guidelines for using the citation and formatting conventions developed by the American Psychological Association, or APA. Writers in disciplines as diverse as astrophysics, biology, psychology, and education follow APA style. The major components of a paper written in APA style are listed in the following box.

These are the major components of an APA-style paper:

Body, which includes the following:

• Headings and, if necessary, subheadings to organize the content
• In-text citations of research sources
• References page

All these components must be saved in one document, not as separate documents.

The title page of your paper includes the following information:

• Title of the paper
• Author’s name
• Name of the institution with which the author is affiliated
• Header at the top of the page with the paper title (in capital letters) and the page number (If the title is lengthy, you may use a shortened form of it in the header.)

List the first three elements in the order given in the previous list, centered about one third of the way down from the top of the page. Use the headers and footers tool of your word-processing program to add the header, with the title text at the left and the page number in the upper-right corner. Your title page should look like the following example.

The next page of your paper provides an abstract , or brief summary of your findings. An abstract does not need to be provided in every paper, but an abstract should be used in papers that include a hypothesis. A good abstract is concise—about one hundred fifty to two hundred fifty words—and is written in an objective, impersonal style. Your writing voice will not be as apparent here as in the body of your paper. When writing the abstract, take a just-the-facts approach, and summarize your research question and your findings in a few sentences.

In Chapter 12 “Writing a Research Paper” , you read a paper written by a student named Jorge, who researched the effectiveness of low-carbohydrate diets. Read Jorge’s abstract. Note how it sums up the major ideas in his paper without going into excessive detail.

Write an abstract summarizing your paper. Briefly introduce the topic, state your findings, and sum up what conclusions you can draw from your research. Use the word count feature of your word-processing program to make sure your abstract does not exceed one hundred fifty words.

Depending on your field of study, you may sometimes write research papers that present extensive primary research, such as your own experiment or survey. In your abstract, summarize your research question and your findings, and briefly indicate how your study relates to prior research in the field.

Margins, Pagination, and Headings

APA style requirements also address specific formatting concerns, such as margins, pagination, and heading styles, within the body of the paper. Review the following APA guidelines.

Use these general guidelines to format the paper:

• Set the top, bottom, and side margins of your paper at 1 inch.
• Use double-spaced text throughout your paper.
• Use a standard font, such as Times New Roman or Arial, in a legible size (10- to 12-point).
• Use continuous pagination throughout the paper, including the title page and the references section. Page numbers appear flush right within your header.
• Section headings and subsection headings within the body of your paper use different types of formatting depending on the level of information you are presenting. Additional details from Jorge’s paper are provided.

Begin formatting the final draft of your paper according to APA guidelines. You may work with an existing document or set up a new document if you choose. Include the following:

• Your title page
• The abstract you created in Note 13.8 “Exercise 1”
• Correct headers and page numbers for your title page and abstract

APA style uses section headings to organize information, making it easy for the reader to follow the writer’s train of thought and to know immediately what major topics are covered. Depending on the length and complexity of the paper, its major sections may also be divided into subsections, sub-subsections, and so on. These smaller sections, in turn, use different heading styles to indicate different levels of information. In essence, you are using headings to create a hierarchy of information.

The following heading styles used in APA formatting are listed in order of greatest to least importance:

• Section headings use centered, boldface type. Headings use title case, with important words in the heading capitalized.
• Subsection headings use left-aligned, boldface type. Headings use title case.
• The third level uses left-aligned, indented, boldface type. Headings use a capital letter only for the first word, and they end in a period.
• The fourth level follows the same style used for the previous level, but the headings are boldfaced and italicized.
• The fifth level follows the same style used for the previous level, but the headings are italicized and not boldfaced.

Visually, the hierarchy of information is organized as indicated in Table 13.1 “Section Headings” .

Table 13.1 Section Headings

A college research paper may not use all the heading levels shown in Table 13.1 “Section Headings” , but you are likely to encounter them in academic journal articles that use APA style. For a brief paper, you may find that level 1 headings suffice. Longer or more complex papers may need level 2 headings or other lower-level headings to organize information clearly. Use your outline to craft your major section headings and determine whether any subtopics are substantial enough to require additional levels of headings.

Working with the document you developed in Note 13.11 “Exercise 2” , begin setting up the heading structure of the final draft of your research paper according to APA guidelines. Include your title and at least two to three major section headings, and follow the formatting guidelines provided above. If your major sections should be broken into subsections, add those headings as well. Use your outline to help you.

Because Jorge used only level 1 headings, his Exercise 3 would look like the following:

Citation Guidelines

In-text citations.

Throughout the body of your paper, include a citation whenever you quote or paraphrase material from your research sources. As you learned in Chapter 11 “Writing from Research: What Will I Learn?” , the purpose of citations is twofold: to give credit to others for their ideas and to allow your reader to follow up and learn more about the topic if desired. Your in-text citations provide basic information about your source; each source you cite will have a longer entry in the references section that provides more detailed information.

In-text citations must provide the name of the author or authors and the year the source was published. (When a given source does not list an individual author, you may provide the source title or the name of the organization that published the material instead.) When directly quoting a source, it is also required that you include the page number where the quote appears in your citation.

This information may be included within the sentence or in a parenthetical reference at the end of the sentence, as in these examples.

Epstein (2010) points out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Here, the writer names the source author when introducing the quote and provides the publication date in parentheses after the author’s name. The page number appears in parentheses after the closing quotation marks and before the period that ends the sentence.

Addiction researchers caution that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (Epstein, 2010, p. 137).

Here, the writer provides a parenthetical citation at the end of the sentence that includes the author’s name, the year of publication, and the page number separated by commas. Again, the parenthetical citation is placed after the closing quotation marks and before the period at the end of the sentence.

As noted in the book Junk Food, Junk Science (Epstein, 2010, p. 137), “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive.”

Here, the writer chose to mention the source title in the sentence (an optional piece of information to include) and followed the title with a parenthetical citation. Note that the parenthetical citation is placed before the comma that signals the end of the introductory phrase.

David Epstein’s book Junk Food, Junk Science (2010) pointed out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Another variation is to introduce the author and the source title in your sentence and include the publication date and page number in parentheses within the sentence or at the end of the sentence. As long as you have included the essential information, you can choose the option that works best for that particular sentence and source.

Citing a book with a single author is usually a straightforward task. Of course, your research may require that you cite many other types of sources, such as books or articles with more than one author or sources with no individual author listed. You may also need to cite sources available in both print and online and nonprint sources, such as websites and personal interviews. Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.2 “Citing and Referencing Techniques” and Section 13.3 “Creating a References Section” provide extensive guidelines for citing a variety of source types.

Writing at Work

APA is just one of several different styles with its own guidelines for documentation, formatting, and language usage. Depending on your field of interest, you may be exposed to additional styles, such as the following:

• MLA style. Determined by the Modern Languages Association and used for papers in literature, languages, and other disciplines in the humanities.
• Chicago style. Outlined in the Chicago Manual of Style and sometimes used for papers in the humanities and the sciences; many professional organizations use this style for publications as well.
• Associated Press (AP) style. Used by professional journalists.

References List

The brief citations included in the body of your paper correspond to the more detailed citations provided at the end of the paper in the references section. In-text citations provide basic information—the author’s name, the publication date, and the page number if necessary—while the references section provides more extensive bibliographical information. Again, this information allows your reader to follow up on the sources you cited and do additional reading about the topic if desired.

The specific format of entries in the list of references varies slightly for different source types, but the entries generally include the following information:

• The name(s) of the author(s) or institution that wrote the source
• The year of publication and, where applicable, the exact date of publication
• The full title of the source
• For books, the city of publication
• For articles or essays, the name of the periodical or book in which the article or essay appears
• For magazine and journal articles, the volume number, issue number, and pages where the article appears
• For sources on the web, the URL where the source is located

The references page is double spaced and lists entries in alphabetical order by the author’s last name. If an entry continues for more than one line, the second line and each subsequent line are indented five spaces. Review the following example. ( Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.3 “Creating a References Section” provides extensive guidelines for formatting reference entries for different types of sources.)

In APA style, book and article titles are formatted in sentence case, not title case. Sentence case means that only the first word is capitalized, along with any proper nouns.

Key Takeaways

• Following proper citation and formatting guidelines helps writers ensure that their work will be taken seriously, give proper credit to other authors for their work, and provide valuable information to readers.
• Working ahead and taking care to cite sources correctly the first time are ways writers can save time during the editing stage of writing a research paper.
• APA papers usually include an abstract that concisely summarizes the paper.
• APA papers use a specific headings structure to provide a clear hierarchy of information.
• In APA papers, in-text citations usually include the name(s) of the author(s) and the year of publication.
• In-text citations correspond to entries in the references section, which provide detailed bibliographical information about a source.

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Only articles that have been published or accepted by a named publication, or that have been uploaded to a recognized preprint server (for example, arXiv, bioRxiv), should be in the reference list; papers in preparation should be mentioned in the text with a list of authors (or initials if any of the authors are co-authors of the present contribution).

Published conference abstracts, numbered patents, preprints on recognized servers, papers in press, and research datasets that have been assigned a digital object identifier may be included in reference lists, but text, grant details and acknowledgements may not. (An exception is the highlighted references which we ask authors of Reviews, Perspectives and Insights articles to provide.)

All authors should be included in reference lists unless there are more than five, in which case only the first author should be given, followed by ‘et al.’.

Please follow the style below in the published edition of Nature in preparing reference lists.

Authors should be listed surname first, followed by a comma and initials of given names.

Titles of all cited articles are required. Titles of articles cited in reference lists should be in upright, not italic text; the first word of the title is capitalized, the title written exactly as it appears in the work cited, ending with a full stop. Book titles are italic with all main words capitalized. Journal titles are italic and abbreviated according to common usage. Volume numbers are bold. The publisher and city of publication are required for books cited. (Refer to published papers in Nature for details.)

Research datasets may be cited in the reference list if they have been assigned digital object identifiers (DOIs) and include authors, title, publisher (repository name), identifier (DOI expressed as a URL). Example: Hao, Z., AghaKouchak, A., Nakhjiri, N. & Farahmand, A. Global Integrated Drought Monitoring and Prediction System (GIDMaPS) data sets. figshare http://dx.doi.org/10.6084/m9.figshare.853801 (2014).

Recognized preprints may be cited in the reference list. Example: Babichev, S. A., Ries, J. & Lvovsky, A. I. Quantum scissors: teleportation of single-mode optical states by means of a nonlocal single photon. Preprint at http://arXiv.org/quant-ph/0208066 (2002).

References to web-only journals should give authors, article title and journal name as above, followed by URL in full - or DOI if known - and the year of publication in parentheses.

References to websites should give authors if known, title of cited page, URL in full, and year of posting in parentheses.

End notes are brief and follow the Methods (or Methods References, if any).

Acknowledgements should be brief, and should not include thanks to anonymous referees and editors, inessential words, or effusive comments. A person can be thanked for assistance, not “excellent” assistance, or for comments, not “insightful” comments, for example. Acknowledgements can contain grant and contribution numbers.

Author Contributions: Authors are required to include a statement to specify the contributions of each co-author. The statement can be up to several sentences long, describing the tasks of individual authors referred to by their initials. See the authorship policy page for further explanation and examples.

Competing interests  statement.

Additional Information: Authors should include a set of statements at the end of the paper, in the following order:

Papers containing Supplementary Information contain the statement: “Supplementary Information is available for this paper.”

A sentence reading "Correspondence and requests for materials should be addressed to XX.” Nature expects this identified author to respond to readers’ enquiries and requests for materials, and to coordinate the handling of any other matters arising from the published contribution, including corrections complaints. The author named as corresponding author is not necessarily the senior author, and publication of this author’s name does not imply seniority. Authors may include more than one e-mail address if essential, in which event Nature will communicate with the first-listed address for any post-publication matters, and expect that author to coordinate with the other co-authors.

Peer review information includes the names of reviewers who agree to be cited and is completed by Nature staff during proofing.

A sentence reading “Reprints and permissions information is available at www.nature.com/reprints.”

Life sciences and behavioural & social sciences reporting guidelines

To improve the transparency of reporting and the reproducibility of published results, authors of life sciences and behavioural & social sciences Articles must provide a completed Reporting Summary that will be made available to editors and reviewers during manuscript assessment. The Reporting Summary will be published with all accepted manuscripts.

Please note: because of the advanced features used in these forms, you must use Adobe Reader to open the documents and fill them out.

Guidance and resources related to the use and reporting of statistics are available here .

Tables should each be presented on a separate page, portrait (not landscape) orientation, and upright on the page, not sideways.

Tables have a short, one-line title in bold text. Tables should be as small as possible. Bear in mind the size of a Nature page as a limiting factor when compiling a table.

Symbols and abbreviations are defined immediately below the table, followed by essential descriptive material as briefly as possible, all in double-spaced text.

Standard table formats are available for submissions of cryo-EM , NMR and X-ray crystallography data . Authors providing these data must use these standard tables and include them as Extended Data.

Figure legends

For initial submissions, we encourage authors to present the manuscript text and figures together in a single Word doc or PDF file, and for each figure legend to be presented together with its figure. However, when preparing the final paper to be accepted, we require figure legends to be listed one after the other, as part of the text document, separate from the figure files, and after the main reference list.

Each figure legend should begin with a brief title for the whole figure and continue with a short description of each panel and the symbols used. If the paper contains a Methods section, legends should not contain any details of methods. Legends should be fewer than 300 words each.

All error bars and statistics must be defined in the figure legend, as discussed above.

Nature requires figures in electronic format. Please ensure that all digital images comply with the Nature journals’ policy on image integrity .

Figures should be as small and simple as is compatible with clarity. The goal is for figures to be comprehensible to readers in other or related disciplines, and to assist their understanding of the paper. Unnecessary figures and parts (panels) of figures should be avoided: data presented in small tables or histograms, for instance, can generally be stated briefly in the text instead. Avoid unnecessary complexity, colouring and excessive detail.

Figures should not contain more than one panel unless the parts are logically connected; each panel of a multipart figure should be sized so that the whole figure can be reduced by the same amount and reproduced on the printed page at the smallest size at which essential details are visible. For guidance, Nature ’s standard figure sizes are 90 mm (single column) and 180 mm (double column) and the full depth of the page is 170 mm.

Amino-acid sequences should be printed in Courier (or other monospaced) font using the one-letter code in lines of 50 or 100 characters.

Authors describing chemical structures should use the Nature Research Chemical Structures style guide .

Some brief guidance for figure preparation:

Lettering in figures (labelling of axes and so on) should be in lower-case type, with the first letter capitalized and no full stop.

Units should have a single space between the number and the unit, and follow SI nomenclature or the nomenclature common to a particular field. Thousands should be separated by commas (1,000). Unusual units or abbreviations are defined in the legend.

Scale bars should be used rather than magnification factors.

Layering type directly over shaded or textured areas and using reversed type (white lettering on a coloured background) should be avoided where possible.

Where possible, text, including keys to symbols, should be provided in the legend rather than on the figure itself.

Figure quality

At initial submission, figures should be at good enough quality to be assessed by referees, preferably incorporated into the manuscript text in a single Word doc or PDF, although figures can be supplied separately as JPEGs if authors are unable to include them with the text. Authors are advised to follow the initial and revised submissions guidelines with respect to sizing, resolution and labelling.

Please note that print-publication quality figures are large and it is not helpful to upload them at the submission stage. Authors will be asked for high-quality figures when they are asked to submit the final version of their article for publication.At that stage, please prepare figures according to these guidelines .

Third party rights

Nature discourages the use or adaptation of previously published display items (for example, figures, tables, images, videos or text boxes). However, we recognize that to illustrate some concepts the use of published data is required and the reuse of previously published display items may be necessary. Please note that in these instances we might not be able to obtain the necessary rights for some images to be reused (as is, or adapted versions) in our articles. In such cases, we will contact you to discuss the sourcing of alternative material.

Figure costs

In order to help cover some of the additional cost of four-colour reproduction, Nature Portfolio charges our authors a fee for the printing of their colour figures. Please contact our offices for exact pricing and details. Inability to pay this charge will not prevent publication of colour figures judged essential by the editors, but this must be agreed with the editor prior to acceptance.

Production-quality figures

When a manuscript is accepted in principle for publication, the editor will ask for high-resolution figures. Do not submit publication-quality figures until asked to do so by an editor. At that stage, please prepare figures according to these guidelines .

Extended Data

Extended Data figures and tables are online-only (appearing in the online PDF and full-text HTML version of the paper), peer-reviewed display items that provide essential background to the Article but are not included in the printed version of the paper due to space constraints or being of interest only to a few specialists. A maximum of ten Extended Data display items (figures and tables) is typically permitted. See Composition of a Nature research paper .

Extended Data tables should be formatted along similar lines to tables appearing in print (see section 5.7) but the main body (excluding title and legend, which should be included at the end of the Word file) should be submitted separately as an image rather than as an editable format in Word, as Extended Data tables are not edited by Nature’s subediting department. Small tables may also be included as sub-panels within Extended Data figures. See Extended Data Formatting Guide .

Extended Data figures should be prepared along slightly different guidelines compared to figures appearing in print, and may be multi-panelled as long as they fit to size rules (see Extended Data Formatting Guide ). Extended Data figures are not edited or styled by Nature’s art department; for this reason, authors are requested to follow Nature style as closely as possible when preparing these figures. The legends for Extended Data figures should be prepared as for print figures and should be listed one after the other at the end of the Word file.

If space allows, Nature encourages authors to include a simple schematic, as a panel in an Extended Data figure, that summarizes the main finding of the paper, where appropriate (for example, to assist understanding of complex detail in cell, structural and molecular biology disciplines).

If a manuscript has Extended Data figures or tables, authors are asked to refer to discrete items at an appropriate place in the main text (for example, Extended Data Fig. 1 and Extended Data Table 1).

If further references are included in the Extended Data tables and Extended Data figure legends, the numbering should continue from the end of the last reference number in the main paper (or from the last reference number in the additional Methods section if present) and the list should be added to the end of the list accompanying the additional Methods section, if present, or added below the Extended Data legends if no additional Methods section is present.

Supplementary Information

Supplementary Information (SI) is online-only, peer-reviewed material that is essential background to the Article (for example, large data sets, methods, calculations), but which is too large or impractical, or of interest only to a few specialists, to justify inclusion in the printed version of the paper. See the Supplementary Information page for further details.

Supplementary Information should not contain figures (any figures additional to those appearing in print should be formatted as Extended Data figures). Tables may be included in Supplementary Information, but only if they are unsuitable for formatting as Extended Data tables (for example, tables containing large data sets or raw data that are best suited to Excel files).

If a manuscript has accompanying SI, either at submission or in response to an editor’s letter that requests it, authors are asked to refer to discrete items of the SI (for example, videos, tables) at an appropriate point in the main manuscript.

Chemical structures and characterization of chemical materials

For guidelines describing Nature ’s standards for experimental methods and the characterization of new compounds, please see the information sheet on the characterization of chemical materials .

We aim to produce chemical structures in a consistent format throughout our articles. Please use the Nature Portfolio Chemical Structures Guide and ChemDraw template to ensure that you prepare your figures in a format that will require minimal changes by our art and production teams. Submit final files at 100% as .cdx files.

Registered Reports

Registered Reports are empirical articles testing confirmatory hypotheses in which the methods and proposed analyses are pre-registered and peer reviewed prior to research being conducted. For further details about Registered Reports and instructions for how to submit such articles to Nature please consult our Registered Reports page.

All contributions should be submitted online , unless otherwise instructed by the editors. Please be sure to read the information on what to include in your cover letter as well as several important content-related issues when putting a submission together.

Before submitting, all contributors must agree to all of Nature's publication policies .

Nature authors must make data and materials publicly available upon publication. This includes deposition of data into the relevant databases and arranging for them to be publicly released by the online publication date (not after). A description of our initiative to improve the transparency and the reproducibility of published results is available here . A full description of Nature’s publication policies is at the Nature Portfolio Authors and Referees website .

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An account of the relationship between all the Nature journals is provided at the Nature family page . 

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How to format a research paper

Last updated

7 February 2023

Reviewed by

Miroslav Damyanov

Writing a research paper can be daunting if you’re not experienced with the process. Getting the proper format is one of the most challenging aspects of the task. Reviewers will immediately dismiss a paper that doesn't comply with standard formatting, regardless of the valuable content it contains. 

In this article, we'll delve into the essential characteristics of a research paper, including the proper formatting.

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• What is a research paper?

A research paper is a document that provides a thorough analysis of a topic , usually for an academic institution or professional organization. A research paper may be of any length, but they are typically 2,000–10,000 words. 

Unlike less formal papers, such as articles or essays, empirical evidence and data are key to research papers. In addition to students handing in papers, scientists, attorneys, medical researchers, and independent scholars may need to produce research papers.

People typically write research papers to prove a particular point or make an argument. This could support or disprove a theoretical point, legal case, scientific theory, or an existing piece of research on any topic. 

One of the distinguishing characteristics of research papers is that they contain citations to prior research. Citing sources using the correct format is essential for creating a legitimate research paper. 

• Top considerations for writing a research paper

To write a research paper, you must consider several factors. Fields such as the sciences, humanities, and technical professions have certain criteria for writing research papers. 

You’ll write a research paper using one of several types of formatting. These include APA, MLA, and CMOS styles, which we’ll cover in detail to guide you on citations and other formatting rules. 

Specific requirements of the assignment

If the paper is for a college, university, or any specific organization, they’ll give you certain requirements, such as the range of topics, length, and formatting requirements.

You should study the specifics of the assignment carefully, as these will override more general guidelines you may find elsewhere. If you're writing for a particular professor, they may ask for single or double spacing or a certain citation style. 

• Components of a research paper

Here are the basic steps to writing a quality research paper, assuming you've chosen your topic and considered the requirements of the paper. Depending on the specific conditions of the paper you're writing, you may need the following elements:

Thesis statement

The thesis statement provides a blueprint for the paper. It conveys the theme and purpose of the paper. It also informs you and readers what your paper will argue and the type of research it will contain. As you write the paper, you can refer to the thesis statement to help you decide whether or not to include certain items.

Most research papers require an abstract as well as a thesis. While the thesis is a short (usually a single sentence) summary of the work, an abstract contains more detail. Many papers use the IMRaD structure for the abstract, especially in scientific fields. This consists of four elements:

Introduction : Summarize the purpose of the paper

Methods : Describe the research methods (e.g., collecting data , interviews , field research)

Results: Summarize your conclusions.  

Discussion: Discuss the implications of your research. Mention any significant limitations to your approach and suggest areas for further research.

The thesis and abstract come at the beginning of a paper, but you should write them after completing the paper. This approach ensures a clear idea of your main topic and argument, which can evolve as you write the paper.

Table of contents

Like most nonfiction books, a research paper usually includes a table of contents. 

Tables, charts, and illustrations

If your paper contains multiple tables, charts, illustrations, or other graphics, you can create a list of these. 

Works cited or reference page

This page lists all the works you cited in your paper. For MLA and APA styles, you will use in-text citations in the body of the paper. For Chicago (CMOS) style, you'll use footnotes. 

Bibliography

While you use a reference page to note all cited papers, a bibliography lists all the works you consulted in your research, even if you don't specifically cite them. 

While references are essential, a bibliography is optional but usually advisable to demonstrate the breadth of your research.

Dedication and acknowledgments

You may include a dedication or acknowledgments at the beginning of the paper directly after the title page and before the abstract.

• Steps for writing a research paper

These are the most critical steps for researching, writing, and formatting a research paper:

Create an outline

The outline is not part of the published paper; it’s for your use. An outline makes it easier to structure the paper, ensuring you include all necessary points and research. 

Here you can list all topics and subtopics that will support your argument. When doing your research, you can refer to the outline to ensure you include everything. 

Gather research

Solid research is the hallmark of a research paper. In addition to accumulating research, you need to present it clearly. However, gathering research is one of the first tasks. If you compile each piece of research correctly, it will be easier to format the paper correctly. You want to avoid having to go back and look up information constantly.

Start by skimming potentially useful sources and putting them aside for later use. Reading each source thoroughly at this stage will be time-consuming and slow your progress. You can thoroughly review the sources to decide what to include and discard later. At this stage, note essential information such as names, dates, page numbers, and website links. Citing sources will be easier when you’ve written all the information down.

Be aware of the quality of your sources. A research paper should reference scholarly, academic, or scientific journals. It’s vital to understand the difference between primary and secondary sources. 

A primary source is an original, firsthand account of a topic. A secondary source is someone else covering the topic, as in a popular article or interview. While you may include secondary sources, your paper should also include primary research . Online research can be convenient, but you need to be extra careful when assessing the quality of your sources.

Write the first draft

Create a first draft where you put together all your research and address the topic described in your thesis and abstract. 

Edit and format the paper

Proofread, edit, and make any necessary adjustments and improvements to the first draft. List your citations as described below. Ensure your thesis and abstract describe your research accurately. 

• Formatting a research paper: MLA, APA, and CMOS styles

There are several popular formats for research papers: MLA (Modern Language Association) and APA (American Psychological Association). Certain academic papers use CMOS (Chicago Manual of Style). Other formats may apply to particular fields. 

For example, medical research may use AMA (American Medical Association) formatting and IEEE (Institute of Electrical and Electronics Engineers) for particular technical papers. The following are the guidelines and examples of the most popular formats:

The humanities typically use MLA format, including literature, history, and culture. Look over examples of papers created in MLA format . Here are the main rules to keep in mind:

Double-spaced lines.

Indent new paragraphs 1/2 inch.

Title case for headings, where all major words are capitalized, as in "How to Write a Research Paper." 

Use a popular font such as Times New Roman. This applies to all formatting styles.

Use one-inch margins on all sides. 

Number sections of the paper using Arabic numerals (1, 2, 3, etc.). 

Use a running head for each page on the upper right-hand corner, which consists of your last name and the page number.

Use an in-text citation within the text, using the author's last name followed by the page number: "Anything worth dying for is certainly worth living for" (Heller 155).  

On the citations page, list the full name, book or periodical, and other information. For MLA, you will not need footnotes, only in-text citations.

List citations in alphabetical order on a separate page at the end of the paper entitled “Works Cited.” 

Continuing with the above example from Heller, the listing would be: Heller, Joseph. Catch-22, Simon & Schuster, 1961.

For a periodical, the format is "Thompson, Hunter S. "The Kentucky Derby is Decadent and Depraved" Scanlon's, June 1970."

Use title case for source titles, as in "On the Origin of Species."

The sciences typically use APA format, including physical sciences such as physics and social sciences such as psychology. Simply Psychology provides examples of APA formatting . The following are the most important rules of the APA format.

Begin the paper with a title page, which is not required for MLA.

Use double-line spacing.

Use a running head for each page in the upper right-hand corner, which consists of the paper's title in capital letters followed by the page number.

The citations page at the end should be titled "References."

In-text citations should include the publication date: (Smith, 1999, p. 50). Note also that there's a "p" for "page," whereas in MLA, you write the page number without a "p."

As with MLA, use title case for headings, as in "Most Popular Treatments for Cognitive Disorders."

Use sentence case for titles of sources, as in "History of the decline and fall of the Roman empire." Note "Roman" starts with a capital because it's a proper noun.  

When citing in-text references, use the author's last name and the first and middle initials. 

Always use the Oxford comma. This comma goes before the words "or" and "and" in a list. For example, "At the store, I bought oranges, paper towels, and pasta."

CMOS formatting

Book publishers and many academic papers use CMOS formatting based on the Chicago Manual of Style. CMOS is also called Turabian, named after Kate L. Turabian, who wrote the first manual for this style. Here are examples of CMOS style formatting and citations.

Include an unnumbered title page.

Place page numbers on the upper right-hand corner of the page. Do not list your name or the paper's title as you would for MLA or APA styles.

Use title case for both headings and sources (same as MLA).

Unlike MLA and APA, the Chicago style uses footnotes for citations. Use a superscript for footnotes: "Smith argues against Jones' theory¹.” Footnotes may appear at the bottom of the page or the end of the document.  

CMOS supports both short notes and full notes. In most cases, you'll use the full note: "Michael Pollan, The Omnivore's Dilemma: A Natural History of Four Meals (New York: Penguin, 2006), 76." For further references to the same source, use a short note: " Pollan, Omnivore's Dilemma, 45." The requirements of some papers may specify using only short notes for all footnotes.

• General guidelines for writing and formatting research papers

Keep these guidelines in mind for all types of research papers:

Initial formatting

As you create your first draft, don't worry about formatting. If you try to format it perfectly as you write the paper, it will be difficult to progress and develop a flow of thought. With the first draft, you don't have to be concerned about ordering the sections. You can rearrange headings and sections later. 

Citation tools

Use automation tools for citations . Some useful tools make citations easier by automatically generating a citation list and bibliography. Many work with APA, MLA, and CMOS styles.

Check for plagiarism

Use a plagiarism detector to make sure your paper isn't unintentionally plagiarizing. There are many free and paid plagiarism checkers online, such as Grammarly. 

Proofread your work

Do several rounds of editing and proofreading. Editing is necessary for any type of writing, but you’ll need to revisit several distinct areas with a research paper:

Check for spelling and grammatical errors.

Read the paper to make sure it's well-argued and that you’ve organized it properly. 

Check that you’ve correctly formatted citations. It's easy to make errors, such as incorrect numbering of footnotes (e.g., Chicago style) or forgetting to include a source on your citations page.

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How to Write and Publish a Research Paper for a Peer-Reviewed Journal

• Open access
• Published: 30 April 2020
• Volume 36 , pages 909–913, ( 2021 )

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• Clara Busse   ORCID: orcid.org/0000-0002-0178-1000 1 &
• Ella August   ORCID: orcid.org/0000-0001-5151-1036 1 , 2  

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Communicating research findings is an essential step in the research process. Often, peer-reviewed journals are the forum for such communication, yet many researchers are never taught how to write a publishable scientific paper. In this article, we explain the basic structure of a scientific paper and describe the information that should be included in each section. We also identify common pitfalls for each section and recommend strategies to avoid them. Further, we give advice about target journal selection and authorship. In the online resource 1 , we provide an example of a high-quality scientific paper, with annotations identifying the elements we describe in this article.

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Avoid common mistakes on your manuscript.

Introduction

Writing a scientific paper is an important component of the research process, yet researchers often receive little formal training in scientific writing. This is especially true in low-resource settings. In this article, we explain why choosing a target journal is important, give advice about authorship, provide a basic structure for writing each section of a scientific paper, and describe common pitfalls and recommendations for each section. In the online resource 1 , we also include an annotated journal article that identifies the key elements and writing approaches that we detail here. Before you begin your research, make sure you have ethical clearance from all relevant ethical review boards.

Select a Target Journal Early in the Writing Process

We recommend that you select a “target journal” early in the writing process; a “target journal” is the journal to which you plan to submit your paper. Each journal has a set of core readers and you should tailor your writing to this readership. For example, if you plan to submit a manuscript about vaping during pregnancy to a pregnancy-focused journal, you will need to explain what vaping is because readers of this journal may not have a background in this topic. However, if you were to submit that same article to a tobacco journal, you would not need to provide as much background information about vaping.

Information about a journal’s core readership can be found on its website, usually in a section called “About this journal” or something similar. For example, the Journal of Cancer Education presents such information on the “Aims and Scope” page of its website, which can be found here: https://www.springer.com/journal/13187/aims-and-scope .

Peer reviewer guidelines from your target journal are an additional resource that can help you tailor your writing to the journal and provide additional advice about crafting an effective article [ 1 ]. These are not always available, but it is worth a quick web search to find out.

Identify Author Roles Early in the Process

Early in the writing process, identify authors, determine the order of authors, and discuss the responsibilities of each author. Standard author responsibilities have been identified by The International Committee of Medical Journal Editors (ICMJE) [ 2 ]. To set clear expectations about each team member’s responsibilities and prevent errors in communication, we also suggest outlining more detailed roles, such as who will draft each section of the manuscript, write the abstract, submit the paper electronically, serve as corresponding author, and write the cover letter. It is best to formalize this agreement in writing after discussing it, circulating the document to the author team for approval. We suggest creating a title page on which all authors are listed in the agreed-upon order. It may be necessary to adjust authorship roles and order during the development of the paper. If a new author order is agreed upon, be sure to update the title page in the manuscript draft.

In the case where multiple papers will result from a single study, authors should discuss who will author each paper. Additionally, authors should agree on a deadline for each paper and the lead author should take responsibility for producing an initial draft by this deadline.

Structure of the Introduction Section

The introduction section should be approximately three to five paragraphs in length. Look at examples from your target journal to decide the appropriate length. This section should include the elements shown in Fig.  1 . Begin with a general context, narrowing to the specific focus of the paper. Include five main elements: why your research is important, what is already known about the topic, the “gap” or what is not yet known about the topic, why it is important to learn the new information that your research adds, and the specific research aim(s) that your paper addresses. Your research aim should address the gap you identified. Be sure to add enough background information to enable readers to understand your study. Table 1 provides common introduction section pitfalls and recommendations for addressing them.

The main elements of the introduction section of an original research article. Often, the elements overlap

Methods Section

The purpose of the methods section is twofold: to explain how the study was done in enough detail to enable its replication and to provide enough contextual detail to enable readers to understand and interpret the results. In general, the essential elements of a methods section are the following: a description of the setting and participants, the study design and timing, the recruitment and sampling, the data collection process, the dataset, the dependent and independent variables, the covariates, the analytic approach for each research objective, and the ethical approval. The hallmark of an exemplary methods section is the justification of why each method was used. Table 2 provides common methods section pitfalls and recommendations for addressing them.

Results Section

The focus of the results section should be associations, or lack thereof, rather than statistical tests. Two considerations should guide your writing here. First, the results should present answers to each part of the research aim. Second, return to the methods section to ensure that the analysis and variables for each result have been explained.

Begin the results section by describing the number of participants in the final sample and details such as the number who were approached to participate, the proportion who were eligible and who enrolled, and the number of participants who dropped out. The next part of the results should describe the participant characteristics. After that, you may organize your results by the aim or by putting the most exciting results first. Do not forget to report your non-significant associations. These are still findings.

Tables and figures capture the reader’s attention and efficiently communicate your main findings [ 3 ]. Each table and figure should have a clear message and should complement, rather than repeat, the text. Tables and figures should communicate all salient details necessary for a reader to understand the findings without consulting the text. Include information on comparisons and tests, as well as information about the sample and timing of the study in the title, legend, or in a footnote. Note that figures are often more visually interesting than tables, so if it is feasible to make a figure, make a figure. To avoid confusing the reader, either avoid abbreviations in tables and figures, or define them in a footnote. Note that there should not be citations in the results section and you should not interpret results here. Table 3 provides common results section pitfalls and recommendations for addressing them.

Discussion Section

Opposite the introduction section, the discussion should take the form of a right-side-up triangle beginning with interpretation of your results and moving to general implications (Fig.  2 ). This section typically begins with a restatement of the main findings, which can usually be accomplished with a few carefully-crafted sentences.

Major elements of the discussion section of an original research article. Often, the elements overlap

Next, interpret the meaning or explain the significance of your results, lifting the reader’s gaze from the study’s specific findings to more general applications. Then, compare these study findings with other research. Are these findings in agreement or disagreement with those from other studies? Does this study impart additional nuance to well-accepted theories? Situate your findings within the broader context of scientific literature, then explain the pathways or mechanisms that might give rise to, or explain, the results.

Journals vary in their approach to strengths and limitations sections: some are embedded paragraphs within the discussion section, while some mandate separate section headings. Keep in mind that every study has strengths and limitations. Candidly reporting yours helps readers to correctly interpret your research findings.

The next element of the discussion is a summary of the potential impacts and applications of the research. Should these results be used to optimally design an intervention? Does the work have implications for clinical protocols or public policy? These considerations will help the reader to further grasp the possible impacts of the presented work.

Finally, the discussion should conclude with specific suggestions for future work. Here, you have an opportunity to illuminate specific gaps in the literature that compel further study. Avoid the phrase “future research is necessary” because the recommendation is too general to be helpful to readers. Instead, provide substantive and specific recommendations for future studies. Table 4 provides common discussion section pitfalls and recommendations for addressing them.

Follow the Journal’s Author Guidelines

After you select a target journal, identify the journal’s author guidelines to guide the formatting of your manuscript and references. Author guidelines will often (but not always) include instructions for titles, cover letters, and other components of a manuscript submission. Read the guidelines carefully. If you do not follow the guidelines, your article will be sent back to you.

Finally, do not submit your paper to more than one journal at a time. Even if this is not explicitly stated in the author guidelines of your target journal, it is considered inappropriate and unprofessional.

Your title should invite readers to continue reading beyond the first page [ 4 , 5 ]. It should be informative and interesting. Consider describing the independent and dependent variables, the population and setting, the study design, the timing, and even the main result in your title. Because the focus of the paper can change as you write and revise, we recommend you wait until you have finished writing your paper before composing the title.

Be sure that the title is useful for potential readers searching for your topic. The keywords you select should complement those in your title to maximize the likelihood that a researcher will find your paper through a database search. Avoid using abbreviations in your title unless they are very well known, such as SNP, because it is more likely that someone will use a complete word rather than an abbreviation as a search term to help readers find your paper.

After you have written a complete draft, use the checklist (Fig. 3 ) below to guide your revisions and editing. Additional resources are available on writing the abstract and citing references [ 5 ]. When you feel that your work is ready, ask a trusted colleague or two to read the work and provide informal feedback. The box below provides a checklist that summarizes the key points offered in this article.

Checklist for manuscript quality

Data Availability

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Vetto JT (2014) Short and sweet: a short course on concise medical writing. J Cancer Educ 29(1):194–195

Brett M, Kording K (2017) Ten simple rules for structuring papers. PLoS ComputBiol. https://doi.org/10.1371/journal.pcbi.1005619

Lang TA (2017) Writing a better research article. J Public Health Emerg. https://doi.org/10.21037/jphe.2017.11.06

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Ella August is grateful to the Sustainable Sciences Institute for mentoring her in training researchers on writing and publishing their research.

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Home » Research Paper Format – Types, Examples and Templates

Research Paper Format – Types, Examples and Templates

Table of Contents

Research paper format is an essential aspect of academic writing that plays a crucial role in the communication of research findings . The format of a research paper depends on various factors such as the discipline, style guide, and purpose of the research. It includes guidelines for the structure, citation style, referencing , and other elements of the paper that contribute to its overall presentation and coherence. Adhering to the appropriate research paper format is vital for ensuring that the research is accurately and effectively communicated to the intended audience. In this era of information, it is essential to understand the different research paper formats and their guidelines to communicate research effectively, accurately, and with the required level of detail. This post aims to provide an overview of some of the common research paper formats used in academic writing.

Research Paper Formats

Research Paper Formats are as follows:

• APA (American Psychological Association) format
• MLA (Modern Language Association) format
• Chicago/Turabian style
• IEEE (Institute of Electrical and Electronics Engineers) format
• AMA (American Medical Association) style
• Harvard style
• Vancouver style
• ACS (American Chemical Society) style
• ASA (American Sociological Association) style
• APSA (American Political Science Association) style

APA (American Psychological Association) Format

Here is a general APA format for a research paper:

• Title Page: The title page should include the title of your paper, your name, and your institutional affiliation. It should also include a running head, which is a shortened version of the title, and a page number in the upper right-hand corner.
• Abstract : The abstract is a brief summary of your paper, typically 150-250 words. It should include the purpose of your research, the main findings, and any implications or conclusions that can be drawn.
• Introduction: The introduction should provide background information on your topic, state the purpose of your research, and present your research question or hypothesis. It should also include a brief literature review that discusses previous research on your topic.
• Methods: The methods section should describe the procedures you used to collect and analyze your data. It should include information on the participants, the materials and instruments used, and the statistical analyses performed.
• Results: The results section should present the findings of your research in a clear and concise manner. Use tables and figures to help illustrate your results.
• Discussion : The discussion section should interpret your results and relate them back to your research question or hypothesis. It should also discuss the implications of your findings and any limitations of your study.
• References : The references section should include a list of all sources cited in your paper. Follow APA formatting guidelines for your citations and references.

Some additional tips for formatting your APA research paper:

• Use 12-point Times New Roman font throughout the paper.
• Double-space all text, including the references.
• Use 1-inch margins on all sides of the page.
• Indent the first line of each paragraph by 0.5 inches.
• Use a hanging indent for the references (the first line should be flush with the left margin, and all subsequent lines should be indented).
• Number all pages, including the title page and references page, in the upper right-hand corner.

APA Research Paper Format Template

APA Research Paper Format Template is as follows:

Title Page:

• Title of the paper
• Author’s name
• Institutional affiliation
• A brief summary of the main points of the paper, including the research question, methods, findings, and conclusions. The abstract should be no more than 250 words.

Introduction:

• Background information on the topic of the research paper
• Research question or hypothesis
• Significance of the study
• Overview of the research methods and design
• Brief summary of the main findings
• Participants: description of the sample population, including the number of participants and their characteristics (age, gender, ethnicity, etc.)
• Materials: description of any materials used in the study (e.g., survey questions, experimental apparatus)
• Procedure: detailed description of the steps taken to conduct the study
• Presentation of the findings of the study, including statistical analyses if applicable
• Tables and figures may be included to illustrate the results

Discussion:

• Interpretation of the results in light of the research question and hypothesis
• Implications of the study for the field
• Limitations of the study
• Suggestions for future research

References:

• A list of all sources cited in the paper, in APA format

Formatting guidelines:

• Double-spaced
• 12-point font (Times New Roman or Arial)
• 1-inch margins on all sides
• Page numbers in the top right corner
• Headings and subheadings should be used to organize the paper
• The first line of each paragraph should be indented
• Quotations of 40 or more words should be set off in a block quote with no quotation marks
• In-text citations should include the author’s last name and year of publication (e.g., Smith, 2019)

APA Research Paper Format Example

APA Research Paper Format Example is as follows:

The Effects of Social Media on Mental Health

University of XYZ

This study examines the relationship between social media use and mental health among college students. Data was collected through a survey of 500 students at the University of XYZ. Results suggest that social media use is significantly related to symptoms of depression and anxiety, and that the negative effects of social media are greater among frequent users.

Social media has become an increasingly important aspect of modern life, especially among young adults. While social media can have many positive effects, such as connecting people across distances and sharing information, there is growing concern about its impact on mental health. This study aims to examine the relationship between social media use and mental health among college students.

Participants: Participants were 500 college students at the University of XYZ, recruited through online advertisements and flyers posted on campus. Participants ranged in age from 18 to 25, with a mean age of 20.5 years. The sample was 60% female, 40% male, and 5% identified as non-binary or gender non-conforming.

Data was collected through an online survey administered through Qualtrics. The survey consisted of several measures, including the Patient Health Questionnaire-9 (PHQ-9) for depression symptoms, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety symptoms, and questions about social media use.

Procedure :

Participants were asked to complete the online survey at their convenience. The survey took approximately 20-30 minutes to complete. Data was analyzed using descriptive statistics, correlations, and multiple regression analysis.

Results indicated that social media use was significantly related to symptoms of depression (r = .32, p < .001) and anxiety (r = .29, p < .001). Regression analysis indicated that frequency of social media use was a significant predictor of both depression symptoms (β = .24, p < .001) and anxiety symptoms (β = .20, p < .001), even when controlling for age, gender, and other relevant factors.

The results of this study suggest that social media use is associated with symptoms of depression and anxiety among college students. The negative effects of social media are greater among frequent users. These findings have important implications for mental health professionals and educators, who should consider addressing the potential negative effects of social media use in their work with young adults.

References :

References should be listed in alphabetical order according to the author’s last name. For example:

• Chou, H. T. G., & Edge, N. (2012). “They are happier and having better lives than I am”: The impact of using Facebook on perceptions of others’ lives. Cyberpsychology, Behavior, and Social Networking, 15(2), 117-121.
• Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6(1), 3-17.

Note: This is just a sample Example do not use this in your assignment.

MLA (Modern Language Association) Format

MLA (Modern Language Association) Format is as follows:

• Page Layout : Use 8.5 x 11-inch white paper, with 1-inch margins on all sides. The font should be 12-point Times New Roman or a similar serif font.
• Heading and Title : The first page of your research paper should include a heading and a title. The heading should include your name, your instructor’s name, the course title, and the date. The title should be centered and in title case (capitalizing the first letter of each important word).
• In-Text Citations : Use parenthetical citations to indicate the source of your information. The citation should include the author’s last name and the page number(s) of the source. For example: (Smith 23).
• Works Cited Page : At the end of your paper, include a Works Cited page that lists all the sources you used in your research. Each entry should include the author’s name, the title of the work, the publication information, and the medium of publication.
• Formatting Quotations : Use double quotation marks for short quotations and block quotations for longer quotations. Indent the entire quotation five spaces from the left margin.
• Formatting the Body : Use a clear and readable font and double-space your text throughout. The first line of each paragraph should be indented one-half inch from the left margin.

MLA Research Paper Template

MLA Research Paper Format Template is as follows:

• Use 8.5 x 11 inch white paper.
• Use a 12-point font, such as Times New Roman.
• Use double-spacing throughout the entire paper, including the title page and works cited page.
• Set the margins to 1 inch on all sides.
• Use page numbers in the upper right corner, beginning with the first page of text.
• Include a centered title for the research paper, using title case (capitalizing the first letter of each important word).
• Include your name, instructor’s name, course name, and date in the upper left corner, double-spaced.

In-Text Citations

• When quoting or paraphrasing information from sources, include an in-text citation within the text of your paper.
• Use the author’s last name and the page number in parentheses at the end of the sentence, before the punctuation mark.
• If the author’s name is mentioned in the sentence, only include the page number in parentheses.

Works Cited Page

• List all sources cited in alphabetical order by the author’s last name.
• Each entry should include the author’s name, title of the work, publication information, and medium of publication.
• Use italics for book and journal titles, and quotation marks for article and chapter titles.
• For online sources, include the date of access and the URL.

Here is an example of how the first page of a research paper in MLA format should look:

Headings and Subheadings

• Use headings and subheadings to organize your paper and make it easier to read.
• Use numerals to number your headings and subheadings (e.g. 1, 2, 3), and capitalize the first letter of each word.
• The main heading should be centered and in boldface type, while subheadings should be left-aligned and in italics.
• Use only one space after each period or punctuation mark.
• Use quotation marks to indicate direct quotes from a source.
• If the quote is more than four lines, format it as a block quote, indented one inch from the left margin and without quotation marks.
• Use ellipses (…) to indicate omitted words from a quote, and brackets ([…]) to indicate added words.

Works Cited Examples

• Book: Last Name, First Name. Title of Book. Publisher, Publication Year.
• Journal Article: Last Name, First Name. “Title of Article.” Title of Journal, volume number, issue number, publication date, page numbers.
• Website: Last Name, First Name. “Title of Webpage.” Title of Website, publication date, URL. Accessed date.

Here is an example of how a works cited entry for a book should look:

Smith, John. The Art of Writing Research Papers. Penguin, 2021.

MLA Research Paper Example

MLA Research Paper Format Example is as follows:

Your Professor’s Name

Course Name and Number

Date (in Day Month Year format)

Word Count (not including title page or Works Cited)

Title: The Impact of Video Games on Aggression Levels

Video games have become a popular form of entertainment among people of all ages. However, the impact of video games on aggression levels has been a subject of debate among scholars and researchers. While some argue that video games promote aggression and violent behavior, others argue that there is no clear link between video games and aggression levels. This research paper aims to explore the impact of video games on aggression levels among young adults.

Background:

The debate on the impact of video games on aggression levels has been ongoing for several years. According to the American Psychological Association, exposure to violent media, including video games, can increase aggression levels in children and adolescents. However, some researchers argue that there is no clear evidence to support this claim. Several studies have been conducted to examine the impact of video games on aggression levels, but the results have been mixed.

Methodology:

This research paper used a quantitative research approach to examine the impact of video games on aggression levels among young adults. A sample of 100 young adults between the ages of 18 and 25 was selected for the study. The participants were asked to complete a questionnaire that measured their aggression levels and their video game habits.

The results of the study showed that there was a significant correlation between video game habits and aggression levels among young adults. The participants who reported playing violent video games for more than 5 hours per week had higher aggression levels than those who played less than 5 hours per week. The study also found that male participants were more likely to play violent video games and had higher aggression levels than female participants.

The findings of this study support the claim that video games can increase aggression levels among young adults. However, it is important to note that the study only examined the impact of video games on aggression levels and did not take into account other factors that may contribute to aggressive behavior. It is also important to note that not all video games promote violence and aggression, and some games may have a positive impact on cognitive and social skills.

Conclusion :

In conclusion, this research paper provides evidence to support the claim that video games can increase aggression levels among young adults. However, it is important to conduct further research to examine the impact of video games on other aspects of behavior and to explore the potential benefits of video games. Parents and educators should be aware of the potential impact of video games on aggression levels and should encourage young adults to engage in a variety of activities that promote cognitive and social skills.

Works Cited:

• American Psychological Association. (2017). Violent Video Games: Myths, Facts, and Unanswered Questions. Retrieved from https://www.apa.org/news/press/releases/2017/08/violent-video-games
• Ferguson, C. J. (2015). Do Angry Birds make for angry children? A meta-analysis of video game influences on children’s and adolescents’ aggression, mental health, prosocial behavior, and academic performance. Perspectives on Psychological Science, 10(5), 646-666.
• Gentile, D. A., Swing, E. L., Lim, C. G., & Khoo, A. (2012). Video game playing, attention problems, and impulsiveness: Evidence of bidirectional causality. Psychology of Popular Media Culture, 1(1), 62-70.
• Greitemeyer, T. (2014). Effects of prosocial video games on prosocial behavior. Journal of Personality and Social Psychology, 106(4), 530-548.

Chicago/Turabian Style

Chicago/Turabian Formate is as follows:

• Margins : Use 1-inch margins on all sides of the paper.
• Font : Use a readable font such as Times New Roman or Arial, and use a 12-point font size.
• Page numbering : Number all pages in the upper right-hand corner, beginning with the first page of text. Use Arabic numerals.
• Title page: Include a title page with the title of the paper, your name, course title and number, instructor’s name, and the date. The title should be centered on the page and in title case (capitalize the first letter of each word).
• Headings: Use headings to organize your paper. The first level of headings should be centered and in boldface or italics. The second level of headings should be left-aligned and in boldface or italics. Use as many levels of headings as necessary to organize your paper.
• In-text citations : Use footnotes or endnotes to cite sources within the text of your paper. The first citation for each source should be a full citation, and subsequent citations can be shortened. Use superscript numbers to indicate footnotes or endnotes.
• Bibliography : Include a bibliography at the end of your paper, listing all sources cited in your paper. The bibliography should be in alphabetical order by the author’s last name, and each entry should include the author’s name, title of the work, publication information, and date of publication.
• Formatting of quotations: Use block quotations for quotations that are longer than four lines. Indent the entire quotation one inch from the left margin, and do not use quotation marks. Single-space the quotation, and double-space between paragraphs.
• Tables and figures: Use tables and figures to present data and illustrations. Number each table and figure sequentially, and provide a brief title for each. Place tables and figures as close as possible to the text that refers to them.
• Spelling and grammar : Use correct spelling and grammar throughout your paper. Proofread carefully for errors.

Chicago/Turabian Research Paper Template

Chicago/Turabian Research Paper Template is as folows:

Title of Paper

Name of Student

Professor’s Name

I. Introduction

A. Background Information

B. Research Question

C. Thesis Statement

II. Literature Review

A. Overview of Existing Literature

B. Analysis of Key Literature

C. Identification of Gaps in Literature

III. Methodology

A. Research Design

B. Data Collection

C. Data Analysis

IV. Results

A. Presentation of Findings

B. Analysis of Findings

C. Discussion of Implications

V. Conclusion

A. Summary of Findings

B. Implications for Future Research

C. Conclusion

VI. References

A. Bibliography

B. In-Text Citations

VII. Appendices (if necessary)

A. Data Tables

C. Additional Supporting Materials

Chicago/Turabian Research Paper Example

Title: The Impact of Social Media on Political Engagement

Name: John Smith

Class: POLS 101

Professor: Dr. Jane Doe

Date: April 8, 2023

I. Introduction:

Social media has become an integral part of our daily lives. People use social media platforms like Facebook, Twitter, and Instagram to connect with friends and family, share their opinions, and stay informed about current events. With the rise of social media, there has been a growing interest in understanding its impact on various aspects of society, including political engagement. In this paper, I will examine the relationship between social media use and political engagement, specifically focusing on how social media influences political participation and political attitudes.

II. Literature Review:

There is a growing body of literature on the impact of social media on political engagement. Some scholars argue that social media has a positive effect on political participation by providing new channels for political communication and mobilization (Delli Carpini & Keeter, 1996; Putnam, 2000). Others, however, suggest that social media can have a negative impact on political engagement by creating filter bubbles that reinforce existing beliefs and discourage political dialogue (Pariser, 2011; Sunstein, 2001).

III. Methodology:

To examine the relationship between social media use and political engagement, I conducted a survey of 500 college students. The survey included questions about social media use, political participation, and political attitudes. The data was analyzed using descriptive statistics and regression analysis.

Iv. Results:

The results of the survey indicate that social media use is positively associated with political participation. Specifically, respondents who reported using social media to discuss politics were more likely to have participated in a political campaign, attended a political rally, or contacted a political representative. Additionally, social media use was found to be associated with more positive attitudes towards political engagement, such as increased trust in government and belief in the effectiveness of political action.

V. Conclusion:

The findings of this study suggest that social media has a positive impact on political engagement, by providing new opportunities for political communication and mobilization. However, there is also a need for caution, as social media can also create filter bubbles that reinforce existing beliefs and discourage political dialogue. Future research should continue to explore the complex relationship between social media and political engagement, and develop strategies to harness the potential benefits of social media while mitigating its potential negative effects.

Vii. References:

• Delli Carpini, M. X., & Keeter, S. (1996). What Americans know about politics and why it matters. Yale University Press.
• Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin.
• Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon & Schuster.
• Sunstein, C. R. (2001). Republic.com. Princeton University Press.

IEEE (Institute of Electrical and Electronics Engineers) Format

IEEE (Institute of Electrical and Electronics Engineers) Research Paper Format is as follows:

• Title : A concise and informative title that accurately reflects the content of the paper.
• Abstract : A brief summary of the paper, typically no more than 250 words, that includes the purpose of the study, the methods used, the key findings, and the main conclusions.
• Introduction : An overview of the background, context, and motivation for the research, including a clear statement of the problem being addressed and the objectives of the study.
• Literature review: A critical analysis of the relevant research and scholarship on the topic, including a discussion of any gaps or limitations in the existing literature.
• Methodology : A detailed description of the methods used to collect and analyze data, including any experiments or simulations, data collection instruments or procedures, and statistical analyses.
• Results : A clear and concise presentation of the findings, including any relevant tables, graphs, or figures.
• Discussion : A detailed interpretation of the results, including a comparison of the findings with previous research, a discussion of the implications of the results, and any recommendations for future research.
• Conclusion : A summary of the key findings and main conclusions of the study.
• References : A list of all sources cited in the paper, formatted according to IEEE guidelines.

In addition to these elements, an IEEE research paper should also follow certain formatting guidelines, including using 12-point font, double-spaced text, and numbered headings and subheadings. Additionally, any tables, figures, or equations should be clearly labeled and referenced in the text.

AMA (American Medical Association) Style

AMA (American Medical Association) Style Research Paper Format:

• Title Page: This page includes the title of the paper, the author’s name, institutional affiliation, and any acknowledgments or disclaimers.
• Abstract: The abstract is a brief summary of the paper that outlines the purpose, methods, results, and conclusions of the study. It is typically limited to 250 words or less.
• Introduction: The introduction provides a background of the research problem, defines the research question, and outlines the objectives and hypotheses of the study.
• Methods: The methods section describes the research design, participants, procedures, and instruments used to collect and analyze data.
• Results: The results section presents the findings of the study in a clear and concise manner, using graphs, tables, and charts where appropriate.
• Discussion: The discussion section interprets the results, explains their significance, and relates them to previous research in the field.
• Conclusion: The conclusion summarizes the main points of the paper, discusses the implications of the findings, and suggests future research directions.
• References: The reference list includes all sources cited in the paper, listed in alphabetical order by author’s last name.

In addition to these sections, the AMA format requires that authors follow specific guidelines for citing sources in the text and formatting their references. The AMA style uses a superscript number system for in-text citations and provides specific formats for different types of sources, such as books, journal articles, and websites.

Harvard Style

Harvard Style Research Paper format is as follows:

• Title page: This should include the title of your paper, your name, the name of your institution, and the date of submission.
• Abstract : This is a brief summary of your paper, usually no more than 250 words. It should outline the main points of your research and highlight your findings.
• Introduction : This section should introduce your research topic, provide background information, and outline your research question or thesis statement.
• Literature review: This section should review the relevant literature on your topic, including previous research studies, academic articles, and other sources.
• Methodology : This section should describe the methods you used to conduct your research, including any data collection methods, research instruments, and sampling techniques.
• Results : This section should present your findings in a clear and concise manner, using tables, graphs, and other visual aids if necessary.
• Discussion : This section should interpret your findings and relate them to the broader research question or thesis statement. You should also discuss the implications of your research and suggest areas for future study.
• Conclusion : This section should summarize your main findings and provide a final statement on the significance of your research.
• References : This is a list of all the sources you cited in your paper, presented in alphabetical order by author name. Each citation should include the author’s name, the title of the source, the publication date, and other relevant information.

In addition to these sections, a Harvard Style research paper may also include a table of contents, appendices, and other supplementary materials as needed. It is important to follow the specific formatting guidelines provided by your instructor or academic institution when preparing your research paper in Harvard Style.

Vancouver Style

Vancouver Style Research Paper format is as follows:

The Vancouver citation style is commonly used in the biomedical sciences and is known for its use of numbered references. Here is a basic format for a research paper using the Vancouver citation style:

• Title page: Include the title of your paper, your name, the name of your institution, and the date.
• Abstract : This is a brief summary of your research paper, usually no more than 250 words.
• Introduction : Provide some background information on your topic and state the purpose of your research.
• Methods : Describe the methods you used to conduct your research, including the study design, data collection, and statistical analysis.
• Results : Present your findings in a clear and concise manner, using tables and figures as needed.
• Discussion : Interpret your results and explain their significance. Also, discuss any limitations of your study and suggest directions for future research.
• References : List all of the sources you cited in your paper in numerical order. Each reference should include the author’s name, the title of the article or book, the name of the journal or publisher, the year of publication, and the page numbers.

ACS (American Chemical Society) Style

ACS (American Chemical Society) Style Research Paper format is as follows:

The American Chemical Society (ACS) Style is a citation style commonly used in chemistry and related fields. When formatting a research paper in ACS Style, here are some guidelines to follow:

• Paper Size and Margins : Use standard 8.5″ x 11″ paper with 1-inch margins on all sides.
• Font: Use a 12-point serif font (such as Times New Roman) for the main text. The title should be in bold and a larger font size.
• Title Page : The title page should include the title of the paper, the authors’ names and affiliations, and the date of submission. The title should be centered on the page and written in bold font. The authors’ names should be centered below the title, followed by their affiliations and the date.
• Abstract : The abstract should be a brief summary of the paper, no more than 250 words. It should be on a separate page and include the title of the paper, the authors’ names and affiliations, and the text of the abstract.
• Main Text : The main text should be organized into sections with headings that clearly indicate the content of each section. The introduction should provide background information and state the research question or hypothesis. The methods section should describe the procedures used in the study. The results section should present the findings of the study, and the discussion section should interpret the results and provide conclusions.
• References: Use the ACS Style guide to format the references cited in the paper. In-text citations should be numbered sequentially throughout the text and listed in numerical order at the end of the paper.
• Figures and Tables: Figures and tables should be numbered sequentially and referenced in the text. Each should have a descriptive caption that explains its content. Figures should be submitted in a high-quality electronic format.
• Supporting Information: Additional information such as data, graphs, and videos may be included as supporting information. This should be included in a separate file and referenced in the main text.
• Acknowledgments : Acknowledge any funding sources or individuals who contributed to the research.

ASA (American Sociological Association) Style

ASA (American Sociological Association) Style Research Paper format is as follows:

• Title Page: The title page of an ASA style research paper should include the title of the paper, the author’s name, and the institutional affiliation. The title should be centered and should be in title case (the first letter of each major word should be capitalized).
• Abstract: An abstract is a brief summary of the paper that should appear on a separate page immediately following the title page. The abstract should be no more than 200 words in length and should summarize the main points of the paper.
• Main Body: The main body of the paper should begin on a new page following the abstract page. The paper should be double-spaced, with 1-inch margins on all sides, and should be written in 12-point Times New Roman font. The main body of the paper should include an introduction, a literature review, a methodology section, results, and a discussion.
• References : The reference section should appear on a separate page at the end of the paper. All sources cited in the paper should be listed in alphabetical order by the author’s last name. Each reference should include the author’s name, the title of the work, the publication information, and the date of publication.
• Appendices : Appendices are optional and should only be included if they contain information that is relevant to the study but too lengthy to be included in the main body of the paper. If you include appendices, each one should be labeled with a letter (e.g., Appendix A, Appendix B, etc.) and should be referenced in the main body of the paper.

APSA (American Political Science Association) Style

APSA (American Political Science Association) Style Research Paper format is as follows:

• Title Page: The title page should include the title of the paper, the author’s name, the name of the course or instructor, and the date.
• Abstract : An abstract is typically not required in APSA style papers, but if one is included, it should be brief and summarize the main points of the paper.
• Introduction : The introduction should provide an overview of the research topic, the research question, and the main argument or thesis of the paper.
• Literature Review : The literature review should summarize the existing research on the topic and provide a context for the research question.
• Methods : The methods section should describe the research methods used in the paper, including data collection and analysis.
• Results : The results section should present the findings of the research.
• Discussion : The discussion section should interpret the results and connect them back to the research question and argument.
• Conclusion : The conclusion should summarize the main findings and implications of the research.
• References : The reference list should include all sources cited in the paper, formatted according to APSA style guidelines.

In-text citations in APSA style use parenthetical citation, which includes the author’s last name, publication year, and page number(s) if applicable. For example, (Smith 2010, 25).

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Reference List: Articles in Periodicals

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Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

Note:  This page reflects the latest version of the APA Publication Manual (i.e., APA 7), which released in October 2019. The equivalent resource for the older APA 6 style  can be found here .

Please note: the following contains a list of the most commonly cited periodical sources. For a complete list of how to cite periodical publications, please refer to the 7 th edition of the APA Publication Manual.

APA style dictates that authors are named with their last name followed by their initials; publication year goes between parentheses, followed by a period. The title of the article is in sentence-case, meaning only the first word and proper nouns in the title are capitalized. The periodical title is run in title case, and is followed by the volume number which, with the title, is also italicized. If a DOI has been assigned to the article that you are using, you should include this after the page numbers for the article. If no DOI has been assigned and you are accessing the periodical online, use the URL of the website from which you are retrieving the periodical.

Author, A. A., Author, B. B., & Author, C. C. (Year). Title of article.  Title of Periodical , volume number (issue number), pages. https://doi.org/xx.xxx/yyyy

Article in Print Journal

Scruton, R. (1996). The eclipse of listening.  The New Criterion, 15 (3), 5 – 13.

Note: APA 7 advises writers to include a DOI (if available), even when using the print source. The example above assumes no DOI is available.

Article in Electronic Journal

As noted above, when citing an article in an electronic journal, include a DOI if one is associated with the article.

Baniya, S., & Weech, S. (2019). Data and experience design: Negotiating community-oriented digital research with service-learning.  Purdue Journal of Service-Learning and International Engagement ,   6 (1), 11 – 16.  https://doi.org/10.5703/1288284316979

DOIs may not always be available. In these cases, use a URL. Many academic journals provide stable URLs that function similarly to DOIs. These are preferable to ordinary URLs copied and pasted from the browser's address bar.

Denny, H., Nordlof, J., & Salem, L. (2018). "Tell me exactly what it was that I was doing that was so bad": Understanding the needs and expectations of working-class students in writing centers. Writing Center Journal , 37 (1), 67 – 98. https://www.jstor.org/stable/26537363

Note that, in the example above, there is a quotation in the title of the article. Ordinary titles lack quotation marks.

Article in a Magazine

Peterzell, J. (1990, April). Better late than never.  Time, 135 (17), 20 –2 1.

Article in a Newspaper

Schultz, S. (2005, December 28). Calls made to strengthen state energy policies.  The Country Today , 1A, 2A.

Baumeister, R. F. (1993). Exposing the self-knowledge myth [Review of the book  The self-knower: A hero under control , by R. A. Wicklund & M. Eckert].  Contemporary Psychology , 38 (5), 466–467.

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• Knowledge Base
• Research paper

Writing a Research Paper Introduction | Step-by-Step Guide

Published on September 24, 2022 by Jack Caulfield . Revised on March 27, 2023.

The introduction to a research paper is where you set up your topic and approach for the reader. It has several key goals:

• Present your topic and get the reader interested
• Provide background or summarize existing research
• Position your own approach
• Detail your specific research problem and problem statement
• Give an overview of the paper’s structure

The introduction looks slightly different depending on whether your paper presents the results of original empirical research or constructs an argument by engaging with a variety of sources.

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Table of contents

Step 1: introduce your topic, step 2: describe the background, step 3: establish your research problem, step 4: specify your objective(s), step 5: map out your paper, research paper introduction examples, frequently asked questions about the research paper introduction.

The first job of the introduction is to tell the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening hook.

The hook is a striking opening sentence that clearly conveys the relevance of your topic. Think of an interesting fact or statistic, a strong statement, a question, or a brief anecdote that will get the reader wondering about your topic.

For example, the following could be an effective hook for an argumentative paper about the environmental impact of cattle farming:

A more empirical paper investigating the relationship of Instagram use with body image issues in adolescent girls might use the following hook:

Don’t feel that your hook necessarily has to be deeply impressive or creative. Clarity and relevance are still more important than catchiness. The key thing is to guide the reader into your topic and situate your ideas.

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This part of the introduction differs depending on what approach your paper is taking.

In a more argumentative paper, you’ll explore some general background here. In a more empirical paper, this is the place to review previous research and establish how yours fits in.

Argumentative paper: Background information

After you’ve caught your reader’s attention, specify a bit more, providing context and narrowing down your topic.

Provide only the most relevant background information. The introduction isn’t the place to get too in-depth; if more background is essential to your paper, it can appear in the body .

Empirical paper: Describing previous research

For a paper describing original research, you’ll instead provide an overview of the most relevant research that has already been conducted. This is a sort of miniature literature review —a sketch of the current state of research into your topic, boiled down to a few sentences.

This should be informed by genuine engagement with the literature. Your search can be less extensive than in a full literature review, but a clear sense of the relevant research is crucial to inform your own work.

Begin by establishing the kinds of research that have been done, and end with limitations or gaps in the research that you intend to respond to.

The next step is to clarify how your own research fits in and what problem it addresses.

Argumentative paper: Emphasize importance

In an argumentative research paper, you can simply state the problem you intend to discuss, and what is original or important about your argument.

Empirical paper: Relate to the literature

In an empirical research paper, try to lead into the problem on the basis of your discussion of the literature. Think in terms of these questions:

• What research gap is your work intended to fill?
• What limitations in previous work does it address?
• What contribution to knowledge does it make?

You can make the connection between your problem and the existing research using phrases like the following.

Now you’ll get into the specifics of what you intend to find out or express in your research paper.

The way you frame your research objectives varies. An argumentative paper presents a thesis statement, while an empirical paper generally poses a research question (sometimes with a hypothesis as to the answer).

Argumentative paper: Thesis statement

The thesis statement expresses the position that the rest of the paper will present evidence and arguments for. It can be presented in one or two sentences, and should state your position clearly and directly, without providing specific arguments for it at this point.

Empirical paper: Research question and hypothesis

The research question is the question you want to answer in an empirical research paper.

Present your research question clearly and directly, with a minimum of discussion at this point. The rest of the paper will be taken up with discussing and investigating this question; here you just need to express it.

A research question can be framed either directly or indirectly.

• This study set out to answer the following question: What effects does daily use of Instagram have on the prevalence of body image issues among adolescent girls?
• We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls.

If your research involved testing hypotheses , these should be stated along with your research question. They are usually presented in the past tense, since the hypothesis will already have been tested by the time you are writing up your paper.

For example, the following hypothesis might respond to the research question above:

The final part of the introduction is often dedicated to a brief overview of the rest of the paper.

In a paper structured using the standard scientific “introduction, methods, results, discussion” format, this isn’t always necessary. But if your paper is structured in a less predictable way, it’s important to describe the shape of it for the reader.

If included, the overview should be concise, direct, and written in the present tense.

• This paper will first discuss several examples of survey-based research into adolescent social media use, then will go on to …
• This paper first discusses several examples of survey-based research into adolescent social media use, then goes on to …

Full examples of research paper introductions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

• Argumentative paper
• Empirical paper

Are cows responsible for climate change? A recent study (RIVM, 2019) shows that cattle farmers account for two thirds of agricultural nitrogen emissions in the Netherlands. These emissions result from nitrogen in manure, which can degrade into ammonia and enter the atmosphere. The study’s calculations show that agriculture is the main source of nitrogen pollution, accounting for 46% of the country’s total emissions. By comparison, road traffic and households are responsible for 6.1% each, the industrial sector for 1%. While efforts are being made to mitigate these emissions, policymakers are reluctant to reckon with the scale of the problem. The approach presented here is a radical one, but commensurate with the issue. This paper argues that the Dutch government must stimulate and subsidize livestock farmers, especially cattle farmers, to transition to sustainable vegetable farming. It first establishes the inadequacy of current mitigation measures, then discusses the various advantages of the results proposed, and finally addresses potential objections to the plan on economic grounds.

The rise of social media has been accompanied by a sharp increase in the prevalence of body image issues among women and girls. This correlation has received significant academic attention: Various empirical studies have been conducted into Facebook usage among adolescent girls (Tiggermann & Slater, 2013; Meier & Gray, 2014). These studies have consistently found that the visual and interactive aspects of the platform have the greatest influence on body image issues. Despite this, highly visual social media (HVSM) such as Instagram have yet to be robustly researched. This paper sets out to address this research gap. We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls. It was hypothesized that daily Instagram use would be associated with an increase in body image concerns and a decrease in self-esteem ratings.

The introduction of a research paper includes several key elements:

• A hook to catch the reader’s interest
• Relevant background on the topic
• Details of your research problem

and your problem statement

• A thesis statement or research question
• Sometimes an overview of the paper

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

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Abstract LB372: Examining medical mistrust and access to cancer preventive care in a diverse sample

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Jackie Knigh Wilt , Maria D. Thomson; Abstract LB372: Examining medical mistrust and access to cancer preventive care in a diverse sample. Cancer Res 1 April 2024; 84 (7_Supplement): LB372. https://doi.org/10.1158/1538-7445.AM2024-LB372

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Background: Medical mistrust has been linked to lower health care utilization, satisfaction and preventive actions among underserved populations. Further exploration of medical mistrust within these groups is needed to inform research and clinical approaches to improve trust and health equity. This study examined the relationship between medical mistrust and access to cancer preventive care among Black, American Indian (AI) and White participants in Virginia.

Methods: A convenience sample (N=1,288) designed to oversample underrepresented communities in Virginia completed surveys assessing medical mistrust, access to medical care and colorectal cancer screening. Measures included the medical mistrust index, visiting a doctor in the last year (yes/no), ever having a colonoscopy (yes/no), perceived health status, race, age, sex, education, marital status, insurance coverage and rurality. Bivariate tests evaluated significant (p<.05) associations between outcomes of having a doctor visit in the last year and colonoscopy screening with medical mistrust, health status, and sociodemographics; significant findings were included in logistic regression models. Differences among endorsements of individual mistrust scale items by race was also assessed.

Results: Sample was μ age=45 (SD=18), 47% female, 40% Black, 36% White, 14% AI, 45% had ≤ high school diploma/GED, 63% married, majority resided in metro areas (91%) and were medically insured (85%). Among the sample 80% had visited doctor in the last year and 63% of those eligible had a colonoscopy. Medical mistrust was significantly higher among Black and AI participants compared to White. In overall and stratified logistic regression models medical mistrust was not associated with visiting a doctor in the last year or obtaining a colonoscopy. 5 of 7 items of the medical mistrust index were endorsed more often among Black and AI participants compared to White. These included feeling that medical institutions deceive people (Black: r=0.07, p<0.01; AI: r=0.07, p=0.01; White: r=-0.11, p<0.01), cover-up mistakes (Black: r=0.11, p<0.01; AI: r=0.96, p=0.04; White: r=-0.13, p<0.01), conduct harmful experiments (Black: r=0.07, p<0.02; White: r=-0.10, p<0.01), lack competency (AI: r=0.07, p=0.01; White: r=-0.08, p<0.01), and make mistakes often (AI: r=0.10, p<0.01; White: r=-0.09, p<0.01).

Conclusions: Medical mistrust was significantly higher among Black and AI participants compared to White but this was not associated with accessing medical care. Given differential endorsement of items dealing with medical errors and their concealment, these results suggest a need to investigate patient experiences after care is initiated to understand what factors may contribute to medical mistrust and care outcomes long term.

Citation Format: Jackie Knigh Wilt, Maria D. Thomson. Examining medical mistrust and access to cancer preventive care in a diverse sample [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB372.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

• Open access
• Published: 02 April 2022

A qualitative study of rural healthcare providers’ views of social, cultural, and programmatic barriers to healthcare access

• Nicholas C. Coombs 1 ,
• Duncan G. Campbell 2 &
• James Caringi 1  

BMC Health Services Research volume  22 , Article number:  438 ( 2022 ) Cite this article

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Ensuring access to healthcare is a complex, multi-dimensional health challenge. Since the inception of the coronavirus pandemic, this challenge is more pressing. Some dimensions of access are difficult to quantify, namely characteristics that influence healthcare services to be both acceptable and appropriate. These link to a patient’s acceptance of services that they are to receive and ensuring appropriate fit between services and a patient’s specific healthcare needs. These dimensions of access are particularly evident in rural health systems where additional structural barriers make accessing healthcare more difficult. Thus, it is important to examine healthcare access barriers in rural-specific areas to understand their origin and implications for resolution.

We used qualitative methods and a convenience sample of healthcare providers who currently practice in the rural US state of Montana. Our sample included 12 healthcare providers from diverse training backgrounds and specialties. All were decision-makers in the development or revision of patients’ treatment plans. Semi-structured interviews and content analysis were used to explore barriers–appropriateness and acceptability–to healthcare access in their patient populations. Our analysis was both deductive and inductive and focused on three analytic domains: cultural considerations, patient-provider communication, and provider-provider communication. Member checks ensured credibility and trustworthiness of our findings.

Five key themes emerged from analysis: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US.

Conclusions

Inadequate access to healthcare is an issue in the US, particularly in rural areas. Rural healthcare consumers compose a hard-to-reach patient population. Too few providers exist to meet population health needs, and fragmented communication impairs rural health systems’ ability to function. These issues exacerbate the difficulty of ensuring acceptable and appropriate delivery of healthcare services, which compound all other barriers to healthcare access for rural residents. Each dimension of access must be monitored to improve patient experiences and outcomes for rural Americans.

Peer Review reports

Unequal access to healthcare services is an important element of health disparities in the United States [ 1 ], and there remains much about access that is not fully understood. The lack of understanding is attributable, in part, to the lack of uniformity in how access is defined and evaluated, and the extent to which access is often oversimplified in research [ 2 ]. Subsequently, attempts to address population-level barriers to healthcare access are insufficient, and access remains an unresolved, complex health challenge [ 3 , 4 , 5 ]. This paper presents a study that aims to explore some of the less well studied barriers to healthcare access, particularly those that influence healthcare acceptability and appropriateness.

In truth, healthcare access entails a complicated calculus that combines characteristics of individuals, their households, and their social and physical environments with characteristics of healthcare delivery systems, organizations, and healthcare providers. For one to fully ‘access’ healthcare, they must have the means to identify their healthcare needs and have available to them care providers and the facilities where they work. Further, patients must then reach, obtain, and use the healthcare services in order to have their healthcare needs fulfilled. Levesque and colleagues critically examined access conceptualizations in 2013 and synthesized all ways in which access to healthcare was previously characterized; Levesque et al. proposed five dimensions of access: approachability, acceptability, availability, affordability and appropriateness [ 2 ]. These refer to the ability to perceive, seek, reach, pay for, and engage in services, respectively.

According to Levesque et al.’s framework, the five dimensions combine to facilitate access to care or serve as barriers. Approachability indicates that people facing health needs understand that healthcare services exist and might be helpful. Acceptability represents whether patients see healthcare services as consistent or inconsistent with their own social and cultural values and worldviews. Availability indicates that healthcare services are reached both physically and in a timely manner. Affordability simplifies one’s capacity to pay for healthcare services without compromising basic necessities, and finally, appropriateness represents the fit between healthcare services and a patient’s specific healthcare needs [ 2 ]. This study focused on the acceptability and appropriateness dimensions of access.

Before the novel coronavirus (SARS-CoV-2; COVID-19) pandemic, approximately 13.3% of adults in the US did not have a usual source of healthcare [ 6 ]. Millions more did not utilize services regularly, and close to two-thirds reported that they would be debilitated by an unexpected medical bill [ 7 , 8 , 9 ]. Findings like these emphasized a fragility in the financial security of the American population [ 10 ]. These concerns were exacerbated by the pandemic when a sudden surge in unemployment increased un- and under-insurance rates [ 11 ]. Indeed, employer-sponsored insurance covers close to half of Americans’ total cost of illness [ 12 ]. Unemployment linked to COVID-19 cut off the lone outlet to healthcare access for many. Health-related financial concerns expanded beyond individuals, as healthcare organizations were unequipped to manage a simultaneous increase in demand for specialized healthcare services and a steep drop off for routine revenue-generating healthcare services [ 13 ]. These consequences of the COVID-19 pandemic all put additional, unexpected pressure on an already fragmented US healthcare system.

Other structural barriers to healthcare access exist in relation to the rural–urban divide. Less than 10% of US healthcare resources are located in rural areas where approximately 20% of the American population resides [ 14 ]. In a country with substantially fewer providers per capita compared to many other developed countries, persons in rural areas experience uniquely pressing healthcare provider shortages [ 15 , 16 ]. Rural inhabitants also tend to have lower household income, higher rates of un- or under-insurance, and more difficulty with travel to healthcare clinics than urban dwellers [ 17 ]. Subsequently, persons in rural communities use healthcare services at lower rates, and potentially preventable hospitalizations are more prevalent [ 18 ]. This disparity often leads rural residents to use services primarily for more urgent needs and less so for routine care [ 19 , 20 , 21 ].

The differences in how rural and urban healthcare systems function warranted a federal initiative to focus exclusively on rural health priorities and serve as counterpart to Healthy People objectives [ 22 ]. The rural determinants of health, a more specific expression of general social determinants, add issues of geography and topography to the well-documented social, economic and political factors that influence all Americans’ access to healthcare [ 23 ]. As a result, access is consistently regarded as a top priority in rural areas, and many research efforts have explored the intersection between access and rurality, namely within its less understood dimensions (acceptability and appropriateness) [ 22 ].

Acceptability-related barriers to care

Acceptability represents the dimension of healthcare access that affects a patient’s ability to seek healthcare, particularly linked to one’s professional values, norms and culture [ 2 ]. Access to health information is an influential factor for acceptable healthcare and is essential to promote and maintain a healthy population [ 24 ]. According to the Centers for Disease Control and Prevention, health literacy or a high ‘health IQ’ is the degree to which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others, which impacts healthcare use and system navigation [ 25 ]. The literature indicates that lower levels of health literacy contribute to health disparities among rural populations [ 26 , 27 , 28 ]. Evidence points to a need for effective health communication between healthcare organizations and patients to improve health literacy [ 24 ]. However, little research has been done in this area, particularly as it relates to technologically-based interventions to disseminate health information [ 29 ].

Stigma, an undesirable position of perceived diminished status in an individual’s social position, is another challenge that influences healthcare acceptability [ 30 ]. Those who may experience stigma fear negative social consequences in relation to care seeking. They are more likely to delay seeking care, especially among ethnic minority populations [ 31 , 32 ]. Social media presents opportunities for the dissemination of misleading medical information; this runs further risk for stigma [ 33 ]. Stigma is difficult to undo, but research has shown that developing a positive relationship with a healthcare provider or organization can work to reduce stigma among patients, thus promoting healthcare acceptability [ 34 ].

A provider’s attempts to engage patients and empower them to be active decision-makers regarding their treatment has also been shown to improve healthcare acceptability. One study found that patients with heart disease who completed a daily diary of weight and self-assessment of symptoms, per correspondence with their provider, had better care outcomes than those who did not [ 35 ]. Engaging with household family members and involved community healers also mitigates barriers to care, emphasizing the importance of a team-based approach that extends beyond those who typically provide healthcare services [ 36 , 37 ]. One study, for instance, explored how individuals closest to a pregnant woman affect the woman’s decision to seek maternity care; partners, female relatives, and community health-workers were among the most influential in promoting negative views, all of which reduced a woman’s likelihood to access care [ 38 ].

Appropriateness-related barriers to care

Appropriateness marks the dimension of healthcare access that affects a patient’s ability to engage, and according to Levesque et al., is of relevance once all other dimensions (the ability to perceive, seek, reach and pay for) are achieved [ 2 ]. The ability to engage in healthcare is influenced by a patient’s level of empowerment, adherence to information, and support received by their healthcare provider. Thus, barriers to healthcare access that relate to appropriateness are often those that indicate a breakdown in communication between a patient with their healthcare provider. Such breakdown can involve a patient experiencing miscommunication, confrontation, and/or a discrepancy between their provider’s goals and their own goals for healthcare. Appropriateness represents a dimension of healthcare access that is widely acknowledged as an area in need of improvement, which indicates a need to rethink how healthcare providers and organizations can adapt to serve the healthcare needs of their communities [ 39 ]. This is especially true for rural, ethnic minority populations, which disproportionately experience an abundance of other barriers to healthcare access. Culturally appropriate care is especially important for members of minority populations [ 40 , 41 , 42 ]. Ultimately, patients value a patient-provider relationship characterized by a welcoming, non-judgmental atmosphere [ 43 , 44 ]. In rural settings especially, level of trust and familiarity are common factors that affect service utilization [ 45 ]. Evidence suggests that kind treatment by a healthcare provider who promotes patient-centered care can have a greater overall effect on a patient’s experience than a provider’s degree of medical knowledge or use of modern equipment [ 46 ]. Of course, investing the time needed to nurture close and caring interpersonal connections is particularly difficult in under-resourced, time-pressured rural health systems [ 47 , 48 ].

The most effective way to evaluate access to healthcare largely depends on which dimensions are explored. For instance, a population-based survey can be used to measure the barrier of healthcare affordability. Survey questions can inquire directly about health insurance coverage, care-related financial burden, concern about healthcare costs, and the feared financial impacts of illness and/or disability. Many national organizations have employed such surveys to measure affordability-related barriers to healthcare. For example, a question may ask explicitly about financial concerns: ‘If you get sick or have an accident, how worried are you that you will not be able to pay your medical bills?’ [ 49 ]. Approachability and availability dimensions of access are also studied using quantitative analysis of survey questions, such as ‘Is there a place that you usually go to when you are sick or need advice about your health?’ or ‘Have you ever delayed getting medical care because you couldn’t get through on the telephone?’ In contrast, the remaining two dimensions–acceptability and appropriateness–require a qualitative approach, as the social and cultural factors that determine a patient’s likelihood of accepting aspects of the services that are to be received (acceptability) and the fit between those services and the patient’s specific healthcare needs (appropriateness) can be more abstract [ 50 , 51 ]. In social science, qualitative methods are appropriate to generate knowledge of what social events mean to individuals and how those individuals interact within them; these methods allow for an exploration of depth rather than breadth [ 52 , 53 ]. Qualitative methods, therefore, are appropriate tools for understanding the depth of healthcare providers’ experiences in the inherently social context of seeking and engaging in healthcare.

In sum, acceptability- and appropriateness-related barriers to healthcare access are multi-layered, complex and abundant. Ensuring access becomes even more challenging if structural barriers to access are factored in. In this study, we aimed to explore barriers to healthcare access among persons in Montana, a historically underserved, under-resourced, rural region of the US. Montana is the fourth largest and third least densely populated state in the country; more than 80% of Montana counties are classified as non-core (the lowest level of urban/rural classification), and over 90% are designated as health professional shortage areas [ 54 , 55 ]. Qualitative methods supported our inquiry to explore barriers to healthcare access related to acceptability and appropriateness.

Participants

Qualitative methods were utilized for this interpretive, exploratory study because knowledge regarding barriers to healthcare access within Montana’s rural health systems is limited. We chose Montana healthcare providers, rather than patients, as the population of interest so we may explore barriers to healthcare access from the perspective of those who serve many persons in rural settings. Inclusion criteria required study participants to provide direct healthcare to patients at least one-half of their time. We defined ‘provider’ as a healthcare organization employee with clinical decision-making power and the qualifications to develop or revise patients’ treatment plans. In an attempt to capture a group of providers with diverse experience, we included providers across several types and specialties. These included advanced practice registered nurses (APRNs), physicians (MDs and DOs), and physician assistants (PAs) who worked in critical care medicine, emergency medicine, family medicine, hospital medicine, internal medicine, pain medicine, palliative medicine, pediatrics, psychiatry, and urgent care medicine. We also included licensed clinical social workers (LCSWs) and clinical psychologists who specialize in behavioral healthcare provision.

Recruitment and Data Collection

We recruited participants via email using a snowball sampling approach [ 56 ]. We opted for this approach because of its effectiveness in time-pressured contexts, such as the COVID-19 pandemic, which has made healthcare provider populations hard to reach [ 57 ]. Considering additional constraints with the pandemic and the rural nature of Montana, interviews were administered virtually via Zoom video or telephone conferencing with Zoom’s audio recording function enabled. All interviews were conducted by the first author between January and September 2021. The average length of interviews was 50 min, ranging from 35 to 70 min. There were occasional challenges experienced during interviews (poor cell phone reception from participants, dropped calls), in which case the interviewer remained on the line until adequate communication was resumed. All interviews were included for analysis and transcribed verbatim into NVivo Version 12 software. All qualitative data were saved and stored on a password-protected University of Montana server. Hard-copy field notes were securely stored in a locked office on the university’s main campus.

Data analysis included a deductive followed by an inductive approach. This dual analysis adheres to Levesque’s framework for qualitative methods, which is discussed in the Definition of Analytic Domains sub-section below. Original synthesis of the literature informed the development of our initial deductive codebook. The deductive approach was derived from a theory-driven hypothesis, which consisted of synthesizing previous research findings regarding acceptability- and appropriateness-related barriers to care. Although the locations, patient populations and specific type of healthcare services varied by study in the existing literature, several recurring barriers to healthcare access were identified. We then operationalized three analytic domains based on these findings: cultural considerations, patient-provider communication, and provider-provider communication. These domains were chosen for two reasons: 1) the terms ‘culture’ and ‘communication’ were the most frequently documented characteristics across the studies examined, and 2) they each align closely with the acceptability and appropriateness dimensions of access to healthcare, respectively. In addition, ‘culture’ is included in the definition of acceptability and ‘communication’ is a quintessential aspect of appropriateness. These domains guided the deductive portion of our analysis, which facilitated the development of an interview guide used for this study.

Interviews were semi-structured to allow broad interpretations from participants and expand the open-ended characterization of study findings. Data were analyzed through a flexible coding approach proposed by Deterding and Waters [ 58 ]. Qualitative content analysis was used, a method particularly beneficial for analyzing large amounts of qualitative data collected through interviews that offers possibility of quantifying categories to identify emerging themes [ 52 , 59 ]. After fifty percent of data were analyzed, we used an inductive approach as a formative check and repeated until data saturation, or the point at which no new information was gathered in interviews [ 60 ]. At each point of inductive analysis, interview questions were added, removed, or revised in consideration of findings gathered [ 61 ]. The Standards for Reporting Qualitative Research (SRQR) was used for reporting all qualitative data for this study [ 62 ]. The first and third authors served as primary and secondary analysts of the qualitative data and collaborated to triangulate these findings. An audit approach was employed, which consisted of coding completed by the first author and then reviewed by the third author. After analyses were complete, member checks ensured credibility and trustworthiness of findings [ 63 ]. Member checks consisted of contacting each study participant to explain the study’s findings; one-third of participants responded and confirmed all findings. All study procedures were reviewed and approved by the Human Subjects Committee of the authors’ institution’s Institutional Review Board.

Definitions of Analytic Domains

Cultural considerations.

Western health systems often fail to consider aspects of patients’ cultural perspectives and histories. This can manifest in the form of a providers’ lack of cultural humility. Cultural humility is a process of preventing imposition of one’s worldview and cultural beliefs on others and recognizing that everyone’s conception of the world is valid. Humility cultivates sensitive approaches in treating patients [ 64 ]. A lack of cultural humility impedes the delivery of acceptable and appropriate healthcare [ 65 ], which can involve low empathy or respect for patients, or dismissal of culture and traditions as superstitions that interfere with standard treatments [ 66 , 67 ]. Ensuring cultural humility among all healthcare employees is a step toward optimal healthcare delivery. Cultural humility is often accomplished through training that can be tailored to particular cultural- or gender-specific populations [ 68 , 69 ]. Since cultural identities and humility have been marked as factors that can heavily influence patients’ access to care, cultural considerations composed our first analytic domain. To assess this domain, we asked participants how they address the unique needs of their patients, how they react when they observe a cultural behavior or attitude from a patient that may not directly align with their treatment plan, and if they have received any multicultural training or training on cultural considerations in their current role.

Patient-provider communication

Other barriers to healthcare access can be linked to ineffective patient-provider communication. Patients who do not feel involved in healthcare decisions are less likely to adhere to treatment recommendations [ 70 ]. Patients who experience communication difficulties with providers may feel coerced, which generates disempowerment and leads patients to employ more covert ways of engagement [ 71 , 72 ]. Language barriers can further compromise communication and hinder outcomes or patient progress [ 73 , 74 ]. Any miscommunication between a patient and provider can affect one’s access to healthcare, namely affecting appropriateness-related barriers. For these reasons, patient-provider communication composed our second analytic domain. We asked participants to highlight the challenges they experience when communicating with their patients, how those complications are addressed, and how communication strategies inform confidentiality in their practice. Confidentiality is a core ethical principle in healthcare, especially in rural areas that have smaller, interconnected patient populations [ 75 ].

Provider-Provider Communication

A patient’s journey through the healthcare system necessitates sufficient correspondence between patients, primary, and secondary providers after discharge and care encounters [ 76 ]. Inter-provider and patient-provider communication are areas of healthcare that are acknowledged to have some gaps. Inconsistent mechanisms for follow up communication with patients in primary care have been documented and emphasized as a concern among those with chronic illness who require close monitoring [ 68 , 77 ]. Similar inconsistencies exist between providers, which can lead to unclear care goals, extended hospital stays, and increased medical costs [ 78 ]. For these reasons, provider-provider communication composed our third analytic domain. We asked participants to describe the approaches they take to streamline communication after a patient’s hospital visit, the methods they use to ensure collaborative communication between primary or secondary providers, and where communication challenges exist.

Healthcare provider characteristics

Our sample included 12 providers: four in family medicine (1 MD, 1 DO, 1 PA & 1 APRN), three in pediatrics (2 MD with specialty in hospital medicine & 1 DO), three in palliative medicine (2 MDs & 1 APRN with specialty in wound care), one in critical care medicine (DO with specialty in pediatric pulmonology) and one in behavioral health (1 LCSW with specialty in trauma). Our participants averaged 9 years (range 2–15) as a healthcare provider; most reported more than 5 years in their current professional role. The diversity of participants extended to their patient populations as well, with each participant reporting a unique distribution of age, race and level of medical complexity among their patients. Most participants reported that a portion of their patients travel up to five hours, sometimes across county- or state-lines, to receive care.

Theme 1: A friction exists between aspects of patients’ rural identities and healthcare systems

Our participants comprised a collection of medical professions and reported variability among health-related reasons their patients seek care. However, most participants acknowledged similar characteristics that influence their patients’ challenges to healthcare access. These identified factors formed categories from which the first theme emerged. There exists a great deal of ‘rugged individualism’ among Montanans, which reflects a self-sufficient and self-reliant way of life. Stoicism marked a primary factor to characterize this quality. One participant explained:

True Montanans are difficult to treat medically because they tend to be a tough group. They don’t see doctors. They don’t want to go, and they don’t want to be sick. That’s an aspect of Montana that makes health culture a little bit difficult.

Another participant echoed this finding by stating:

The backwoods Montana range guy who has an identity of being strong and independent probably doesn’t seek out a lot of medical care or take a lot of medications. Their sense of vitality, independence and identity really come from being able to take care and rely on themselves. When that is threatened, that’s going to create a unique experience of illness.

Similar responses were shared by all twelve participants; stoicism seemed to be heavily embedded in many patient populations in Montana and serves as a key determinant of healthcare acceptability. There are additional factors, however, that may interact with stoicism but are multiply determined. Stigma is an example of this, presented in this context as one’s concern about judgement by the healthcare system. Respondents were openly critical of this perception of the healthcare system as it was widely discussed in interviews. One participant stated:

There is a real perception of a punitive nature in the medical community, particularly if I observe a health issue other than the primary reason for one’s hospital visit, whether that may be predicated on medical neglect, delay of care, or something that may warrant a report to social services. For many of the patients and families I see, it’s not a positive experience and one that is sometimes an uphill barrier that I work hard to circumnavigate.

Analysis of these factors suggest that low use of healthcare services may link to several characteristics, including access problems. Separately, a patient’s perceived stigma from healthcare providers may also impact a patient’s willingness to receive services. One participant put it best by stating

Sometimes, families assume that I didn’t want to see them because they will come in for follow up to meet with me but end up meeting with another provider, which is frustrating because I want to maintain patients on my panel but available time and resource occasionally limits me from doing so. It could be really hard adapting to those needs on the fly, but it’s an honest miss.

When a patient arrives for a healthcare visit and experiences this frustration, it may elicit a patient’s perceptions of neglect or disorganization. This ‘honest miss’ may, in turn, exacerbate other acceptable-related barriers to care.

Theme 2: Facilitating access to healthcare requires application of and respect for cultural differences

The biomedical model is the standard of care utilized in Western medicine [ 79 , 80 ]. However, the US comprises people with diverse social and cultural identities that may not directly align with Western conceptions of health and wellness. Approximately 11.5% of the Montana population falls within an ethnic minority group. 6.4% are of American Indian or Alaska Native origin, 0.5% are of Black or African American origin, 0.8% are of Asian origin and 3.8% are of multiple or other origins. [ 81 ]. Cultural insensitivity is acknowledged in health services research as an active deterrent for appropriate healthcare delivery [ 65 ]. Participants for this study were asked how they react when a patient brings up a cultural attitude or behavior that may impact the proposed treatment plan. Eight participants noted a necessity for humility when this occurs. One participant conceptualized this by stating:

When this happens, I learn about individuals and a way of life that is different to the way I grew up. There is a lot of beauty and health in a non-patriarchal, non-dominating, non-sexist framework, and when we can engage in such, it is really expansive for my own learning process.

The participants who expressed humility emphasized that it is best to work in tandem with their patient, congruently. Especially for those with contrasting worldviews, a provider and a patient working as a team poses an opportunity to develop trust. Without it, a patient can easily fall out of the system, further hindering their ability to access healthcare services in the future. One participant stated:

The approach that ends up being successful for a lot of patients is when we understand their modalities, and they have a sense we understand those things. We have to show understanding and they have to trust. From there, we can make recommendations to help get them there, not decisions for them to obey, rather views based on our experiences and understanding of medicine.

Curiosity was another reaction noted by a handful of participants. One participant said:

I believe patients and their caregivers can be engaged and loving in different ways that don’t always follow the prescribed approach in the ways I’ve been trained, but that doesn’t necessarily mean that they are detrimental. I love what I do, and I love learning new things or new approaches, but I also love being surprised. My style of medicine is not to predict peoples’ lives, rather to empower and support what makes life meaningful for them.

Participants mentioned several other characteristics that they use in practice to prevent cultural insensitivity and support a collaborative approach to healthcare. Table 1 lists these facilitating characteristics and quotes to explain the substance of their benefit.

Consensus among participants indicated that the use of these protective factors to promote cultural sensitivity and apply them in practice is not standardized. When asked, all but two participants said they had not received any culturally-based training since beginning their practice. Instead, they referred to developing skills through “on the job training” or “off the cuff learning.” The general way of medicine, one participant remarked, was to “throw you to the fire.” This suggested that use of standardized cultural humility training modules for healthcare providers was not common practice. Many attributed this to time constraints.

Individual efforts to gain culturally appropriate skills or enhance cultural humility were mentioned, however. For example, three participants reported that they attended medical conferences to discuss cultural challenges within medicine, one participant sought out cultural education within their organization, and another was invited by Native American community members to engage in traditional peace ceremonies. Participants described these additional efforts as uncommon and outside the parameters of a provider’s job responsibilities, as they require time commitments without compensation.

Additionally, eight participants said they share their personal contact information with patients so they may call them directly for medical needs. The conditions and frequency with which this is done was variable and more common among providers in specialized areas of medicine or those who described having a manageable patient panel. All who reported that they shared their personal contact information described it as an aspect of rural health service delivery that is atypical in other, non-rural healthcare systems.

Theme 3: Communication between healthcare providers is systematically fragmented

Healthcare is complex and multi-disciplinary, and patients’ treatment is rarely overseen by a single provider [ 82 ]. The array of provider types and specialties is vast, as is the range of responsibilities ascribed to providers. Thus, open communication among providers both within and between healthcare systems is vital for the success of collaborative healthcare [ 83 ]. Without effective communication achieved between healthcare providers, the appropriate delivery of healthcare services may be become compromised. Our participants noted that they face multiple challenges that complicate communication with other providers. Miscommunication between departments, often implicating the Emergency Department (ED), was a recurring point noted among participants. One participant who is a primary care physician said:

If one of my patients goes to the ER, I don’t always get the notes. They’re supposed to send them to the patient’s primary care doc. The same thing happens with general admissions, but again, I often find out from somebody else that my patient was admitted to the hospital.

This failure to communicate can negatively impact the patient, particularly if time sensitivity or medical complexity is essential to treatment. A patient’s primary care physician is the most accurate source of their medical history; without an effective way to obtain and synthesize a patient’s health information, there may be increased risk of medical error. One participant in a specialty field stated:

One of the biggest barriers I see is obtaining a concise description of a patient’s history and needs. You can imagine if you’re a mom and you’ve got a complicated kid. You head to the ER. The ER doc looks at you with really wide eyes, not knowing how to get information about your child that’s really important.

This concern was highlighted with a specific example from a different participant:

I have been unable to troubleshoot instances when I send people to the ER with a pretty clear indication for admission, and then they’re sent home. For instance, I had an older fellow with pretty severe chronic kidney disease. He presented to another practitioner in my office with shortness of breath and swelling and appeared to have newly onset decompensated heart failure. When I figured this out, I sent him to the ER, called and gave my report. The patient later came back for follow up to find out not only that they had not been admitted but they lost no weight with outpatient dialysis . I feel like a real opportunity was missed to try to optimize the care of the patient simply because there was poor communication between myself and the ER. This poor guy… He ended up going to the ER four times before he got admitted for COVID-19.

In some cases, communication breakdown was reported as the sole cause of a poor outcome. When communication is effective, each essential member of the healthcare team is engaged and collaborating with the same information. Some participants called this process ‘rounds’ when a regularly scheduled meeting is staged between a group of providers to ensure access to accurate patient information. Accurate communication may also help build trust and improve a patient’s experience. In contrast, ineffective communication can result in poor clarity regarding providers’ responsibilities or lost information. Appropriate delivery of healthcare considers the fit between providers and a patient’s specific healthcare needs; the factors noted here suggest that provider-provider miscommunication can adversely affect this dimension of healthcare access.

Another important mechanism of communication is the sharing of electronic medical records (EMRs), a process that continues to shift with technological advances. Innovation is still recent enough, however, for several of our study participants to be able to recall a time when paper charts were standard. Widespread adoption and embrace of the improvements inherent in electronic medical records expanded in the late 2000’s [ 84 ]. EMRs vastly improved the ability to retain, organize, safeguard, and transfer health information. Every participant highlighted EMRs at one point or another and often did so with an underlying sense of anger or frustration. Systematic issues and problems with EMRs were discussed. One participant provided historical context to such records:

Years back, the government aimed to buy an electronic medical record system, whichever was the best, and a number of companies created their own. Each were a reasonable system, so they all got their checks and now we have four completely separate operating systems that do not talk to each other. The idea was to make a router or some type of relay that can share information back and forth. There was no money in that though, so of course, no one did anything about it. Depending on what hospital, clinic or agency you work for, you will most likely work within one of these systems. It was a great idea; it just didn’t get finished.

Seven participants confirmed these points and their impacts on making coordination more difficult, relying on outdated communication strategies more often than not. Many noted this even occurs between facilities within the same city and in separate small metropolitan areas across the state. One participant said:

If my hospital decides to contract with one EMR and the hospital across town contracts with another, correspondence between these hospitals goes back to traditional faxing. As a provider, you’re just taking a ‘fingered crossed’ approach hoping that the fax worked, is picked up, was put in the appropriate inbox and was actually looked at. Information acquisition and making sure it’s timely are unforeseen between EMRs.

Participants reported an “astronomic” number of daily faxes and telephone calls to complete the communication EMRs were initially designed to handle. These challenges are even more burdensome if a patient moves from out of town or out of state; obtaining their medical records was repeatedly referred to as a “chore” so onerous that it often remains undone. Another recurring concern brought up by participants regarded accuracy within EMRs to lend a false sense of security. They are not frequently updated, not designed to be family-centered and not set up to do anything automatically. One participant highlighted these limitations by stating:

I was very proud of a change I made in our EMR system [EPIC], even though it was one I never should have had to make. I was getting very upset because I would find out from my nursing assistant who read the obituary that one of my patients had died. There was a real problem with the way the EMR was notifying PCP’s, so I got an EPIC-level automated notification built into our EMR so that any time a patient died, their status would be changed to deceased and a notification would be sent to their PCP. It’s just really awful to find out a week later that your patient died, especially when you know these people and their families really well. It’s not good care to have blind follow up.

Whether it be a physical or electronic miscommunication between healthcare providers, the appropriate delivery of healthcare can be called to question

Theme 4: Time and resource constraints disproportionately harm rural health systems

Several measures of system capacity suggest the healthcare system in the US is under-resourced. There are fewer physicians and hospital beds per capita compared to most comparable countries, and the growth of healthcare provider populations has stagnated over time [ 15 ]. Rural areas, in particular, are subject to resource limitations [ 16 ]. All participants discussed provider shortages in detail. They described how shortages impact time allocation in their day-to-day operations. Tasks like patient intakes, critical assessments, and recovering information from EMRs take time, of which most participants claimed to not have enough of. There was also a consensus in having inadequate time to spend on medically complex cases. Time pressures were reported to subsequently influence quality of care. One participant stated:

With the constant pace of medicine, time is not on your side. A provider cannot always participate in an enriching dialogue with their patients, so rather than listen and learn, we are often coerced into the mindset of ‘getting through’ this patient so we can move on. This echoes for patient education during discharge, making the whole process more arduous than it otherwise could be if time and resources were not as sparse.

Depending on provider type, specialty, and the size of patient panels, four participants said they have the luxury of extending patient visits to 40 + minutes. Any flexibility with patient visits was regarded as just that: a luxury. Very few providers described the ability to coordinate their schedules as such. This led some study participants to limit the number of patients they serve. One participant said:

We simply don’t have enough clinicians, which is a shame because these people are really skilled, exceptional, brilliant providers but are performing way below their capacity. Because of this, I have a smaller case load so I can engage in a level of care that I feel is in the best interest of my patients. Everything is a tradeoff. Time has to be sacrificed at one point or another. This compromise sets our system up to do ‘ok’ work, not great work.

Of course, managing an overly large number of patients with high complexity is challenging. Especially while enduring the burden of a persisting global pandemic, participants reflected that the general outlook of administering healthcare in the US is to “do more with less.” This often forces providers to delegate responsibilities, which participants noted has potential downsides. One participant described how delegating patient care can cause problems.

Very often will a patient schedule a follow up that needs to happen within a certain time frame, but I am unable to see them myself. So, they are then placed with one of my mid-level providers. However, if additional health issues are introduced, which often happens, there is a high-risk of bounce-back or need to return once again to the hospital. It’s an inefficient vetting process that falls to people who may not have specific training in the labs and imaging that are often included in follow up visits. Unfortunately, it’s a forlorn hope to have a primary care physician be able to attend all levels of a patient’s care.

Several participants described how time constraints stretch all healthcare staff thin and complicate patient care. This was particularly important among participants who reported having a patient panel exceeding 1000. There were some participants, however, who praised the relationships they have with their nurse practitioners and physician’s assistants and mark transparency as the most effective way to coordinate care. Collectively, these clinical relationships were built over long standing periods of time, a disadvantage to providers at the start of their medical career. All but one participant with over a decade of clinical experience mentioned the usefulness of these relationships. The factors discussed in Theme 4 are directly linked to the Availability dimension of access to healthcare. A patient’s ability to reach care is subject to the capacity of their healthcare provider(s). Additionally, further analysis suggests these factors also link to the Appropriateness dimension because the quality of patient-provider relationships may be negatively impacted if a provider’s time is compromised.

Theme 5: Profits are prioritized over addressing barriers to healthcare access in the US.

The US healthcare system functions partially for-profit in the public and private sectors. The federal government provides funding for national programs such as Medicare, but a majority of Americans access healthcare through private employer plans [ 85 ]. As a result, uninsurance rates influence healthcare access. Though the rate of the uninsured has dropped over the last decade through expansion of the Affordable Care Act, it remains above 8 percent [ 86 ]. Historically, there has been ethical criticism in the literature of a for-profit system as it is said to exacerbate healthcare disparities and constitute unfair competition against nonprofit institutions. Specifically, the US healthcare system treats healthcare as a commodity instead of a right, enables organizational controls that adversely affect patient-provider relationships, undermines medical education, and constitutes a medical-industrial complex that threatens influence on healthcare-related public policy [ 87 ]. Though unprompted by the interviewer, participants raised many of these concerns. One participant shared their views on how priorities stand in their practice:

A lot of the higher-ups in the healthcare system where I work see each patient visit as a number. It’s not that they don’t have the capacity to think beyond that, but that’s what their role is, making sure we’re profitable. That’s part of why our healthcare system in the US is as broken as it is. It’s accentuated focus on financially and capitalistically driven factors versus understanding all these other barriers to care.

Eight participants echoed a similar concept, that addressing barriers to healthcare access in their organizations is largely complicated because so much attention is directed on matters that have nothing to do with patients. A few other participants supported this by alluding to a “cherry-picking” process by which those at the top of the hierarchy devote their attention to the easiest tasks. One participant shared an experience where contrasting work demands between administrators and front-line clinical providers produces adverse effects:

We had a new administrator in our hospital. I had been really frustrated with the lack of cultural awareness and curiosity from our other leaders in the past, so I offered to meet and take them on a tour of the reservation. This was meant to introduce them to kids, families and Tribal leaders who live in the area and their interface with healthcare. They declined, which I thought was disappointing and eye-opening.

Analysis of these factors suggest that those who work directly with patients understand patient needs better than those who serve in management roles. This same participant went on to suggest an ulterior motive for a push towards telemedicine, as administrators primarily highlight the benefit of billing for virtual visits instead of the nature of the visits themselves.

This study explored barriers and facilitators to healthcare access from the perspective of rural healthcare providers in Montana. Our qualitative analysis uncovered five key themes: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US. Themes 2 and 3 were directly supported by earlier qualitative studies that applied Levesque’s framework, specifically regarding healthcare providers’ poor interpersonal quality and lack of collaboration with other providers that are suspected to result from a lack of provider training [ 67 , 70 ]. This ties back to the importance of cultural humility, which many previous culture-based trainings have referred to as cultural competence. Cultural competence is achieved through a plethora of trainings designed to expose providers to different cultures’ beliefs and values but induces risk of stereotyping and stigmatizing a patient’s views. Therefore, cultural humility is the preferred idea, by which providers reflect and gain open-ended appreciation for a patient’s culture [ 88 ].

Implications for Practice

Perhaps the most substantial takeaway is how embedded rugged individualism is within rural patient populations and how difficult that makes the delivery of care in rural health systems. We heard from participants that stoicism and perceptions of stigma within the system contribute to this, but other resulting factors may be influential at the provider- and organizational-levels. Stoicism and perceived stigma both appear to arise, in part, from an understandable knowledge gap regarding the care system. For instance, healthcare providers understand the relations between primary and secondary care, but many patients may perceive both concepts as elements of a single healthcare system [ 89 ]. Any issue experienced by a patient when tasked to see both a primary and secondary provider may result in a patient becoming confused [ 90 ]. This may also overlap with our third theme, as a disjointed means of communication between healthcare providers can exacerbate patients’ negative experiences. One consideration to improve this is to incorporate telehealth programs into an existing referral framework to reduce unnecessary interfacility transfers; telehealth programs have proven effective in rural and remote settings [ 91 ].

In fact, telehealth has been rolled out in a variety of virtual platforms throughout its evolution, its innovation matched with continued technological advancement. Simply put, telehealth allows health service delivery from a distance; it allows knowledge and practice of clinical care to be in a different space than a patient. Because of this, a primary benefit of telehealth is its impact on improving patient-centered outcomes among those living in rural areas. For instance, text messaging technology improves early infant diagnosis, adherence to recommended diagnostic testing, and participant engagement in lifestyle change interventions [ 92 , 93 , 94 ]. More sophisticated interventions have found their way into smartphone-based technology, some of which are accessible even without an internet connection [ 95 , 96 ]. Internet accessibility is important because a number of study participants noted internet connectivity as a barrier for patients who live in low resource communities. Videoconferencing is another function of telehealth that has delivered a variety of health services, including those which are mental health-specific [ 97 ], and mobile health clinics have been used in rural, hard-to-reach settings to show the delivery of quality healthcare is both feasible and acceptable [ 98 , 99 , 100 ]. While telehealth has potential to reduce a number of healthcare access barriers, it may not always address the most pressing healthcare needs [ 101 ]. However, telehealth does serve as a viable, cost-effective alternative for rural populations with limited physical access to specialized services [ 102 ]. With time and resource limitations acknowledged as a key theme in our study, an emphasis on expanding telehealth services is encouraged as it will likely have significant involvement on advancing healthcare in the future, especially as the COVID-19 pandemic persists [ 103 ].

Implications for Policy

One could argue that most of the areas of fragmentation in the US healthcare system can be linked to the very philosophy on which it is based: an emphasis on profits as highest priority. Americans are, therefore, forced to navigate a health service system that does not work solely in their best interests. It is not surprising to observe lower rates of healthcare usage in rural areas, which may be a result from rural persons’ negative views of the US healthcare system or a perception that the system does not exist to support wellness. These perceptions may interact with ‘rugged individualism’ to squelch rural residents’ engagement in healthcare. Many of the providers we interviewed for this study appeared to understand this and strived to improve their patients’ experiences and outcomes. Though these efforts are admirable, they may not characterize all providers who serve in rural areas of the US. From a policy standpoint, it is important to recognize these expansive efforts from providers. If incentives were offered to encourage maximum efforts be made, it may lessen burden due to physician burnout and fatigue. Of course, there is no easy fix to the persisting limit of time and resources for providers, problems that require workforce expansion. Ultimately, though, the current structure of the US healthcare system is failing rural America and doing little to help the practice of rural healthcare providers.

Implications for Future Research

It is important for future health systems research efforts to consider issues that arise from both individual- and system-level access barriers and where the two intersect. Oftentimes, challenges that appear linked to a patient or provider may actually stem from an overarching system failure. If failures are critically and properly addressed, we may refine our understanding of what we can do in our professional spaces to improve care as practitioners, workforce developers, researchers and advocates. This qualitative study was exploratory in nature. It represents a step forward in knowledge generation regarding challenges in access to healthcare for rural Americans. Although mental health did not come up by design in this study, future efforts exploring barriers to healthcare access in rural systems should focus on access to mental healthcare. In many rural areas, Montana included, rates of suicide, substance use and other mental health disorders are highly prevalent. These characteristics should be part of the overall discussion of access to healthcare in rural areas. Optimally, barriers to healthcare access should continue to be explored through qualitative and mixed study designs to honor its multi-dimensional stature.

Strengths and Limitations

It is important to note first that this study interviewed healthcare providers instead of patients, which served as both a strength and limitation. Healthcare providers were able to draw on numerous patient-provider experiences, enabling an account of the aggregate which would have been impossible for a patient population. However, accounts of healthcare providers’ perceptions of barriers to healthcare access for their patients may differ from patients’ specific views. Future research should examine acceptability- and appropriateness-related barriers to healthcare access in patient populations. Second, study participants were recruited through convenience sampling methods, so results may be biased towards healthcare providers who are more invested in addressing barriers to healthcare access. Particularly, the providers interviewed for this study represented a subset who go beyond expectations of their job descriptions by engaging with their communities and spending additional uncompensated time with their patients. It is likely that a provider who exhibits these behavioral traits is more likely to participate in research aimed at addressing barriers to healthcare access. Third, the inability to conduct face-to-face interviews for our qualitative study may have posed an additional limitation. It is possible, for example, that in-person interviews might have resulted in increased rapport with study participants. Notwithstanding this possibility, the remote interview format was necessary to accommodate health risks to the ongoing COVID-19 pandemic. Ultimately, given our qualitative approach, results from our study cannot be generalizable to all rural providers’ views or other rural health systems. In addition, no causality can be inferred regarding the influence of aspects of rurality on access. The purpose of this exploratory qualitative study was to probe research questions for future efforts. We also acknowledge the authors’ roles in the research, also known as reflexivity. The first author was the only author who administered interviews and had no prior relationships with all but one study participant. Assumptions and pre-dispositions to interview content by the first author were regularly addressed throughout data analysis to maintain study integrity. This was achieved by conducting analysis by unique interview question, rather than by unique participant, and recoding the numerical order of participants for each question. Our commitment to rigorous qualitative methods was a strength for the study for multiple reasons. Conducting member checks with participants ensured trustworthiness of findings. Continuing data collection to data saturation ensured dependability of findings, which was achieved after 10 interviews and confirmed after 2 additional interviews. We further recognize the heterogeneity in our sample of participants, which helped generate variability in responses. To remain consistent with appropriate means of presenting results in qualitative research however, we shared minimal demographic information about our study participants to ensure confidentiality.

The divide between urban and rural health stretches beyond a disproportionate allocation of resources. Rural health systems serve a more complicated and hard-to-reach patient population. They lack sufficient numbers of providers to meet population health needs. These disparities impact collaboration between patients and providers as well as the delivery of acceptable and appropriate healthcare. The marker of rurality complicates the already cumbersome challenge of administering acceptable and appropriate healthcare and impediments stemming from rurality require continued monitoring to improve patient experiences and outcomes. Our qualitative study explored rural healthcare providers’ views on some of the social, cultural, and programmatic factors that influence access to healthcare among their patient populations. We identified five key themes: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US. This study provides implications that may shift the landscape of a healthcare provider’s approach to delivering healthcare. Further exploration is required to understand the effects these characteristics have on measurable patient-centered outcomes in rural areas.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to individual privacy could be compromised but are available from the corresponding author on reasonable request.

Ethics approval and consent to participate.

All study procedures and methods were carried out in accordance with relevant guidelines and regulations from the World Medical Association Declaration of Helsinki. Ethics approval was given by exempt review from the Institutional Review Board (IRB) at the University of Montana (IRB Protocol No.: 186–20). Participants received oral and written information about the study prior to interview, which allowed them to provide informed consent for the interviews to be recorded and used for qualitative research purposes. No ethical concerns were experienced in this study pertaining to human subjects.

Consent for publication.

The participants consented to the publication of de-identified material from the interviews.

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Acknowledgements

This research was supported by the Center for Biomedical Research Excellence award (P20GM130418) from the National Institute of General Medical Sciences of the National Institute of Health. The first author was also supported by the University of Montana Burnham Population Health Fellowship. We would like to thank Dr. Christopher Dietrich, Dr. Jennifer Robohm and Dr. Eric Arzubi for their contributions on determining inclusion criteria for the healthcare provider population used for this study.

 This research did not receive any specific grant from funding agencies in the public, commercial, and not-for-profit sectors. 

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The authors confirm contribution to the paper as follows: study conception and design: NC and JC; data collection: NC; analysis and interpretation of results: NC and JC; draft manuscript preparation: NC, DC and JC; and manuscript editing: NC, DC and JC. All authors reviewed the results and approved the final version of the manuscript.

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Coombs, N.C., Campbell, D.G. & Caringi, J. A qualitative study of rural healthcare providers’ views of social, cultural, and programmatic barriers to healthcare access. BMC Health Serv Res 22 , 438 (2022). https://doi.org/10.1186/s12913-022-07829-2

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Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets with increased complexity, making feature selection an essential technique for single-cell data analysis. Here, we revisit feature selection techniques and summarise recent developments. We review their application to a range of single-cell data types generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions and finally consider their scalability and make general recommendations on each type of feature selection method. We hope this review stimulates future research and application of feature selection in the single-cell era.

Introduction

High-throughput biotechnologies are at the centre of modern molecular biology, where typically a sheer number of biomolecules are measured in cells and tissues. While significantly higher coverage of molecules is achieved by high-throughput biotechnologies compared to traditional biochemical assays, the variation in sample quality, reagents and workflow introduces profound technical variation in the data. The high dimensionality, redundancy and noise commonly found in these large-scale molecular datasets create significant challenges in their analysis and can lead to a reduction in model generalisability and reliability. Feature selection, a class of computational techniques for data analytics and machine learning, is at the forefront in dealing with these challenges and has been an essential driving force in a wide range of bioinformatics applications [ 1 ].

Until recently, the global molecular signatures generated from most high-throughput biotechnologies have been the average profiles of mixed populations of cells from tissues, organs or patients, and feature selection techniques have been predominately applied to such ‘bulk’ data. However, the recent development of technologies that enables the profiling of various molecules (e.g. DNA, RNA, protein) in individual cells at the omics scale has revolutionised our ability to study various molecular programs and cellular processes at the single-cell resolution [ 2 ]. The accumulation of large-scale and high-dimensional single-cell data has seen renewed interests in developing and the need for applying feature selection techniques to such data given their increased scale and complexity compared to their bulk counterparts.

To foster research in feature selection in the new era of single-cell sciences, we set out to revisit the feature selection literature, summarise its advancement in the last decade and recent development in the field of deep learning and review its current applications in various single-cell data types. We then discuss some key challenges and opportunities that we hope would inspire future research and development in this fast-growing interdisciplinary field. Finally, we consider the scalability and applicability of each type of feature selection methods and make general recommendations to their usage.

Basics of feature selection techniques

Feature selection refers to a class of computational methods where the aim is to select a subset of useful features from the original feature set in a dataset. When dealing with high-dimensional data, feature selection is an effective strategy to reduce the feature dimension and redundancy and can alleviate issues such as model overfitting in downstream analysis. Different from dimension reduction methods (e.g. principal component analysis) where features in a dataset are combined and/or transformed to derive a lower feature dimension, feature selection methods do not alter the original features in the dataset but only identify and select features that satisfy certain pre-defined criteria or optimise certain computational procedures [ 3 ]. The application of feature selection in bioinformatics is widespread [ 1 ]. Some of the most popular research directions include selecting genes that can discriminate complex diseases such as cancers from microarray data [ 4 , 5 ], selecting protein markers that can be used for disease diagnosis and prognostic prediction from mass spectrometry-based proteomics data [ 6 ], identifying single nucleotide polymorphisms (SNPs) and their interactions that are associated with specific phenotypes or diseases in genome-wide association studies (GWAS) [ 7 ], selecting epigenetic features that mark cancer subtypes [ 8 ] and selecting DNA structural properties for predicting genomic regulatory elements [ 9 ]. Traditionally, feature selection techniques fall into one of the three categories including filters, wrappers and embedded methods (Fig. 1 ). In this section, we revisit the key properties and defining characteristics of the three categories of feature selection methods. Please refer to [ 10 ] for a comprehensive survey of feature selection methods.

Schematic illustrations of typical filter ( a ), wrapper ( b ) and embedded methods ( c ) in feature selection

Filter methods typically rank the features based on certain criteria that may facilitate other subsequent analyses (e.g. discriminating samples) and select those that pass a threshold judged by the filtering criteria (Fig. 1 A). In bioinformatics applications, commonly used criteria are univariate methods such as t statistics, on which most ‘differential expression’ (DE) methods for biological data analysis are built [ 11 ], and multivariate methods that take into account relationships among features [ 12 ]. The main advantages of filter methods lie in their simplicity, requiring less computational resources in general and ease of applications in practice [ 13 ]. However, filter methods typically select features independent from the induction algorithms (e.g. classification algorithms) that are applied for downstream analyses, and therefore, the selected features may not be optimal with respect to the induction algorithms in the subsequent applications.

In comparison, wrappers utilise the performance of the induction algorithms to guide the feature selection process and therefore may lead to features that are more conducive to the induction algorithm used for optimisation in downstream analyses [ 14 ] (Fig. 1 B). A key aspect of wrapper methods is the design of the feature optimisation algorithms that maximise the performance of the induction algorithms. Since the feature dimensions are typically very high in bioinformatics applications, exhaustive search is often impractical. To this end, various greedy algorithms, such as forward and backward selection [ 15 ], and nature-inspired algorithms, such as the genetic algorithm (GA) [ 16 ] and the particles swarm optimisation (PSO) [ 17 ], were employed to speed up the optimisation and feature selection processes. Nevertheless, since the induction algorithms are included to iteratively evaluate feature subsets, wrappers are typically computationally intensive compared to filter methods.

While filters and wrappers separate feature selection from downstream analysis, embedded methods typically perform feature selection as part of the induction algorithm itself [ 18 ] (Fig. 1 C). Akin to wrappers, embedded methods optimise selected features with respect to an induction model and therefore may lead to more suited features for the induction algorithm in subsequent tasks such as sample classification. Since the embedded methods perform feature selection and induction simultaneously, it is also generally more computationally efficient than wrapper methods albeit less so when compared to filter methods [ 19 ]. Nevertheless, as feature selection is part of the induction algorithm in embedded methods, they are often specific to the algorithmic design and less generic compared to filters and wrappers. Popular choices of embedded methods in bioinformatics applications include tree-based methods [ 20 , 21 ] and shrinkage-based methods such as LASSO [ 22 ].

Advance of feature selection in the past decade

Besides the astonishing increase in the number of feature selection techniques in the last decade, we have also seen a few notable trends in their development. Here, we summarise three aspects that have shown proliferating research in various fields and applications, including bioinformatics.

First, a variety of approaches have been proposed for ensemble feature selection, including those for filters [ 23 , 24 ], wrappers [ 25 ] and embedded methods such as tree-based ensembles [ 26 ]. Ensemble learning is a well-established approach where instead of building a single model, multiple ‘base’ models are combined to perform tasks [ 27 ]. Supervised ensemble classification models are popular among bioinformatics applications [ 28 ] and have recently seen their increasing integration with deep learning models [ 29 ]. Similar to their counterpart in supervised learning, ensemble feature selection methods, typically, rely on either perturbation to the dataset or hyperparameters of the feature selection algorithms for creating ‘base selectors’ from which the ensemble could be derived [ 30 ]. Examples include using different subsets of samples for creating multiple filters or using different learning parameters in an induction algorithm of a wrapper method. Key attributes of ensemble feature selection methods are that they generally achieve better generalisability in sample classification [ 31 ] and higher reproducibility in feature selection [ 32 , 33 ]. Although these improvements in performance typically come with a cost on computational efficiency, ensemble feature selection methods are increasingly popular given the increasing computational capacity in the last decade and the parallelisation in some of their implementations [ 34 , 35 , 36 ].

Second, various hybrid methods have been proposed to combine filters, wrappers and embedded methods [ 37 ]. While these methods closely resemble ensemble approaches, they do not rely on data or model perturbations but instead use heterogeneous feature selection algorithms for creating a consensus [ 38 ]. Typically, these include combining different filter algorithms or different types of feature selection algorithms (e.g. stepwise combination of filter and wrapper). Generally, hybrid methods are motivated by the aim of taking advantage of the strengths of individual methods while alleviating or avoiding their weaknesses [ 39 ]. For example, in bioinformatics applications, several methods combine filters with wrappers in that filters are first applied to reduce the number of features from high dimension to a moderate number so that wrappers can be employed more efficiently for generating the final set of features [ 40 , 41 ]. As another example, genes selected by various feature selection methods are used for training a set of support vector machines (SVMs) for achieving better classification accuracy using microarray data [ 42 ]. While many hybrid feature selection algorithms are intuitive and numerous studies have reported favourable results compared to their individual components, a fundamental issue of these methods is their ad hoc nature, complicating the formal analysis of their underlying properties, such as theoretical algorithmic complexity and scalability.

Third, a recent evolution in feature selection has been its development and implementation using deep learning models. These include models based on perturbation [ 43 , 44 ], such as randomly excluding features to test their impact on the neural network output, and gradient propagation, where the gradient from the trained neural network is backpropagated to determine the importance of the input feature [ 45 , 46 ]. These deep learning feature selection models share a common concept of ‘saliency’ which was initially designed for interpreting black-box deep neural networks by highlighting input features that are relevant for the prediction of the model [ 47 ]. Some examples in bioinformatics applications include a deep feature selection model that uses a neural network with a weighted layer to select key input features for the identification and understanding regulatory events [ 48 ]; and a generative adversarial network approach for identifying genes that are associated with major depressive disorders using gradient-based methods [ 49 ]. While feature selection methods that are based on deep learning generally require significantly more computational resources (e.g. memory) and may be slower than traditional methods (especially when compared to filter methods), their capabilities for identifying complex relationships (e.g. non-linearity, interaction) among features have attracted tremendous attention in recent years.

Feature selection in the single-cell era

Until recently, the global molecular signatures generated from most biotechnologies are the average profiles from mixed populations of cells, masking the heterogeneity of cell and tissue types, a foundational characteristic of multicellular organisms [ 50 ]. Breakthroughs in global profiling techniques at the single-cell resolution, such as single-cell RNA-sequencing (scRNA-seq), single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) [ 51 ] and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) [ 52 ], have reshaped many of our long-held views on multicellular biological systems. These advances of single-cell technologies create unprecedented opportunities for studying complex biological systems at resolutions that were previously unattainable and have led to renewed interests in feature selection for analysing such data. Below we review some of the latest developments and applications of feature selection across various domains in the single-cell field. Table 1 summarises the methods and their applications with additional details included in Additional file 1 : Table S1.

Feature selection in single-cell transcriptomics

By far, the most widely applied single-cell omics technologies are single-cell transcriptomics [ 53 ] made popular by an array of scRNA-seq protocols [ 54 ]. Given the availability of a huge amount of scRNA-seq data and the large number of genes profiled in these datasets, a similar characteristic of their bulk counterparts, most of recent feature selection applications in single-cell transcriptomics have been concentrated on gene selection from scRNA-seq data for various upstream pre-processing and downstream data analyses.

Among these, some of the most popular methods are univariate filters designed for identifying differential distributed genes, including t statistics or ANOVA based DE methods [ 55 , 56 ] and other statistical approaches such as differential variability (DV) [ 57 ] and differential proportion (DP) [ 58 ]. While differential distribution-based methods can often identify genes that are highly discriminative for downstream analysis, they require labels such as cell types to be pre-defined, limiting their applicability when such information is not available. A less restrictive and widely used alternative approach is to filter for highly variable genes (HVGs), which is implemented in various methods including the popular Seurat package [ 59 ]. Other methods that do not require label information include SCMarker which relies on testing the number of modalities of each gene through its expression profile [ 60 ], M3Drop which models the relationship between mean expression and dropout rate [ 61 ], and OGFSC, a variant of HVGs, based on modelling coefficient of variance of genes across cells [ 62 ]. Many scRNA-seq clustering algorithms also implement HVGs and its variants for gene filtering to improve the clustering of cells [ 63 ]. Besides the above univariate filters, recent research has also explored multivariate approaches. Examples include COMET which relies on a modified hypergeometric test for filtering gene pairs [ 64 ] and a multinomial method for gene filtering using the deviance statistic [ 65 ].

While filters are the most common options for pre-processing and feature selection from single-cell transcriptomics data, the application of wrapper methods is gaining much attention with a range of approaches built and extends on classic methods with the primary goal of facilitating downstream analyses such as cell type classification. Some examples include the application of classic methods such as greedy-based optimisation of entropy [ 66 ], nature-inspired optimisation such as using GA [ 67 , 68 ], and their hybrid with filters [ 69 , 70 , 71 ] or embedded methods [ 72 ]. More advanced methods include active learning-based feature selection using SVM as a wrapper [ 73 ] and optimisation based on data projection [ 74 ]. The impact of optimal feature selection using wrapper methods on improving cell type classification is well demonstrated through these studies.

Due to the simplicity in their application, the popularity of embedded methods is growing quickly in the last few years especially in studies that treat feature selection as a key goal in their analyses. These include the discovery of the minimum marker gene combinations using tree-based models [ 75 ], discriminative learning of DE genes using logistic regression models [ 76 ], regulatory gene signature identification using LASSO [ 77 ] and marker gene selection based on compressed sensing optimisation [ 78 ].

Lastly, several studies have compared the effect of various feature selection methods on the clustering of cell types [ 63 ] and investigated factors that affect feature selection in cell lineage analysis [ 79 ]. Together, these studies demonstrate the utility and flexibility of feature selection techniques in a wide range of tasks in single-cell transcriptomic data analyses.

Feature selection in single-cell epigenomics

Besides single-cell transcriptomic profiling, another fast-maturing single-cell omics technology is single-cell epigenomics profiling using scATAC-seq [ 51 ]. In particular, scATAC-seq measures genome-wide chromatin accessibility and therefore can provide a clue regarding the activity of epigenomic regulatory elements and their transcription factor binding motifs in single cells. Such data can offer additional information that is not accessible to scRNA-seq technologies and hence can complement and significantly enrich scRNA-seq data for characterising cell identity and gene regulatory networks (GRNs) in single cells [ 80 ]. Although most application of feature selection has been on investigating single-cell transcriptomes, recent studies have broadened the view to single-cell epigenomics primarily through their application in scATAC-seq data analysis. These analyses enable us to expand the gene expression analysis to also include regulatory elements such as enhancers and silencers in understanding molecular and cellular processes.

Feature selection methods could be directly applied to scATAC-seq data for identifying differential accessible chromatin regions or one can summarise scATAC-seq data to the gene level using tools such as those reviewed in [ 81 ] and then feature selection be performed for selecting ‘differentially accessible genes’ (DAGs) using such summarised data. For instance, Scasat, a tool for classifying cells using scATAC-seq data, implements both information gain and Fisher’s exact test for filtering and selecting differential accessible chromatin regions [ 82 ]. Similarly, scATAC-pro, a pipeline for scATAC-seq analysis at the chromatin level, employs Wilcoxon test as the default for filtering differential accessible chromatin regions, while also implements embedded methods such as logistic regression and negative binomial regression-based models as alternative options [ 83 ]. Another example is SnapATAC [ 84 ] which performs differential accessible chromatin analysis using the DE method implemented in edgeR [ 85 ]. In contrast, Kawaguchi et al. [ 86 ] summarised scATAC-seq data to the gene level using SCANPY [ 87 ] and performed embedded feature selection using either logistic LASSO or random forests to identify DAGs [ 86 ]. Muto et al. [ 88 ] performed filter-based differential analysis on both chromatin and gene levels based on Cicero estimated gene activity scores [ 89 ]. Finally, DUBStepR [ 71 ], a hybrid approach that combines a correlation-based filter and a regression-based wrapper for gene selection from scRNA-seq data, can also be applied to scATAC-seq data. Collectively, these methods and tools demonstrate the utility and impact of feature selection on scATAC data for cell-type identification, motif analysis, regulatory element and gene interaction detection among other applications.

Feature selection for single-cell surface proteins

Owing to the recent advancement in flow cytometry and related technologies such as mass cytometry [ 90 , 91 ], and single-cell multimodal sequencing technologies such as CITE-seq [ 52 ], surface proteins of the cells have now also become increasingly accessible at the single-cell resolution.

A key application of feature selection methods to flow and mass cytometry data has been for finding optimal protein markers for cell gating [ 92 ]. A representative example is GateFinder which implements a random forest-based feature selection procedure for optimising stepwise gating strategies on each given dataset [ 93 ]. Besides automated gating, several studies have also explored the use of feature selection for improving model performance on sample classification. For example, in their study, Hassan et al. [ 94 ] demonstrated the utility of shrinkage-based embedded models for classifying cancer samples. Another application of feature selection techniques was recently demonstrated by Tanhaemami et al. [ 95 ] for discovering signatures from label-free single cells. In particular, the authors employed a GA for feature selection and verified its utility in predicting lipid contents in algal cells under different conditions. Together, these studies illustrate the wide applicability of feature selection methods in a wide range of challenges in flow and mass cytometry data analysis.

Recent advancement in single-cell multimodal sequencing technologies such as CITE-seq and other related techniques such as RNA expression and protein sequencing (REAP-seq) [ 96 ] has enabled the profiling of both surface proteins and gene expressions at the single-cell level. While still at its infancy, feature selection techniques have already found their use in such data. One example is the application of a random forest-based approach for selecting marker proteins that can distinguish closely related cell types profiled using CITE-seq from PBMCs isolated from the blood of healthy human donors [ 97 ]. Another example is the use of a greedy forward feature selection wrapper that maximises a logistic regression model for identifying surface protein markers for each cell type from a given CITE-seq dataset [ 98 ].

Feature selection in single-cell imaging data

Other widely accessible data at the single-cell resolution are imaging-related data types such as those generated by image cytometry [ 99 ] and various single-cell imaging techniques [ 100 ]. Although the application of feature selection methods in this domain is very diverse, the following examples provide a snapshot of different types of feature selection techniques used for single-cell imaging data analysis.

To classify cell states using imaging flow cytometry data, Pischel et al. [ 101 ] employed a set of filters, including mutual information maximisation, maximum relevance minimum redundancy and Fisher score, for feature selection and demonstrated their utility on apoptosis detection. To predict cell cycle phases, Hennig et al. [ 102 ] implemented two embedded feature selection techniques, gradient boosting and random forest, for selecting the most predictive features from image cytometry data. These implementations are included in the CellProfiler, open-source software for imaging flow cytometry data analysis. To improve data interpretability of single-cell imaging data, Peralta and Saeys [ 103 ] proposed a clustering-based method for selecting representative features from each cluster and thus significantly reducing data dimensionality. To classify cell phenotypes, Doan et al. [ 104 ] implemented supervised and weakly supervised deep learning models in a framework called Deepometry for feature selection from imaging cytometry data. To classify cells according to their response to insulin stimulation, Norris et al. [ 105 ] used a random forest approach for ranking the informativeness of various temporal features extracted from time-course live-cell imaging data. Finally, to select spatially variable genes from imaging data generated by multiplexed single-molecule fluorescence in situ hybridization (smFISH), Svensson et al. [ 106 ] introduced a model based on the Gaussian process regression that decomposes expression and spatial information for gene selection.

Upcoming domains and future opportunities

The works reviewed above covers some of the most popular single-cell data types. Nevertheless, the technological advances in the single-cell field are extending our capability at a breakneck speed, enabling many other data modalities [ 107 ] as well as the spatial locations [ 108 ] of individual cells to be captured in high-throughput. For instance, recent development in single-cell DNA-sequencing provides the opportunity to analyse SNPs and copy-number variations (CNVs) in individual cells from cancer and normal tissues [ 109 , 110 ], and single-cell proteomics seems now on the horizon [ 111 , 112 ], holding great promises to further transform the single-cell field. Given the high feature-dimensionality of such data (e.g. numbers of SNPs, proteins and spatial locations), we anticipate feature selection techniques to be readily adopted for these single-cell data types when they become more available.

Another fast-growing capability in the single-cell field is increasingly towards multimodality. CITE-seq and REAP-seq are examples where both the gene expression and the surface proteins are measured in each individual cell. Nevertheless, many more recent techniques now also enable other combinations of modalities to be profiled at the single-cell level (Fig. 2 ). Some examples include ASAP-seq for profiling gene expression, chromatin accessibility and protein levels [ 113 ]; scMT-seq for profiling gene expression and DNA methylation [ 114 ] and its extension, scNMT-seq, for gene expression, chromatin accessibility and DNA methylation [ 115 ]; SHARE-seq and SNARE-seq for gene expression and chromatin accessibility [ 116 , 117 ]; scTrio-seq for CNVs, DNA methylation and gene expression [ 118 ]; and G&T-seq for genomic DNA and gene expression [ 119 ]. Given the complexity in the data structure in these single-cell multimodal data, feature selection methods that can facilitate integrative analysis of multiple data modalities are in great need. While some preliminary works have emerged recently [ 120 ], research on integrative feature selection is still at its infancy and requires significant innovation in their design and implementation.

A schematic summary of some recent multimodal single-cell omics technologies

On the design of feature selection techniques in the single-cell field, most current studies directly use one of the three main types of methods (i.e. filters, wrappers and embedded methods). While we found a small number of them employed hybrid approaches (e.g. [ 71 , 72 ]), most are relatively straightforward combinations (such as stepwise application of filter and then wrapper methods) as have been used previously for bulk data analyses. The application of ensemble and deep learning-based feature selection methods is even sparser in the field. One ensemble feature selection method is EDGE which uses a set of weak learners to vote for important genes from scRNA-seq data [ 121 ], and the current literature on deep learning-based feature selection in single cells are a study for identifying regulatory modules from scRNA-seq data through autoencoder deconvolution [ 122 ]; and another for identifying disease-associated gene from scRNA-seq data using gradient-based methods [ 49 ]. Owing to the non-linear nature of the deep learning models, feature selection methods that are based on deep learning are well-suited to learn complex non-linear relationships among features. Given the widespread non-linearity relationships, such as gene-gene and protein-protein interactions, and interactions among genomic regulatory elements and their target genes in biological systems, and hence the data derived from them, we anticipate more research to be conducted on developing and adopting deep learning-based feature selection techniques in the single-cell field in the near future.

Applicability considerations

The works we have reviewed above showcase diverse feature selection strategies and promising future directions in single-cell data analytics. In practice, scalability and robustness are critical in choosing feature selection techniques and are largely dependent on the algorithm structure and implementation. Here, we discuss several key aspects specific to the utility and applicability of feature selection methods with the goal of guiding the choice of methods from each feature selection category for readers who are interested in their application.

Scalability towards the feature dimension

A key aspect in the applicability of a feature selection method rests upon its scalability to large datasets. Univariate filter algorithms are probably the most efficient in terms of scalability towards the feature dimension since, in general, the computation time of these algorithms increases linearly with the number of features. We therefore recommend univariate filters as the first choice when working with datasets with very high feature dimensions. In comparison, wrapper algorithms generally do not scale well with respect to the number of features due to their frequent reliance on combinatorial optimisation and therefore will remain applicable to datasets with a relatively small number of features. While other factors such as available computational resources and specific algorithm implementations also affect the choice of methods, wrapper algorithms are generally applied to datasets with up to a few hundred features. Embedded methods offer a good trade-off and both tree- and shrinkage-based methods computationally scale well with the number of features [ 19 ]. Nevertheless, like wrapper methods, embedded methods rely on an induction algorithm for feature selection and therefore are sensitive to model overfitting when dealing with data with a small sample size. We recommend choosing embedded methods for datasets with up to a few thousand features when the sample size (e.g. number of cells) is moderate or large. Similarly, hybrid algorithms that combine the filter with wrappers or filter with embedded methods also make a useful compromise and can be applied to the dataset with relatively high to very high feature dimensions, depending on the reduced feature dimension following the filtering step.

Scalability towards the sample size

With the advance of biotechnologies, the number of cells profiled in an experiment is growing exponentially. Hence, apart from the feature dimensionality, the scalability of the feature selection algorithm towards the sample size, typically in terms of the number of cells, is also a central determinant of its applicability to large-scale single-cell datasets. Although classic feature selection algorithms such as filters scale linearly towards the feature dimension, this does not necessarily mean they also scale linearly with the increasing number of cells [ 55 ]. To this end, the choice is more dependent on the specific implementation of the feature selection algorithms. Methods that purely rely on estimating variabilities (e.g. HVGs) without using cell type labels and fitting models generally scale better due to the extra steps taken by the latter for learning various data characteristics (e.g. zero-inflation). Another aspect to note is the memory usage. Most filter methods require the entire dataset to be loaded into the computer memory before feature selection can be performed. This can be an issue when the size of the dataset exceeds the size of the computer memory. Interestingly, deep learning-based feature selection methods could be better suited for analysing datasets with a very large number of cells. This is due to the unique characteristic of these methods where the neural network can be trained using small batches of input data sequentially and therefore alleviates the need to load the entire dataset into the computer memory.

Robustness and interpretability

Besides algorithm scalability, robustness and interpretability are also important criteria for assessing and selecting feature selection methods. This is especially crucial when the downstream applications are to identify reproducible biomarkers, where the selection of robust and stable features is essential, or to characterise gene regulatory networks, where model interpretability will be highly desirable. A key property of ensemble feature selection methods is their robustness to noise and slight variations in the data, which leads to better reproducibility in selected features [ 32 , 33 ]. We thus recommend exploring ensemble feature selection methods when the task is related to identify reproducible biomarkers such as marker genes for cells of a given type. In terms of interpretability, complex models, while often offering better performance in downstream analyses such as cell classification, may not be the most appropriate choices given the difficulties in their model interpretation. To this end, simpler models such as tree-based methods can provide clarity, for example, to how selected features are used to classify a cell and hence can facilitate the characterisation of gene regulatory networks underlying cell identity. Notably, however, significant progress has been made to improve interpretability especially for deep learning models [ 123 ]. Given the increasing importance of downstream analyses that involves biomarker discovery and pathway/network characterisation in single-cell research, we anticipate increasing efforts to be devoted to improving the robustness and interpretability of advanced methods such as deep learning models in feature selection applications.

Other considerations

Finally, the choice of feature selection methods also depends on other factors such as programming language, computing platform, parallelisation and whether they are well documented and easy to use. While most recent methods are implemented using popular programming languages such as R and Python which are well supported in various computing platforms including Windows, macOS and Linux/Unix and its variants, their difficulty in application varies and requires different levels of expertise from interacting with a simple graphical user interface to more complex execution that involves programming (e.g. loading packages in the R programming environment). Methods that optimise for computation speed may use C/C++ as their programming language and may also offer parallelisation. However, these methods are often computing platform-specific and may require more expertise from a specific operating system and programming language from users for their application. Lastly, the quality of the documentation of methods can have a significant impact on their ease of use. Methods that have comprehensive documentations with testable examples could help popularise their application. To this end, methods that are implemented under standardised framework such as Bioconductor [ 124 ] generally provide well-documented usages and examples known as ‘vignette’ for supporting users and therefore can be a practical consideration in their choices.

Conclusions

The explosion of single-cell data in recent years has led to a resurgence in the development and application of feature selection techniques for analysing such data. In this review, we revisited and summarised feature selection methods and their key development in the last decade. We then reviewed the recent literature for their applications in the single-cell field, summarising achievements so far and identifying missing aspects in the field. Based on these, we propose several research directions and discuss practical considerations that we hope will spark future research in feature selection and their application in the single-cell era.

Availability of data and materials

Not applicable.

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Yang, P., Huang, H. & Liu, C. Feature selection revisited in the single-cell era. Genome Biol 22 , 321 (2021). https://doi.org/10.1186/s13059-021-02544-3

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ORIGINAL RESEARCH article

Decomposing the inequalities in the catastrophic health expenditures on the hospitalization in india: empirical evidence from national sample survey data.

• 1 Department of Social and Public Health, College of Health Sciences and Professions, Ohio University, Athens, OH, United States
• 2 Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
• 3 Chettinad Hospital and Research Institute, Chennai, Tamil Nadu, India
• 4 Taibah University, Medina, Saudi Arabia

Introduction: Sustainable Development Goal (SDG) Target 3.8.2 entails financial protection against catastrophic health expenditure (CHE) by reducing out-of-pocket expenditure (OOPE) on healthcare. India is characterized by one of the highest OOPE on healthcare, in conjunction with the pervasive socio-economic disparities entrenched in the population. As a corollary, India has embarked on the trajectory of ensuring financial risk protection, particularly for the poor, with the launch of various flagship initiatives. Overall, the evidence on wealth-related inequities in the incidence of CHE in low- and middle-Income countries has been heterogenous. Thus, this study was conducted to estimate the income-related inequalities in the incidence of CHE on hospitalization and glean the individual contributions of wider socio-economic determinants in influencing these inequalities in India.

Methods: The study employed cross-sectional data from the nationally represented survey on morbidity and healthcare (75th round of National Sample Survey Organization) conducted during 2017–2018, which circumscribed a sample size of 1,13,823 households and 5,57,887 individuals. The inequalities and need-adjusted inequities in the incidence of CHE on hospitalization care were assessed via the Erreygers corrected concentration index. Need-standardized concentration indices were further used to unravel the inter- and intra-regional income-related inequities in the outcome of interest. The factors associated with the incidence of CHE were explored using multivariate logistic regression within the framework of Andersen’s model of behavioral health. Additionally, regression-based decomposition was performed to delineate the individual contributions of legitimate and illegitimate factors in the measured inequalities of CHE.

Results: Our findings revealed pervasive wealth-related inequalities in the CHE for hospitalization care in India, with a profound gap between the poorest and richest income quintiles. The negative value of the concentration index (EI: −0.19) indicated that the inequalities were significantly concentrated among the poor. Furthermore, the need-adjusted inequalities also demonstrated the pro-poor concentration (EI: −0.26), denoting the unfair systemic inequalities in the CHE, which are disadvantageous to the poor. Multivariate logistic results indicated that households with older adult, smaller size, vulnerable caste affiliation, poorest income quintile, no insurance cover, hospitalization in a private facility, longer stay duration in the hospital, and residence in the region at a lower level of epidemiological transition level were associated with increased likelihood of incurring CHE on hospitalization. The decomposition analysis unraveled that the contribution of non-need/illegitimate factors (127.1%) in driving the inequality was positive and relatively high vis-à-vis negative low contribution of need/legitimate factors (35.3%). However, most of the unfair inequalities were accounted for by socio-structural factors such as the size of the household and enabling factors such as income group and utilization pattern.

Conclusion: The study underscored the skewed distribution of CHE as the poor were found to incur more CHE on hospitalization care despite the targeted programs by the government. Concomitantly, most of the inequality was driven by illegitimate factors amenable to policy change. Thus, policy interventions such as increasing the awareness, enrollment, and utilization of Publicly Financed Health Insurance schemes, strengthening the public hospitals to provide improved quality of specialized care and referral mechanisms, and increasing the overall budgetary share of healthcare to improve the institutional capacities are suggested.

1 Introduction

The Universal Health Coverage (UHC) has been proclaimed as the third major transition in health, after the demographic and epidemiological transitions ( 1 ) and has become the focal point of health policy discourse as the world made transition from millennium development goals (MDSs) to sustainable development goals (SDGs). Goal 3.8 of the SDG Agenda enunciates to achieve the UHC and encompasses two components: (i) Indicator 3.8.1–Coverage of essential health services (defined as average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, and service capacity and access, among the general and most disadvantaged population). (ii) Indicator 3.8.2–Incidence of catastrophic health spending (defined as the proportion of the population with large household expenditures on health as a share of total household expenditure or income). Despite the institutional commitment, there is an inordinate reliance on out-of-pocket-expenditure (OOPE) to finance healthcare due to the severely underfunded health system. For India, specifically, the public health expenditure as a share of GDP (1.25%) is the lowest in the world. Furthermore, the estimates from the National Health Accounts of India divulged that abysmally low coverage of government-sponsored pre-payment schemes coupled with the dearth of private health insurance has impelled households to have excessive reliance on out-of-pocket payments (58.7% of total health expenditure) for healthcare ( 2 ).

Healthcare expenditures or costs are incurred whenever a person accesses the healthcare system and utilizes the healthcare services. Health expenditures could be broadly defined as any expense that is spent on healthcare and related activities, including paying premiums for private or public health insurance coverage ( 3 ). A multitude of cost components encompasses healthcare payments on hospitalization, such as direct medical costs related to user fees, made at the time of health service use, incorporating charges ranging from registration, consultation, drugs, diagnostics, bed charges, etc. A legion of studies examining the impact of user fees on healthcare-seeking behavior in LMICs have conceded that the higher user fee/increase in prices can lead to decreased healthcare utilization and vice-versa ( 4 – 6 ). Literature in the Indian context underscores the impact of user charges and direct medical costs, specifically on drugs and diagnostics ( 7 , 8 ). In addition to the direct cost, indirect costs, such as expenses on food, lodging, and transportation, also account for a large proportion of OOPE, as evinced in the literature from LMICs and India ( 9 – 13 ). Furthermore, other invisible costs that were not incurred because of medical management of disease but rather of other incurred losses, such as lost wages, lost productivity, and costs resulting from the need for home care and child care otherwise not incurred, also pose a formidable barrier to access.

The unprecedented level of financial burden posed by healthcare expenditures has two-pronged implications. First, at the macroeconomic level, the burden posed by forgone care due to affordability barriers has a deleterious impact on the economic growth of the region due to loss in productivity. Second, out-of-pocket health payments precipitate an adverse shock on the financial stability of households incurring such expenditure, subsequently rendering the households vulnerable to catastrophic health expenditure and impoverishment due to income shocks perpetuated via health shocks, which can further potentially culminate into a trans-generational cycle of poverty, bearing long-term consequences. Health shock is the most common idiosyncratic income shock and one of the most pertinent reasons for the descent of households into poverty in LMICs ( 14 ).

The out-of-pocket payments for healthcare are usually the most inequitable type of finance due to its tendency to hit the poor the hardest by being a barrier to healthcare/by denying individuals’ financial protection from catastrophic illness ( 15 ). Studies from India have established the Inverse Care Law, i.e., individuals with the greatest need for healthcare have the greatest difficulty in accessing healthcare services ( 16 – 18 ). There is strong evidence that financial access to healthcare is very low among those residing in rural areas, uneducated, lowest wealth quintile, and otherwise marginalized sections of society ( 19 ). In a resource-poor setting, there are substantial heterogeneities in healthcare measures and capacity to pay thereof; as a corollary, pervasive income-based inequalities in the economic burden of care on the households are pronounced in these settings as well. A systematic review of LMICs has evinced that across all the LMICs, the risk of incurring CHE is six times more concentrated among the poor ( 20 ). Furthermore, evidence on hospitalization from countries such as Argentina, China, India, and Tanzania also revealed the disproportionate impact of CHE on the poor ( 21 ). Although there is some literature on the impact of socio-economic inequalities on the incidence of catastrophic payments in the Indian context ( 22 – 24 ), the evidence is rather exiguous and does not commensurate with the policy implications.

In India, the National Health Policy 2017 ( 25 ) directed that budgetary allocations would ensure horizontal equity by targeting specific population subgroups, geographical areas, healthcare services, and gender-related issues. Horizontal equity entails equal treatment for equal needs, irrespective of other socio-economic characteristics such as income, education, place of residence, and social group. Meanwhile, vertical equity connotes unequal treatment for unequal needs. However, the measurement of horizontal inequities is quite complex vis-a-vis vertical inequality, as need is a rather elusive concept both in terms of the choice of measurable indicators and also normative ethical considerations ( 26 ). However, the degree to which health inequality is considered inequitable is estimated via the need-adjustment of inequality. Literature commonly suggests that people with similar health statuses have the same needs and persons with dissimilar health statuses have different needs ( 27 ). The need-based variables are not amenable to the policy intervention and, thus, considered as fair or legitimate variables, whereas non-need variables are due to systemic inequalities and are amenable to policy intervention, thus, considered as unfair or illegitimate. Therefore, standardizing the inequality in health outcomes by need results in systematic disparities and captures the degree to which the inequality is inequitable.

The systemic inequalities along the socio-economic gradient with respect to the burden of healthcare payments continue to pose an unprecedented challenge in India despite the launch of various initiatives to provide financial risk protection to the poor and vulnerable. Previous studies have revealed that the incidence of CHE on hospitalization care has increased in the last few decades in India ( 24 ). However, the evidence of the impact of these initiatives in reducing the catastrophic burden among poor households remains elusive. Thus, it becomes imperative to explore the dimension of equity w.r.t. incidence of the catastrophic burden of out-of-pocket payments to correct existing interventions and promulgate inclusive policies.

However, there is a dearth of literature to study the need-adjusted inequities in the incidence of CHE for hospitalization care, and, further, to the best of our knowledge, no study has been conducted to decompose the effect of the legitimate and illegitimate factors causing the inequalities in the CHE. At the same time, it is pertinent to decompose and identify the need and non-need factors that affect the health and financial protection in the household to enable the targeted policy response. Thus, this study was conducted to estimate the degree of inequalities and need-adjusted inequities in the incidence of CHE for hospitalization care using a modified Erreygers concentration index. Furthermore, wider socio-economic-contextual determinates influencing the CHE on hospitalization care were unraveled succinctly within a conceptual framework. Additionally, the study also attempted to measure the relative contributions of need and non-need factors driving the inequality in the CHE by conducting a robust regression-based decomposition of the inequalities to identify the key variables for the policy response.

The study employed national representative unit-level cross-sectional data from the 75th round of the National Sample Survey Organization (Household Social Consumption in India: Health) . The survey was conducted under the stewardship of the Ministry of Statistics and Program Implementation , Government of India, during the time period of July 2017–June 2018. The survey schedule collects information pertaining to the demographic - socio-economic characteristics , morbidity status , utilization of healthcare services, and healthcare expenditure across ambulatory, inpatient, delivery, and immunization care for households and individuals. A two-stage stratified random sampling design was adopted in the survey with census villages and urban blocks as the First Stage Units for rural and urban areas, respectively, and households as the Second Stage Units. The overall sample size consisted of 1,13,823 households and 5,57,887 individuals (including the death cases). The analysis, however, circumscribed 66,237 individuals who were hospitalized in the last 365 days of the survey (without childbirth episodes). For this study, the information encompassing both medical expenses such as doctor’s/surgeon’s fee, medicines, diagnostic tests, bed charges, and consumables, viz. blood, oxygen, etc., and non-medical expenses such as expenses incurred on transportation, food, and lodging on account of treatment was employed in the study. Detailed information on the survey design can be found in the official report released by the National Sample Survey Organization ( 28 ).

2.2 Measures

The following measures were assessed in the study: (a) Extent of CHE on hospitalization cases in India; (b) Wealth-related inequities in the incidence of CHE on hospitalization; (c) Socio-economic-demographic factors impacting the CHE on hospitalization cases; and (d) Relative contribution of the factors in driving the wealth-based inequality in the CHE for hospitalization cases.

2.2.1 Outcome measure

The survey encompasses information on the expenses incurred in hospital treatment (medical and non-medical). The medical component subsumed data on the expenses toward the doctor’s/surgeon’s fee, medicines, diagnostics, bed charges, physiotherapy, personal medical appliances, and other consumables such as oxygen and blood. However, the non-medical component incorporated the expenses incurred on other ancillary payments, such as transportation, lodging, and food for the patient and caretaker, on account of the treatment. Given the information, the out-of-pocket expenditure (OOPE) is then defined as the direct payments made by the patients at the time of treatment, net of any reimbursements by the insurance provider. The CHE can be defined via two approaches, i.e., (a) capacity-to-pay approach and (b) budget-share approach. Under the capacity-to-pay approach, the OOPE on healthcare is considered catastrophic if a household’s financial contributions to the healthcare treatment exceed the 40% of income remaining after the subsistence needs have been met ( 29 , 30 ). Meanwhile, under the Budget-share approach, the OOPE is catastrophic if a household’s financial contribution to the treatment equals or exceeds 10% of the household’s total expenditure ( 31 , 32 ). In this study, the CHE was computed using the budget-share approach, where a 10% threshold of total household expenditure was considered. The outcome variable of interest in the study was binary in nature, indicating whether a household faced CHE on inpatient treatment.

2.2.2 Covariates

A gamut of household and individual level variables, drawn from Andersen’s behavioral health model ( 33 ), were incorporated into the study. The covariates were cogitated into legitimate/need and illegitimate/non-need variables to unravel the horizontal inequities underlying the CHE. The need for healthcare is considered an elusive concept, and the choice of variables is embedded in the normative categorization, which requires a potentially contestable value judgment ( 27 ). In general, the need sources of variation in health are ethically acceptable, whereas the non-need sources are ethically unjust or unfair ( 34 ). The variables underscoring the differential need for healthcare expenditure, viz. demographic characteristics, health status, and severity of ailments, such as age composition of household members, number of chronic members, hospitalization cases in households, and duration of stay in the hospital, were considered as the need-based variables in the study.

A myriad of factors impacted the choice of non-need variables, such as previous literature ( 35 – 37 ), relevance to explaining the inequality within the available dataset, and availability of periodic and routine monitoring of the indicators. A broad spectrum of household-level variables across the demographic characteristics such as age and gender of the household head, household size, and marital status of the household members; socio-economic characteristics, such as education, social group, religion, principal occupation of the household, monthly household consumption expenditure, and housing conditions (comprehensive indicator coalescing information on the drinking water source, cooking source, drainage type, and garbage disposal) ; enabling characteristics, such as insurance coverage and type of facility where care is sought; and contextual var iables such as the level of epidemiological transition level of the residential region and the geographical location (urban/rural) were chosen as the non-need variables. The monthly household consumption expenditure was adjusted to account for the economies of scale in household consumption stemming from the household size and demographic composition due to underlying differences in need among the household members using the Oxford equivalence scale ( 38 ). Furthermore, the monthly consumption household expenditure was converted to the annual expenditure to make it uniform with the expenses incurred on hospitalization with a recall period of 365 days.

2.3 Statistical analysis

2.3.1 incidence of catastrophic health expenditure.

The incidence of catastrophic health expenditure was computed via a budget-share approach and elucidated as the share of out-of-pocket health expenditure and out of the total household expenditure:

Where, O O P E i is the out-of-pocket expenditure of household i , T H E i is the household’s total consumption expenditure of household i , and S i is the share of the total healthcare expenditure out of the total consumption expenditure of household i . Consider Z i is the threshold beyond which the household i incurs catastrophic expenditure if S i > 10 % , which can be represented as:

2.3.2 Concentration curve and index

The concentration curve was used to glean the inequities in the CHE on hospitalization care. Cumulative proportions of the catastrophic health payment (vertical axis) were plotted against the cumulative proportion of the households with hospitalization cases (horizontal axis), ranked by the equivalized household consumption expenditure. The concentration index, denoted by C, is estimated as twice the area between the concentration curve and diagonal, which is represented as:

where, C H E i is the variable of interest for the household; μ is the mean of C H E i ; and R i is the i t h ranked household in the socio-economic distribution from most disadvantaged (i.e., poorest) to the least disadvantaged (i.e., richest). The value of C I ranges between −1 and + 1, where a positive value indicates the distribution concentrated among the rich and a negative value represents a distribution concentrated among the poor.

2.3.3 Choice of index

The outcome variable chosen in our study is binary, which is not consonant with the standard concentration index that measures relative inequality and does not allow for the differences between the individuals to be compared. When the standard concentration index is applied to the binary variable, characterized by ordinal and bounded nature, erroneous estimates are produced due to the following reasons: (a) An increase in the binary measure is mirrored by the decrease in the measure; (b) An equi-proportionate increase in the binary measure does not translate to the equi-proportionate decrease in the measure; and (c) Bounds act as constraints to (proportionally) equal transformations of the binary measure. The standard concentration index violates the mirror condition and cardinal invariance property. Additionally, a scale-invariant and rank-dependent index, such as the standard concentration index, fails to account for mirror conditions while accounting for the relative differences simultaneously ( 39 , 40 ). These conditions, however, can be satisfied by the generalized version of the modified concentration index proposed by Wagstaff ( 41 ) or Erreygers corrected concentration index ( 39 ). The generalized concentration index departs from the Erreygers index based on value judgments related to the desirability of level independence ( 42 ). This study employed the Erreygers corrected concentration index to compute the wealth-related inequalities in incurring the CHE by the households. Erreygers corrected concentration index is an absolute rather than a relative measure and is only a rank-dependent measure, which is suitable for our binary outcome measure as it satisfies all the desirable properties for rank-dependent indices, i.e., mirror, transfer, cardinal invariance, and level independence. Furthermore, Erreygers has developed the notions of ‘quasi-absoluteness’ and ‘quasi-relativity’ best suited for the bounded variables as they mitigate the infeasibility of equi-proportional change or equal additions in binary constructs. The index is represented as:

Where C I denotes the standard concentration index as represented in Equation 2 , μ is the mean of CHE in the population, and a n , b n are the upper and lower bounds of the outcome variables.

2.3.4 Need standardization

The differential role of need-based factors such as health conditions and demographics in driving health inequality is not considered in the unstandardized distribution of the outcome measures. However, the differential role of such factors can be observed by segregating the inequality into legitimate and illegitimate health inequality. As a result, the need-standardization was conducted to adjust for the legitimate factors impacting health inequality and to facilitate the comparison across groups. The need-standardization can be done via direct-standardization and indirect-standardization methods. The indirect standardization, reflecting the actual distribution of healthcare outcomes and the distribution that would be expected given the distribution of need, was adopted in this study. The indirect standardization exhibits greater accuracy when dealing with unit-level data. However, the evidence on standardization of equity procedures suggests that inequity measures do not digress significantly with the use of linear methods vis-a-vis non-linear methods ( 43 , 44 ). Thus, a linear regression model for standardization was employed first, which is depicted as follows:

Where, y i is the CHE for the household i ; x j i and Z k i are the vectors of need and non-need factors driving the inequality; α , β j , and θ k are the parameters, while the ε i is the error term. Additionally, the predicted values of the outcome measure ( y ^ i x ) was obtained using the OLS parameter estimates ( a ^ , β ^ j , and θ ^ k ), individual values of the need-variables ( x j i ), and sampled means of the controlled non-need variables ( z ¯ j i ). In the next step, the estimates for indirect standardization of outcome measure ( y ^ i IS ) was obtained by subtracting the predicted values from actual values and adding the overall sample mean ( y ¯ ). The subsequent procedure is depicted as follows:

2.3.5 Decomposition of concentration index

The Erreygers concentration index was decomposed to estimate the relative contribution of covariates to explain the inequality in the outcome measure and other unexplained residual variations. A linear approximation of the model, which is based on the partial effects of each covariate evaluated at the sample means, was employed to perform the decomposition. The linear decomposition of inequalities in outcome measure is illustrated as:

Where, x ¯ j and z ¯ j denotes the means of need and non-need factors, respectively, whereas, C I j and C I k are representative of the respective concentration indices. G C I ε is the generalized concentration index for ε i (residual term), which corresponds to the inequality in the outcome measure that cannot be explained by the systematic variation in other variables. The representation is depicted below:

The modified form of decomposition of Erreyger’s index is thus, given as ( 44 ):

The horizontal inequity (HI) in the CHE was thus estimated by subtracting the absolute contributions made by the need-based factors from the unadjusted value of the Erreygers index. A positive value of HI indicates the inequities concentrated among the better-off, whereas a negative value indicates the inequities concentrated among the worse-off.

2.3.6 Determinants of catastrophic health expenditure

The determinants of CHE were gleaned using a gamut of variables that were embedded within Andersen’s behavioral health model ( 45 ). As per the Andersen framework, the choice variables were prorated into (a) Predisposing components reflecting the demographic and socio-structural characteristics of the household; (b) Enabling components subsuming standard of living and insurance coverage for the households; (c) Need components underscoring the severity of disease, frequency, and duration of hospitalization episodes; and (d) Contextual components comprising the regional aspects such as spatial location and burden of the NCD’s in the region.

A multivariate logistic regression model was employed to unravel the determinants of CHE, represented as:

where, the S i , which is the share of out-of-pocket health expenditure ( O O P E i ) out of the total health expenditure ( T H E i ), is dichotomous, i.e., S i assumes the value of 1 if the out-of-pocket health expenditure ( O O P E i ) exceeds the 10% threshold of the total health expenditure T H E i and 0 otherwise. The notation X 1 , X 2 ….. X n represents the socio-economic-demographic-contextual variables driving the CHE. The analysis was conducted using the STATA 15.0 statistical package. The estimates were weighted to account for the complex multistage sample design and confidence intervals for the horizontal inequity index were computed using Bootstrap with 1,000 replications.

The unstandardized and need-standardized distribution of CHE on Hospitalization care in India is illustrated in Figure 1 . Overall, 27% of the ailing treated as inpatients (except for childbirth) incurred CHE during 2017–2018 in India. The incidence of CHE, however, exhibited an inverse relationship with the relative ranking of the expenditure quintile groups. An extensive gradient in the levels of CHE was found between the lowest and highest quintile groups. The incidence of CHE for the population hospitalized in the poorest quintile (41%) was more than twice as compared to the richest quintile (19%). Furthermore, the estimates of the need-standardized CHE were found to be higher than the unstandardized CHE estimates for poor- and middle-income groups (need-standardized CHE greater than unstandardized by 4, 2, and 1% points for poorest, poor, and middle quintile groups); whereas, standardized CHE levels were less than the unstandardized estimates for the wealthier groups (need-standardized CHE lesser than unstandardized estimates by 1 and 7% for rich and richest quintile groups, respectively).

Figure 1 . Distribution of actual and need-standardized levels of CHE on inpatient care in India.

3.1 Inequality and inequities in the catastrophic health expenditure on hospitalization care

The concentration curve eliciting the inequalities and inequities in the CHE on hospitalization care is plotted in Figure 2 . The concentration curve (unstandardized) was found to be above (dominates) the line of equality, indicating that the burden of CHE on inpatient care was concentrated among the poor. Furthermore, the standardized curve (adjusted for differential needs) dominated the unstandardized curve, which denoted that for equal need, the concentration of inequality among the poor was more pronounced vis-a-vis the inequality in CHE, which is not adjusted by the need-based confounding factors. The dominance testing to test the difference between estimated concentration curve ordinates and diagonal via the Multiple Comparison Approach and Intersection Union Principle rejected the null of no wealth-related inequality and established that concentration curves significantly dominated the line of equality. Correspondingly, the estimated value of the Erreyger’s corrected concentration index ( Table 1 ) was negative and significant (EI: -0.191; p  < 0.05), underscoring the disproportionate incidence of CHE among the poor in India. Moreover, the estimates of the need-adjusted concentration index (EI: -0.258; p  < 0.01) corroborated the wider inequities when accounting for the differential needs.

Figure 2 . Concentration curves depicting the inequalities in CHE on inpatient care in India.

Table 1 . Concentration indices depicting the inequality in CHE for hospitalization care.

3.2 Inter-state differentials in the inequities in CHE on hospitalization

The extent of the need-adjusted wealth inequities in incurring the CHE on inpatient care is exhibited in Figure 3 . The measure of inequity was perceptibly concentrated among the poor in most of the Indian states. However, substantial heterogeneities were found in the degree of the inequities among the states. Wealth-related inequities (concentrated among the poor) were found to be high in the states such as Goa (EI: −0.18) and Jharkhand (EI: −0.13). A few states, such as Uttar Pradesh and Maharashtra, with just approximately one-fourth of the total health spending financed by the government, also exhibited significantly high inequities concentrated among the poor. Conversely, no inequities (EI: 0.00) were estimated for the states of Bihar, Chhattisgarh, and Kerala. Furthermore, the states of Assam and Jammu and Kashmir with the highest level of government spending as a proportion of total health spending (55.2 and 51.3% for Assam and Jammu and Kashmir, respectively) evinced relatively less wealth-related inequities. However, the need-adjusted inequalities were concentrated among the rich in the North-Eastern states of Sikkim (EI: 0.07) and Manipur (0.03) in India.

Figure 3 . Need-adjusted inequality indices for CHE on hospitalization in Indian states.

3.3 Descriptive statistics of the variables

The descriptive statistics of the households with hospitalization episodes in the survey period are presented in Table 2 . Most households were headed by adults aged 25–75 years (95.6%) and were men (88.6%). The demographic structure consisted of small (47.5%) and middle (50.2%)-sized households, and more than half of the households (53.5%) lived with children and older adult dependents. Furthermore, one-fourth of the households had a vulnerable widowed population. Approximately 24% of households were headed by household heads who were not literate, and a majority of the households were not employed in activities with regular sources of income. Most of the targeted surveyed households prescribed the religion of Hinduism (75.8%), followed by Islam (13.6%). Socially, a vast proportion of households belonged to the marginal communities, viz. scheduled caste/scheduled tribes (27.9%) and other backward castes (40.2%). Additionally, the housing conditions for most of the households were good (82.3%). However, the access to healthcare services for the household members was considerably low as more than three-fourths of the households were bereft of insurance coverage. Government-sponsored insurance coverage (14%) constituted the highest financial risk protection cover, followed by employer-sponsored coverage (4.4%). Health-seeking behavior divulged that a colossal 50.8% of households sought care from only private facilities, whereas less than half of the households (43.1%) sought care from only public facilities (43.1%). The need for healthcare was more for certain households, as approximately one-fourth of households had at least two or more members suffering from chronic ailments and had more than one hospitalization episode. The majority of the households (63.2%) accounted for a total duration of ≤7 days stay in the hospital, while 32% of households reported a hospital stay of between 7 and 14 days. Spatially, approximately 50.9% of households were residing in the states/UT’s with a higher-middle and high epidemiological transition level. Furthermore, 55.7% of households were in rural areas, while 44.3% of sampled households were residing in urban areas.

Table 2 . Descriptive statistics of the variables.

3.4 Factors impacting the CHE on hospitalization care among households

The wider socio-economic-contextual predictors of the CHE on hospitalization care among households in India are presented in Table 3 . The estimates revealed that among the predisposing demographic factors, the age mix in the household significantly impacted the CHE. Households that were composed of only older adult members and older adult, but no children, were 9% (significant at 1% level) and 4.7% (significant at 1% level), respectively, more likely to incur the CHE vis-a-vis households with a mixed composition of both children and older adult. The structural factor of household size strongly influenced the outcome, as smaller households with less than 5 members and 5–10 members had 16.3 and 10.7%, respectively, more probability than larger households to get impacted by the CHE on inpatient care. Additionally, those households that are principally unemployed/engaged in unpaid work were less likely to be subjected to the CHE vis-a-vis households that were self-employed or receiving pensions post-retirement. Among the social characteristics, households that are ascribed to the other backward castes were more likely to suffer the catastrophic impacts of health payments compared to the households that are classified as scheduled caste/scheduled tribes. Furthermore, practicing Hinduism or other religions, such as Sikhism and Judaism, was positively associated with the CHE incidence as Hindus and other religious groups were 4 and 7.3% more likely vis-a-vis households practicing Islam to face the CHE. The results also underscored the significance of enabling factors in driving the CHE. The evidence indicated an inverse relationship of the CHE with the wealth of households, as richer households were significantly less likely to incur the CHE than their poorer counterparts. The poor, middle, rich, and richest had 11.2, 18.7, 24.1, and 30.5%, respectively, less probability of facing CHE than the poorest household. Analogously, households with government-sponsored insurance cover (6.6%), employer-sponsored cover (10.9%), and private insurance/other covers (12.9%) were less likely to incur CHE vis-a-vis households that are not covered under any financial risk protection scheme. Conversely, households that sought inpatient treatment from private facilities had significantly more likelihood of spending a catastrophic amount on treatment (24.7% for households who sought treatment in a mix of public and private facilities and 32.7% for households who sought treatment in private facilities alone) than those households which sought treatment in just the public hospitals. With respect to the need-based factors, longer duration of hospital stay was associated with more CHE; the probability of incurring CHE was lesser for shorter admission time of fewer than 2 weeks (18.9%), 4–7 days (35.9%), and 3 or fewer days (50.7%) in comparison with the households with longer inpatient days. Finally, the contextual factor of geographical (spatial) location impacted the CHE, as households residing in the regions at higher levels of epidemiological transition level were less likely (7, 4.8, and 6.8% lesser probability for lower-middle, higher-middle, and high epidemiological transition level) to face the CHE on hospital stay as compared to the households residing in the regions having low epidemiolocal level.

Table 3 . Determinants of the CHE on hospitalization care among households in India.

3.5 Decomposition of the inequalities in the CHE on hospitalization care in India

The results ascertaining the contribution of various determinants in driving the wealth-related inequality in CHE on hospitalization care in India is encapsulated in Table 4 , which presents the estimates of coefficients, Erreyger’s concentration indices, absolute contributions (computing the product of elasticity and regressor’s concentration index), and relative contributions (denoting the percentage of inequality in CHE attributable to the inequality in the contributing factor). A positive (negative) value of the absolute contribution of a correlate demonstrates that if the inequality in the CHE was determined by that correlate alone, then it would be concentrated toward the worse-off (better off). The relative contribution of a correlate is computed by dividing the absolute contribution of correlates by total inequality in the outcome variable and multiplying it by 100. The aggregate relative contributions of covariates in driving the inequality are also illustrated in Figure 4 . Overall, the relative contribution of need-based variables was exhibited to be negative, connoting that if the CHE were determined by need alone, it would be more concentrated among the poor. Aggregately, the need factors accounted for 35.3% of the unstandardized concentration index, and most of this contribution was attributed to the duration of stay (30.6% of the unstandardized concentration index) in the hospital. However, the inequality push toward the poor was offset to a degree by the effect of the non-need/illegitimate factors. The majority of the inequality in the CHE was driven by illegitimate/non-need factors, with most of the contributions from the enabling factors such as inequality in the wealth of households (expenditure quintiles) and health utilization pattern (facility mix for hospitalization) in conjunction with socio-structural variables such as the size of the household. Additionally, the decomposition results enable the estimation of horizontal inequity, which is obtained by subtracting the absolute need contributions (0.068) from the unstandardized index (−0.19), thus yielding an index value of −0.26.

Table 4 . Regression coefficients (B), absolute contribution and relative contribution of determinants to income-related inequality in catastrophic health expenditure on hospitalization in India.

Figure 4 . Decomposition analysis of income-related inequalities in CHE on hospitalization.

4 Discussion and conclusion

Our study revealed significant wealth-related inequalities in the CHE for hospitalization care in India, with a pervasive gap between the poorest and richest income quintiles. The CHE was concentrated more among the poor, with the incidence of CHE being more than twice for the poorest quintile vis-a-vis the richest quintile group. The findings were corroborated by the negative value of the Erreygers concentration index, denoting the inequalities that are disadvantageous to the poor. Furthermore, need-adjusted inequalities also underscored the systemic inequalities (caused by the factors amenable to the policy change) to be concentrated among the poor. Globally, the evidence on the relationship between CHE and socio-economic status has been mixed, and few findings suggest that the better-off experience more CHE in low- and middle-income settings (LMIC) due to the higher propensity of the rich to consume more health services ( 46 ). However, our findings were consonant with the studies conducted in other LMIC settings such as Iran ( 47 ), China ( 48 ), Malawi ( 49 ), Columbia ( 50 ), and Sub-Saharan Africa ( 46 ), where inequality gradients indicated the poor getting afflicted by the CHE disproportionately. The higher incidence of CHE among the poor can be understood by the fact that for households with low income, even a small proportion of healthcare costs can be catastrophic.

The relatively higher incidence of CHE among the poor is pertinent from a policy perspective as it also connotes the intrinsic disparities in healthcare access and finance. India has launched various programs targeted toward the poor to move along the trajectory of Universal Health Coverage (UHC). To achieve the goal of equitable financial risk protection for the marginalized, India launched flagship initiatives such as the National Rural Health Mission (NRHM) in 2005, providing free cost care to the poor and Rashtriya Swasthya Bima Yojana (RSBY) in 2008, covering the poor population with cashless insurance on hospitalization. However, the relatively higher incidence of CHE among the poor alludes to the inefficacy of these programs in providing financial risk protection to the poor. Furthermore, the empirical evidence on the impact of schemes such as RSBY has concurred with its ineffectiveness in reducing the inpatient out-of-pocket spending and catastrophic inpatient spending ( 51 , 52 ). However, India recently revamped and bolstered these schemes further for expanded coverage by launching the Ayushman Bharat (AB) Program (National Health Protection Mission) for integrated healthcare. The scheme has two components: (a) AB-Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), which provides cashless cover up to INR 5 lakh per family for hospitalization in secondary and tertiary care to over 10 crore poor and vulnerable families; and (b) AB-Health and Wellness Centers (AB-HWCs) providing comprehensive primary and community-based services free of cost to the population. Furthermore, India has launched other initiatives such as free drugs and diagnostics services and financial assistance to patients living below the poverty line for life-threatening diseases under schemes such as Rashtriya Arogya Nidhi (RAN), Health Minister’s Cancer Patient Fund (HMCPF), and Health Minister’s Discretionary Grant (HMDG). Furthermore, affordable medicines and reliable implants for treatment (AMRIT) deendayal outlets have been opened to make available drugs and implants for cardiovascular diseases (CVDs), cancer, and diabetes at discounted prices to patients ( 53 ). Although a legion of health initiatives providing free healthcare to different marginalized sections of society have been launched recently, the impact evaluation of these interventions in reducing the burden of OOP on hospitalization among the poor in India needs to be undertaken.

Our findings indicated that members of more than half of the poor households were hospitalized in private facilities with a disproportionately higher incidence of CHE (38.5% in private facilities vis-a-vis 11.5% in public facilities). A myriad of reasons for the preference for private provider(s) in India has been expounded in literature, such as poor readiness and quality of care, higher waiting times, inconvenient facility timings, long distances, absence of healthcare personnel, and lack of acceptability and trust in public providers ( 54 – 57 ). Hence, it is recommended to strengthen the public healthcare system to encompass NCD care (with a disproportionately higher incidence of CHE) ( 58 ) and improve the quality of care in terms of infrastructure, equipment, drugs, and diagnostics. A legion of guidelines and standards to ensure the quality of care has been enforced in India, such as Indian Public Health Standards (IPHS), Mera Aspataal (My hospital), and National Quality Assurance Standards (NQAS). However, the non-compliance of quality protocols and standards has hampered the readiness of public health facilities. Thus, the objective periodic monitoring and evaluation of the quality parameters along the continuum of care is suggested to ensure readiness. Concomitantly, surveillance measures such as record keeping, frequent monitoring of employee absence behavior, detection of absence via biometric attendance, and management-oriented punitive action measures for dereliction of duties can be introduced to minimize absenteeism. Simultaneously, to mitigate the low acceptability and poor confidence in public provider, knowledge dissemination, advocacy, and public engagement activities should be promoted at an individual, household, and community and regional level as a confidence-building measure.

Our findings found a legion of factors influencing CHE on hospitalization care. The role of demographic factors was accentuated in the study, and it was found that households comprising only older adult members incur significantly high CHE on hospitalization, which is in tandem with other studies conducted in India ( 59 ). Analogously, our estimates revealed that larger size households experience more CHE, which is conflated by other research conducted in LMICs ( 60 , 61 ). Additionally, other predisposing socio-structural factors, such as affiliation with the marginalized social group and practicing the religion of Hinduism, are associated with higher CHE, which is consonant with the other studies conducted in India ( 62 – 64 ). Although equity has been a primary goal of the flagship programs launched by the Government of India, the related policy discourse has been focused on the praxis of wealth-related inequalities and has precluded other social disparities, such as religion and caste, as a potential axis of healthcare marginalization ( 65 ). The multivariate regression estimates also underscored the role of enabling factors such as the absence of insurance coverage and treatment-seeking in private facilities to increase the CHE significantly. The role of these enabling factors, such as the type of health facility and insurance coverage, in influencing the CHE has also been accentuated in many other studies from similar settings ( 66 , 67 ).

In the LMIC context, the policy discourse has given impetus to the establishment/extension of national/social health insurance in which service providers are paid from designated government funds, which are partly funded through taxes. India via AB-PMJAY provides such insurance coverage for hospitalization to the poor and vulnerable; however, evidence from rural India suggests that around one-fourth of the eligible participants are still unaware of the AB-PMJAY scheme; moreover, the level of utilization of the scheme has been found to be abysmally low at 1.3% ( 68 ). The low level of utilization can be explained via complex enrollment or reimbursement process, which acts as a significant barrier to take up. The findings on PMJAY in India also suggest that this scheme shifted the use of health facilities from public providers to privately empaneled hospitals where the cost of care is higher ( 69 ). Thus, a gamut of strategies can be employed to increase the penetration and uptake of Public Funded Health Insurance (PHFI) schemes in India, such as an increase in the awareness of benefits and community engagement via appropriate training for competencies of the community health workers, such as Accredited social health activists (ASHA) and Anganwadi workers (AWW); easing the process of enrollment and reimbursement and streamlining other hospital-based processes for effective implementation of the scheme ( 70 ) and establishing a robust referral linkage between the primary healthcare facilities with secondary and tertiary hospitals with the help of digital interventions and infrastructure. However, in regions where the institutional capacity to organize mandatory nationwide risk-pooling is weak, community-based health insurance schemes can be effective in protecting poor households from unpredictably high medical expenses ( 31 ).

The findings also demonstrated the role of contextual factors such as the region in influencing the CHE as the households belonging to the states with higher levels of the epidemiological transition level (defined based on the ratio of disability-adjusted life years and computed as the sum of years of potential life lost due to the premature mortality and the years of productive life lost due to disability from communicable disease to those from non-communicable and injuries combined) incurred lesser CHE as compared to their counterparts residing in the states at a lower level of ETL. These inter-region heterogeneities can be explained by the inverse relationship between the epidemiological transition ratio and socio-economic development of the states ( 71 ). A higher burden of CHE on the states with a lower level of epidemiological transition is a pertinent finding from the policy perspective as these states are associated with the lower per capita expenditure on healthcare, thus lacking financial risk protection vis-a-vis other states. Thus, there is a need to increase public spending on healthcare to reach the targeted level of 4% of GDP by 2025. However, realistically, the state governments can set a target to allocate at least 2.5% of the state’s gross domestic product (SGDP) to healthcare, which is the recommended level by the World Health Organization (WHO). It is further suggested that the government explore new and innovative financing mechanisms to generate the fiscal space, such as the public–private partnership to fund the sector; simultaneously, other fiscal space measures, such as the collection of health-specific tax, goods, and services tax reform, higher excise duty on tobacco products, tax administration reform and direct beneficiary transfer of health services could be employed as the alternative revenue mobilization channels for fiscal space in health ( 72 ).

The decomposition analysis revealed that the contribution of non-need/illegitimate factors in driving the inequality was relatively high vis-à-vis need/legitimate factors, as most of the inequality in CHE was driven by the non-need factors amenable to the policy change. Most of the unfair inequalities arose from socio-structural factors such as the size of the household and enabling factors such as income (expenditure) and type of facility (public or private) utilized. The relative contribution of these determinants in influencing inequalities in CHE is found in other LMICs. A study on the decomposition of inequalities in CHE in Iran ( 47 ) demonstrated that most of the illegitimate inequalities emanated from household economic status (64%), followed by household size (40%). Other studies in China have also accounted for household size as the largest contributor to CHE inequality ( 73 , 74 ). Furthermore, evidence from Sierra Leonne suggested that the distributional effect of the type of facility significantly impacted the inequalities in the CHE ( 75 ). Thus, from the policy perspective, it is imperative to invest more in public health facilities, providing significant financial risk protection to the poor. From the Indian perspective, the burden of CHE was found to be disproportionately higher for the poor and middle-population groups as well. Thus, it is suggested that the state and central governments expand the PFHI coverage to the missing middle population as well.

The study has a few caveats due to the nature of the dataset and the methodological approach. First , the same weights are assigned to the catastrophic payments incurred by poor and non-poor households and, thus, ignore the differentials in the opportunity cost in the health spending between rich and poor, thereby rendering the measure non-normative, which does not allow for distributional sensitivity. Second , health expenditures are not adjusted for coping mechanisms such as distressed financing or adjustment in the consumption pattern to pay for the health expenditure, thus understating CHE. Third , the data on expenditure used in the survey is self-reported and is susceptible to recall and information bias. Fourth, in the multivariate regression, the information on outcome measures and covariates was collected concurrently due to the cross-sectional design; thus, associations rather than causal relationships are defined in the study. Fifth, the information on self-reported monthly household consumer expenditure is a one-shot open-ended with no parallel validation, and thus can lead to the underestimation of the household’s income.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

SS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. VV: Data curation, Formal analysis, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. PG: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – review & editing. MA: Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The research was funded by the internal funding support made available to the first and corresponding author Dr. Shyamkumar Sriram from Ohio University College of Health Sciences and Professions, Ohio University, USA.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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Keywords: out-of-pocket healthcare expenditures, hospitalization care, catastrophic health expenditures, inequality, need-adjusted inequities, decomposition of inequality

Citation: Sriram S, Verma VR, Gollapalli PK and Albadrani M (2024) Decomposing the inequalities in the catastrophic health expenditures on the hospitalization in India: empirical evidence from national sample survey data. Front. Public Health . 12:1329447. doi: 10.3389/fpubh.2024.1329447

Received: 29 October 2023; Accepted: 18 March 2024; Published: 04 April 2024.

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Copyright © 2024 Sriram, Verma, Gollapalli and Albadrani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Shyamkumar Sriram, [email protected]

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