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In This Article Expand or collapse the "in this article" section Training and Development

Introduction, general overviews.

  • Reference Works
  • Instructional Systems Design
  • Needs Assessment
  • Training Methods
  • Pre-training Interventions
  • Training Media
  • Training Teams
  • Training Evaluation
  • Learner Characteristics
  • Learning Context
  • Employee Development
  • Macroperspectives

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Training and Development by Kenneth G. Brown LAST REVIEWED: 13 July 2020 LAST MODIFIED: 26 October 2015 DOI: 10.1093/obo/9780199846740-0013

Training and development is the study of how structured experiences help employees gain work-related knowledge, skill, and attitudes. It is like many other topics in management in that it is inherently multidisciplinary in nature. At its core is the psychological study of learning and transfer. A variety of disciplines offer insights into this topic, including, but not limited to, industrial and organizational psychology, educational psychology, human resource development, organizational development, industrial and labor relations, strategic management, and labor economics. The focus of this bibliography is primarily psychological with an emphasis on theory and practice that examines training processes and the learning outcomes they seek to influence. Nevertheless, literature from other perspectives will be introduced on a variety of topics within this area of study.

These articles and chapters provide background for the study of training and development, particularly as studied by management scholars with backgrounds in human resource management, organizational behavior, human resource development, and industrial and organizational psychology. Kraiger 2003 examines training from three different perspectives. Aguinis and Kraiger 2009 provides a narrative review of ten years of research on training and employee development, focusing on the many benefits of providing structured learning experiences to employees. Brown and Sitzmann 2011 also reviews the literature and emphasizes research on the processes that are required to ensure that training benefits emerge. Arthur, et al. 2003 meta-analyzes the literature on training effectiveness. Russ-Eft 2002 proposes a typology of training designs. Salas, et al. 2012 offers recommendations for evidence-based training practice. Noe, et al 2014 examines training in a broader context, relative to the roles of informal learning and knowledge transfer.

Aguinis, Herman, and Kurt Kraiger. “Benefits of Training and Development for Individuals and Teams, Organizations, and Society.” Annual Review of Psychology 60.1 (January 2009): 451–474.

DOI: 10.1146/annurev.psych.60.110707.163505

A comprehensive review of training and development literature from 1999 to 2009 with an emphasis on the benefits that training offers across multiple levels of analysis.

Arthur, Winfred A., Jr., Winston Bennett Jr., Pamela S. Edens, and Suzanne T. Bell. “Effectiveness of Training in Organizations: A Meta-analysis of Design and Evaluation Features.” Journal of Applied Psychology 88.2 (April 2003): 234–245.

DOI: 10.1037/0021-9010.88.2.234

Offers a comprehensive meta-analysis of the relationships among training design and evaluation features and various training effectiveness outcomes (reaction, learning, behavior, and results).

Brown, Kenneth G., and Traci Sitzmann. “Training and Employee Development for Improved Performance.” In APA Handbook of Industrial and Organizational Psychology . Vol. 2, Selecting and Developing Members for the Organization . Edited by Sheldon Zedeck, 469–503. Washington, DC: American Psychological Association, 2011.

DOI: 10.1037/12170-000

A comprehensive review of training and development in work organizations with an emphasis on the processes necessary for training to be effective for improving individual and team performance.

Kraiger, Kurt. “Perspectives on Training and Development.” In Handbook of Psychology . Vol. 12. Edited by Irving B. Weiner and Walter C. Borman, Daniel R. Ilgen, and Richard J. KIlimoski, 171–192. Hoboken, NJ: John Wiley, 2003.

DOI: 10.1002/0471264385

Reviews training literature from three perspectives: instruction, learning, and organizational change.

Noe, Raymond A., Alena D. M. Clarke, and Howard J. Klein. “Learning in the Twenty-first-century Workplace.” Annual Review of Organizational Psychology and Organizational Behavior 1 (2014): 245–275.

DOI: 10.1146/annurev-orgpsych-031413-091321

A review that places training and development in a broader context with other learning-related interventions and practices such as informal learning and knowledge sharing. The chapter explains factors that facilitate learning in organizations.

Russ-Eft, Darlene. “A Typology of Training Design and Work Environment Factors Affecting Workplace Learning and Transfer.” Human Resource Development Review 1 (March 2002): 45–65.

DOI: 10.1177/1534484302011003

Presents a typology summarizing elements of training and work environments that foster transfer of training.

Salas, Eduardo, Scott I. Tannenbaum, Kurt Kraiger, and Kimberly A. Smith-Jentsch. “The Science of Training and Development in Organizations: What Matters in Practice.” Psychological Science in the Public Interest 13.2 (2012): 74–101.

DOI: 10.1177/1529100612436661

Reviews meta-analytic evidence and offers evidence-based recommendations for maximizing training effectiveness.

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A literature review on training and development and quality of work life

The authors suggest that training and development is a process leading to qualitative as well as quantitative advancements in an organization, especially at the managerial level. It is stated that training has specific areas and objectives whilst development is a continuous process less concerned with physical skills than with knowledge, values, attitudes and behavior. The authors discuss the process in which an organization recognizes their responsibility for optimal organizational performance and development of organizational motives for optimum quality of employee working life. Th...  Show more

The authors suggest that training and development is a process leading to qualitative as well as quantitative advancements in an organization, especially at the managerial level. It is stated that training has specific areas and objectives whilst development is a continuous process less concerned with physical skills than with knowledge, values, attitudes and behavior. The authors discuss the process in which an organization recognizes their responsibility for optimal organizational performance and development of organizational motives for optimum quality of employee working life.

This paper focuses and analyses the literature findings on importance of training and development and its relation with the employees' quality of work life.

Edited excerpts from published abstract. Show less

Authors: Kulkarni, Pallavi P.

Published: Nashik, India, Researchers World, 2013

Resource type: Article

Journal title: Journal of arts science and commerce

Journal volume: 4

Journal number: 2

Journal date: 2013

Pages: pp. 136-143

ISSN: 2231-4172; 2229-4686 (online)

Peer reviewed: Yes

Document number: TD/TNC 116.440

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what is literature review of training and development

Subjects: Management Teaching and learning Employment Workforce development Research

Keywords: Human resources Training Quality of working life Human resource development Literature review

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A LITERATURE REVIEW ON TRAINING & DEVELOPMENT AND QUALITY OF WORK LIFE

Profile image of Rahul Mehra

In this competitive world, training plays an important role in the competent and challenging format of business. Training is the nerve that suffices the need of fluent and smooth functioning of work which helps in enhancing the quality of work life of employees and organizational development too. Development is a process that leads to qualitative as well as quantitative advancements in the organization, especially at the managerial level, it is less considered with physical skills and is more concerned with knowledge, values, attitudes and behaviour in addition to specific skills. Hence, development can be said as a continuous process whereas training has specific areas and objectives. So, every organization needs to study the role, importance and advantages of training and its positive impact on development for the growth of the organization. Quality of work life is a process in which the organization recognizes their responsibility for excellence of organizational performance as well as employee skills. Training implies constructive development in such organizational motives for optimum enhancement of quality of work life of the employees. These types of training and development programs help in improving the employee behaviour and attitude towards the job and also uplift their morale. Thus, employee training and development programs are important aspects which are needed to be studied and focused on. This paper focuses and analyses the literature findings on importance of training and development and its relation with the employees' quality of work life.

Related Papers

IAEME Publication

Quality of Work Life (QWL) of employees in any organization plays a very vital role in shaping of both the employees and the organization. The objective of this research is to highlight the prominence of training and development programmes adopted in manufacturing industries encompassing the private and public sectors and the impact that it exerts on the quality of work life of employees in these sectors. It is assumed that employees who undergo T & D programme either in private or public sectors enjoy better QWL. Here a comparative study among the employees of private and public manufacturing industries is carried out to measure the QWL of employees in these respective sectors. Hence the research concludes that the QWL enjoyed by the employees of private industries is superior to the QWL of employees of public industries.

what is literature review of training and development

Noble Academic Publisher

josiah emmanuel

International Journal of Latest Technology in Engineering, Management & Applied Science -IJLTEMAS (www.ijltemas.in)

In this era where competition is increasing day by day in the corporate world training and development has become one of the important key to achieve success. Training is an important subsystem of Human Resource Development. It is a specialized function and is one of the fundamental operative functions for known resource management. Development is a long-term educational process utilizing a systematic and organized procedure by which managerial personnel get conceptual and theoretical knowledge. Basically, it is an attempt to improve the current or future employee performance of the employee by increasing his or her ability to perform through learning, usually by changing the employee’s attitude or increasing his or her skills and knowledge. These types of training and development programs help in improving the employee behavior and attitude towards the job and also uplift their morale. Thus, employee training and development programs are important aspects which are needed to be studied and focused on. This paper focusses on the advantages of the training and development for the employee’s.

International Journal of Scientific Research in Science and Technology IJSRST

The purpose of this paper is to present a conceptual study established on the employee training and development program and its benefits. This paper will inspect the structure and elements of employee training and development program and later the study present what are the positive outcomes for employees and organizations. Training and development play an important role in the effectiveness of organizations and to the experiences of people in work. Training has implications for productivity, health and safety at work and personal development. Modern organizations therefore use their resources (money, time, energy, information, etc.) for permanent training and advancement of their employees. Training and development is an instrument that aid human capital in exploring their dexterity. Therefore training and development is vital to the productivity of organization " s workforce. The study described here is a vigilant assessment of literature on fundamental of employee development program and its benefits to organizations and employees.

Dr Yashpal D Netragaonkar

“ To Study the Effectiveness of Employees Training & Development Program ”. The prime objective of research is to study the changes in skill , attitude, knowledge, behavior of Employees after Training program. It also studies the effectiveness of Training on both Individual and Organizational levels. Due to this research we are able to absorb current trends related to whole academic knowledge a nd its practical use. Such research is exposed us to set familiar with professional environment, working culture, behavior, oral communication & manners. Since the training is a result oriented process and a lot of time and expenditure, it is necessary tha t the training program should be designed with a great care. For evaluating effectiveness if training a questionnaire has to be carefully prepared for participants in order to receive feedback.

Venkata Sandeep

Tolulope J Ogunleye

Overtime, study had shown that to be relevant in any field of work there is need for continuous learning through training and development. The study is aimed at finding out the need for employees training and development in an organization. The need for improvement to change the phenomenon of low productivity and poor service delivery attributed to the employee’s in-adequate experience, calls for investigation on how effective training and development of employee can facilitate improved corporate performance using the banking industry as a field of discuss.. The study concluded that training and development brings about career growth for the employees and bankers thus the study recommended that all organization must do induction training at entry point into the banking sector.

International Journal of Research Publication (IJRP)

IAEME PUBLICATION

Training and development enables to develop skills and competencies necessary to enhance bottom-line results for their organization. It is a key ingredient for organizational performance improvement. It ensures that randomness is reduced and learning or behavioural change takes place in structured format. Training and Development helps in increasing the job knowledge and skills of employees at each level and helps to expand the horizons of human intellect and an overall personality of the employees. This paper analyses the link between various Training and Development programs organized in Larsen &Toubro Group of Companies and their impacts on employee satisfaction and performance. Data for the paper have been collected through primary source that are from questionnaire, surveys. There were two variables: Training and Development (independent) and Employees satisfaction and performance (dependent). The goal was to see whether Training and development has an impact on employee’s satisfaction and performance

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Systematic Literature Review of E-Learning Capabilities to Enhance Organizational Learning

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  • Published: 01 February 2021
  • Volume 24 , pages 619–635, ( 2022 )

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  • Michail N. Giannakos 1 ,
  • Patrick Mikalef 1 &
  • Ilias O. Pappas   ORCID: orcid.org/0000-0001-7528-3488 1 , 2  

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E-learning systems are receiving ever increasing attention in academia, business and public administration. Major crises, like the pandemic, highlight the tremendous importance of the appropriate development of e-learning systems and its adoption and processes in organizations. Managers and employees who need efficient forms of training and learning flow within organizations do not have to gather in one place at the same time or to travel far away to attend courses. Contemporary affordances of e-learning systems allow users to perform different jobs or tasks for training courses according to their own scheduling, as well as to collaborate and share knowledge and experiences that result in rich learning flows within organizations. The purpose of this article is to provide a systematic review of empirical studies at the intersection of e-learning and organizational learning in order to summarize the current findings and guide future research. Forty-seven peer-reviewed articles were collected from a systematic literature search and analyzed based on a categorization of their main elements. This survey identifies five major directions of the research on the confluence of e-learning and organizational learning during the last decade. Future research should leverage big data produced from the platforms and investigate how the incorporation of advanced learning technologies (e.g., learning analytics, personalized learning) can help increase organizational value.

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1 Introduction

E-learning covers the integration of information and communication technology (ICT) in environments with the main goal of fostering learning (Rosenberg and Foshay 2002 ). The term “e-learning” is often used as an umbrella term to portray several modes of digital learning environments (e.g., online, virtual learning environments, social learning technologies). Digitalization seems to challenge numerous business models in organizations and raises important questions about the meaning and practice of learning and development (Dignen and Burmeister 2020 ). Among other things, the digitalization of resources and processes enables flexible ways to foster learning across an organization’s different sections and personnel.

Learning has long been associated with formal or informal education and training. However organizational learning is much more than that. It can be defined as “a learning process within organizations that involves the interaction of individual and collective (group, organizational, and inter-organizational) levels of analysis and leads to achieving organizations’ goals” (Popova-Nowak and Cseh 2015 ) with a focus on the flow of knowledge across the different organizational levels (Oh 2019 ). Flow of knowledge or learning flow is the way in which new knowledge flows from the individual to the organizational level (i.e., feed forward) and vice versa (i.e., feedback) (Crossan et al. 1999 ; March 1991 ). Learning flow and the respective processes constitute the cornerstone of an organization’s learning activities (e.g., from physical training meetings to digital learning resources), they are directly connected to the psycho-social experiences of an organization’s members, and they eventually lead to organizational change (Crossan et al. 2011 ). The overall organizational learning is extremely important in an organization because it is associated with the process of creating value from an organizations’ intangible assets. Moreover, it combines notions from several different domains, such as organizational behavior, human resource management, artificial intelligence, and information technology (El Kadiri et al. 2016 ).

A growing body of literature lies at the intersection of e-learning and organizational learning. However, there is limited work on the qualities of e-learning and the potential of its qualities to enhance organizational learning (Popova-Nowak and Cseh 2015 ). Blockages and disruptions in the internal flow of knowledge is a major reason why organizational change initiatives often fail to produce their intended results (Dee and Leisyte 2017 ). In recent years, several models of organizational learning have been published (Berends and Lammers 2010 ; Oh 2019 ). However, detailed empirical studies indicate that learning does not always proceed smoothly in organizations; rather, the learning meets interruptions and breakdowns (Engeström et al. 2007 ).

Discontinuities and disruptions are common phenomena in organizational learning (Berends and Lammers 2010 ), and they stem from various causes. For example, organizational members’ low self-esteem, unsupportive technology and instructors (Garavan et al. 2019 ), and even crises like the Covid-19 pandemic can result in demotivated learners and overall unwanted consequences for their learning (Broadbent 2017 ). In a recent conceptual article, Popova-Nowak and Cseh ( 2015 ) emphasized that there is a limited use of multidisciplinary perspectives to investigate and explain the processes and importance of utilizing the available capabilities and resources and of creating contexts where learning is “attractive to individual agents so that they can be more engaged in exploring ways in which they can contribute through their learning to the ongoing renewal of organizational routines and practices” (Antonacopoulou and Chiva 2007 , p. 289).

Despite the importance of e-learning, the lack of systematic reviews in this area significantly hinders research on the highly promising value of e-learning capabilities for efficiently supporting organizational learning. This gap leaves practitioners and researchers in uncharted territories when faced with the task of implementing e-learning designs or deciding on their digital learning strategies to enhance the learning flow of their organizations. Hence, in order to derive meaningful theoretical and practical implications, as well as to identify important areas for future research, it is critical to understand how the core capabilities pertinent to e-learning possess the capacity to enhance organizational learning.

In this paper, we define e-learning enhanced organizational learning (eOL) as the utilization of digital technologies to enhance the process of improving actions through better knowledge and understanding in an organization. In recent years, a significant body of research has focused on the intersection of e-learning and organizational learning (e.g., Khandakar and Pangil 2019 ; Lin et al. 2019 ; Menolli et al. 2020 ; Turi et al. 2019 ; Xiang et al. 2020 ). However, there is a lack of systematic work that summarizes and conceptualizes the results in order to support organizations that want to move from being information-based enterprises to being knowledge-based ones (El Kadiri et al. 2016 ). In particular, recent technological advances have led to an increase in research that leverages e-learning capacities to support organizational learning, from virtual reality (VR) environments (Costello and McNaughton 2018 ; Muller Queiroz et al. 2018 ) to mobile computing applications (Renner et al. 2020 ) to adaptive learning and learning analytics (Zhang et al. 2019 ). These studies support different skills, consider different industries and organizations, and utilize various capacities while focusing on various learning objectives (Garavan et al. 2019 ). Our literature review aims to tease apart these particularities and to investigate how these elements have been utilized over the past decade in eOL research. Therefore, in this review we aim to answer the following research questions (RQs):

RQ1: What is the status of research at the intersection of e-learning and organizational learning, seen through the lens of areas of implementation (e.g., industries, public sector), technologies used, and methodologies (e.g., types of data and data analysis techniques employed)?

RQ2: How can e-learning be leveraged to enhance the process of improving actions through better knowledge and understanding in an organization?

Our motivation for this work is based on the emerging developments in the area of learning technologies that have created momentum for their adoption by organizations. This paper provides a review of research on e-learning capabilities to enhance organizational learning with the purpose of summarizing the findings and guiding future studies. This study can provide a springboard for other scholars and practitioners, especially in the area of knowledge-based enterprises, to examine e-learning approaches by taking into consideration the prior and ongoing research efforts. Therefore, in this paper we present a systematic literature review (SLR) (Kitchenham and Charters 2007 ) on the confluence of e-learning and organizational learning that uncovers initial findings on the value of e-learning to support organizational learning while also delineating several promising research streams.

The rest of this paper is organized as follows. In the next section, we present the related background work. The third section describes the methodology used for the literature review and how the studies were selected and analyzed. The fourth section presents the research findings derived from the data analysis based on the specific areas of focus. In the fifth section, we discuss the findings, the implications for practice and research, and the limitations of the selected methodological approach. In the final section, we summarize the conclusions from the study and make suggestions for future work.

2 Background and Related Work

2.1 e-learning systems.

E-learning systems provide solutions that deliver knowledge and information, facilitate learning, and increase performance by developing appropriate knowledge flow inside organizations (Menolli et al. 2020 ). Putting into practice and appropriately managing technological solutions, processes, and resources are necessary for the efficient utilization of e-learning in an organization (Alharthi et al. 2019 ). Examples of e-learning systems that have been widely adopted by various organizations are Canvas, Blackboard, and Moodle. Such systems provide innovative services for students, employees, managers, instructors, institutions, and other actors to support and enhance the learning processes and facilitate efficient knowledge flow (Garavan et al. 2019 ). Functionalities, such as creating modules to organize mini course information and learning materials or communication channels such as chat, forums, and video exchange, allow instructors and managers to develop appropriate training and knowledge exchange (Wang et al. 2011 ). Nowadays, the utilization of various e-learning capabilities is a commodity for supporting organizational and workplace learning. Such learning refers to training or knowledge development (also known in the literature as learning and development, HR development, and corporate training: Smith and Sadler-Smith 2006 ; Garavan et al. 2019 ) that takes place in the context of work.

Previous studies have focused on evaluating e-learning systems that utilize various models and frameworks. In particular, the development of maturity models, such as the e-learning capability maturity model (eLCMM), addresses technology-oriented concerns (Hammad et al. 2017 ) by overcoming the limitations of the domain-specific models (e.g., game-based learning: Serrano et al.  2012 ) or more generic lenses such as the e-learning maturity model (Marshall 2006 ). The aforementioned models are very relevant since they focus on assessing the organizational capabilities for sustainably developing, deploying, and maintaining e-learning. In particular, the eLCMM focuses on assessing the maturity of adopting e-learning systems and adds a feedback building block for improving learners’ experiences (Hammad et al. 2017 ). Our proposed literature review builds on the previously discussed models, lenses, and empirical studies, and it provides a review of research on e-learning capabilities with the aim of enhancing organizational learning in order to complement the findings of the established models and guide future studies.

E-learning systems can be categorized into different types, depending on their functionalities and affordances. One very popular e-learning type is the learning management system (LMS), which includes a virtual classroom and collaboration capabilities and allows the instructor to design and orchestrate a course or a module. An LMS can be either proprietary (e.g., Blackboard) or open source (e.g., Moodle). These two types differ in their features, costs, and the services they provide; for example, proprietary systems prioritize assessment tools for instructors, whereas open-source systems focus more on community development and engagement tools (Alharthi et al. 2019 ). In addition to LMS, e-learning systems can be categorized based on who controls the pace of learning; for example, an institutional learning environment (ILE) is provided by the organization and is usually used for instructor-led courses, while a personal learning environment (PLE) is proposed by the organization and is managed personally (i.e., learner-led courses). Many e-learning systems use a hybrid version of ILE and PLE that allows organizations to have either instructor-led or self-paced courses.

Besides the controlled e-learning systems, organizations have been using environments such as social media (Qi and Chau 2016 ), massive open online courses (MOOCs) (Weinhardt and Sitzmann 2018 ) and other web-based environments (Wang et al. 2011 ) to reinforce their organizational learning potential. These systems have been utilized through different types of technology (e.g., desktop applications, mobile) that leverage the various capabilities offered (e.g., social learning, VR, collaborative systems, smart and intelligent support) to reinforce the learning and knowledge flow potential of the organization. Although there is a growing body of research on e-learning systems for organizational learning due to the increasingly significant role of skills and expertise development in organizations, the role and alignment of the capabilities of the various e-learning systems with the expected competency development remains underexplored.

2.2 Organizational Learning

There is a large body of research on the utilization of technologies to improve the process and outcome dimensions of organizational learning (Crossan et al. 1999 ). Most studies have focused on the learning process and on the added value that new technologies can offer by replacing some of the face-to-face processes with virtual processes or by offering new, technology-mediated phases to the process (Menolli et al. 2020 ; Lau 2015 ) highlighted how VR capabilities can enhance organizational learning, describing the new challenges and frameworks needed in order to effectively utilize this potential. In the same vein, Zhang et al. ( 2017 ) described how VR influences reflective thinking and considered its indirect value to overall learning effectiveness. In general, contemporary research has investigated how novel technologies and approaches have been utilized to enhance organizational learning, and it has highlighted both the promises and the limitations of the use of different technologies within organizations.

In many organizations, alignment with the established infrastructure and routines, and adoption by employees are core elements for effective organizational learning (Wang et al. 2011 ). Strict policies, low digital competence, and operational challenges are some of the elements that hinder e-learning adoption by organizations (Garavan et al. 2019 ; Wang 2018 ) demonstrated the importance of organizational, managerial, and job support for utilizing individual and social learning in order to increase the adoption of organizational learning. Other studies have focused on the importance of communication through different social channels to develop understanding of new technology, to overcome the challenges employees face when engaging with new technology, and, thereby, to support organizational learning (Menolli et al. 2020 ). By considering the related work in the area of organizational learning, we identified a gap in aligning an organization’s learning needs with the capabilities offered by the various technologies. Thus, systematic work is needed to review e-learning capabilities and how these capabilities can efficiently support organizational learning.

2.3 E-learning Systems to Enhance Organizational Learning

When considering the interplay between e-learning systems and organizational learning, we observed that a major challenge for today’s organizations is to switch from being information-based enterprises to become knowledge-based enterprises (El Kadiri et al. 2016 ). Unidirectional learning flows, such as formal and informal training, are important but not sufficient to cover the needs that enterprises face (Manuti et al. 2015 ). To maintain enterprises’ competitiveness, enterprise staff have to operate in highly intense information and knowledge-oriented environments. Traditional learning approaches fail to substantiate learning flow on the basis of daily evidence and experience. Thus, novel, ubiquitous, and flexible learning mechanisms are needed, placing humans (e.g., employees, managers, civil servants) at the center of the information and learning flow and bridging traditional learning with experiential, social, and smart learning.

Organizations consider lack of skills and competences as being the major knowledge-related factors hampering innovation (El Kadiri et al. 2016 ). Thus, solutions need to be implemented that support informal, day-to-day, and work training (e.g., social learning, collaborative learning, VR/AR solutions) in order to develop individual staff competences and to upgrade the competence affordances at the organizational level. E-learning-enhanced organizational learning has been delivered primarily in the form of web-based learning (El Kadiri et al. 2016 ). More recently, the TEL tools portfolio has rapidly expanded to make more efficient joint use of novel learning concepts, methodologies, and technological enablers to achieve more direct, effective, and lasting learning impacts. Virtual learning environments, mobile-learning solutions, and AR/VR technologies and head-mounted displays have been employed so that trainees are empowered to follow their own training pace, learning topics, and assessment tests that fit their needs (Costello and McNaughton 2018 ; Mueller et al. 2011 ; Muller Queiroz et al. 2018 ). The expanding use of social networking tools has also brought attention to the contribution of social and collaborative learning (Hester et al. 2016 ; Wei and Ram 2016 ).

Contemporary learning systems supporting adaptive, personalized, and collaborative learning expand the tools available in eOL and contribute to the adoption, efficiency, and general prospects of the introduction of TEL in organizations (Cheng et al. 2011 ). In recent years, eOL has emphasized how enterprises share knowledge internally and externally, with particular attention being paid to systems that leverage collaborative learning and social learning functionalities (Qi and Chau 2016 ; Wang  2011 ). This is the essence of computer-supported collaborative learning (CSCL). The CSCL literature has developed a framework that combines individual learning, organizational learning, and collaborative learning, facilitated by establishing adequate learning flows and emerges effective learning in an enterprise learning (Goggins et al. 2013 ), in Fig.  1 .

figure 1

Representation of the combination of enterprise learning and knowledge flows. (adapted from Goggins et al. 2013 )

Establishing efficient knowledge and learning flows is a primary target for future data-driven enterprises (El Kadiri et al. 2016 ). Given the involved knowledge, the human resources, and the skills required by enterprises, there is a clear need for continuous, flexible, and efficient learning. This can be met by contemporary learning systems and practices that provide high adoption, smooth usage, high satisfaction, and close alignment with the current practices of an enterprise. Because the required competences of an enterprise evolve, the development of competence models needs to be agile and to leverage state-of-the art technologies that align with the organization’s processes and models. Therefore, in this paper we provide a review of the eOL research in order to summarize the findings, identify the various capabilities of eOL, and guide the development of organizational learning in future enterprises as well as in future studies.

3 Methodology

To answer our research questions, we conducted an SLR, which is a means of evaluating and interpreting all available research relevant to a particular research question, topic area, or phenomenon of interest. A SLR has the capacity to present a fair evaluation of a research topic by using a trustworthy, rigorous, and auditable methodology (Kitchenham and Charters 2007 ). The guidelines used (Kitchenham and Charters 2007 ) were derived from three existing guides adopted by medical researchers. Therefore, we adopted SLR guidelines that follow transparent and widely accepted procedures (especially in the area of software engineering and information systems, as well as in e-learning), minimize potential bias (researchers), and support reproducibility (Kitchenham and Charters 2007 ). Besides the minimization of bias and support for reproducibility, an SLR allows us to provide information about the impact of some phenomenon across a wide range of settings, contexts, and empirical methods. Another important advantage is that, if the selected studies give consistent results, SLRs can provide evidence that the phenomenon is robust and transferable (Kitchenham and Charters 2007 ).

3.1 Article Collection

Several procedures were followed to ensure a high-quality review of the literature of eOL. A comprehensive search of peer-reviewed articles was conducted in February 2019 (short papers, posters, dissertations, and reports were excluded), based on a relatively inclusive range of key terms: “organizational learning” & “elearning”, “organizational learning” & “e-learning”, “organisational learning” & “elearning”, and “organisational learning” & “e-learning”. Publications were selected from 2010 onwards, because we identified significant advances since 2010 (e.g., MOOCs, learning analytics, personalized learning) in the area of learning technologies. A wide variety of databases were searched, including SpringerLink, Wiley, ACM Digital Library, IEEE Xplore, Science Direct, SAGE, ERIC, AIS eLibrary, and Taylor & Francis. The selected databases were aligned with the SLR guidelines (Kitchenham and Charters 2007 ) and covered the major venues in IS and educational technology (e.g., a basket of eight IS journals, the top 20 journals in the Google Scholar IS subdiscipline, and the top 20 journals in the Google Scholar Educational Technology subdiscipline). The search process uncovered 2,347 peer-reviewed articles.

3.2 Inclusion and Exclusion Criteria

The selection phase determines the overall validity of the literature review, and thus it is important to define specific inclusion and exclusion criteria. As Dybå and Dingsøyr ( 2008 ) specified, the quality criteria should cover three main issues – namely, rigor, credibility, and relevance – that need to be considered when evaluating the quality of the selected studies. We applied eight quality criteria informed by the proposed Critical Appraisal Skills Programme (CASP) and related works (Dybå and Dingsøyr 2008 ). Table 1 presents these criteria.

Therefore, studies were eligible for inclusion if they were focused on eOL. The aforementioned criteria were applied in stages 2 and 3 of the selection process (see Fig.  2 ), when we assessed the papers based on their titles and abstracts, and read the full papers. From March 2020, we performed an additional search (stage 4) following the same process for papers published after the initial search period (i.e., 2010–February 2019). The additional search returned seven papers. Figure 2 summarizes the stages of the selection process.

figure 2

Stages of the selection process

3.3 Analysis

Each collected study was analyzed based on the following elements: study design (e.g., experiment, case study), area (e.g., IT, healthcare), technology (e.g., wiki, social media), population (e.g., managers, employees), sample size, unit of analysis (individual, firm), data collections (e.g., surveys, interviews), research method, data analysis, and the main research objective of the study. It is important to highlight that the articles were coded based on the reported information, that different authors reported information at different levels of granularity (e.g., an online system vs. the name of the system), and that in some cases the information was missing from the paper. Overall, we endeavored to code the articles as accurately and completely as possible.

The coding process was iterative with regular consensus meetings between the two researchers involved. The primary coder prepared the initial coding for a number of articles and both coders reviewed and agreed on the coding in order to reach the final codes presented in the Appendix . Disagreements between the coders and inexplicit aspects of the reviewed papers were discussed and resolved in regular consensus meetings. Although this process did not provide reliability indices (e.g., Cohen’s kappa), it did provide certain reliability in terms of consistency of the coding and what Krippendorff ( 2018 ) stated as the reliability of “the degree to which members of a designated community concur on the readings, interpretations, responses to, or uses of given texts or data”, which is considered acceptable research practice (McDonald et al. 2019 ).

In this section, we present the detailed results of the analysis of the 47 papers. Analysis of the studies was performed using non-statistical methods that considered the variables reported in the Appendix . This section is followed by an analysis and discussion of the categories.

4.1 Sample Size and Population Involved

The categories related to the sample of the articles and included the number of participants in each study (size), their position (e.g., managers, employees), and the area/topic covered by the study. The majority of the studies involved employees (29), with few studies involving managers (6), civil servants (2), learning specialists (2), clients, and researchers. Regarding the sample size, approximately half of the studies (20) were conducted with fewer than 100 participants; some (12) can be considered large-scale studies (more than 300 participants); and only a few (9) can be considered small scale (fewer than 20 participants). In relation to the area/topic of the study, most studies (11) were conducted in the context of the IT industry, but there was also good coverage of other important areas (i.e., healthcare, telecommunications, business, public sector). Interestingly, several studies either did not define the area or were implemented in a generic context (sector-agnostic studies, n = 10), and some studies were implemented in a multi-sector context (e.g., participants from different sections or companies, n = 4).

4.2 Research Methods

When assessing the status of research for an area, one of the most important aspects is the methodology used. By “method” in the Appendix , we refer to the distinction between quantitative, qualitative, and mixed methods research. In addition to the method, in our categorization protocol we also included “study design” to refer to the distinction between survey studies (i.e., those that gathered data by asking a group of participants), experiments (i.e., those that created situations to record beneficial data), and case studies (i.e., those that closely studied a group of individuals).

Based on this categorization, the Appendix shows that the majority of the papers were quantitative (34) and qualitative (7), with few studies (6) utilizing mixed methods. Regarding the study design, most of the studies were survey studies (26), 13 were case studies, and fewer were experiments (8). For most studies, the individual participant (40) was the unit of analysis, with few studies having the firm as the unit of analysis, and only one study using the training session as a unit of analysis. Regarding the measures used in the studies, most utilized surveys (39), with 11 using interviews, and only a few studies using field notes from focus groups (2) and log files from the systems (2). Only eight studies involved researchers using different measures to triangulate or extend their findings. Most articles used structural equation modeling (SEM) (17) to analyze their data, with 13 studies employing descriptive statistics, seven using content analysis, nine using regression analysis or analyses of variances/covariance, and one study using social network analysis (SNA).

4.3 Technologies

Concerning the technology used, most of the studies (17) did not study a specific system, referring instead in their investigation to a generic e-learning or technological solution. Several studies (9) named web-based learning environments, without describing the functionalities of the identified system. Other studies focused on online learning environments (4), collaborative learning systems (3), social learning systems (3), smart learning systems (2), podcasting (2), with the rest of the studies using a specific system (e.g., a wiki, mobile learning, e-portfolios, Second Life, web application).

4.4 Research Objectives

The research objectives of the studies could be separated into six main categories. The first category focuses on the intention of the employees to use the technology (9); the second focuses on the performance of the employees (8); the third focuses on the value/outcome for the organization (4); the fourth focuses on the actual usage of the system (7); the fifth focuses on employees’ satisfaction (4); and the sixth focuses on the ability of the proposed system to foster learning (9). In addition to these six categories, we also identified studies that focused on potential barriers for eOL in organizations (Stoffregen et al. 2016 ), the various benefits associated with the successful implementation of eOL (Liu et al. 2012 ), the feasibility of eOL (Kim et al. 2014 ; Mueller et al. 2011 ), and the alignment of the proposed innovation with the other processes and systems in the organization (Costello and McNaughton 2018 ).

4.5 E-learning Capabilities in Various Organizations and for Various Objectives

The technology used has an inherent role for both the organization and the expected eOL objective. E-learning systems are categorized based on their functionalities and affordances. Based on the information reported in the selected papers, we ranked them based on the different technologies and functionalities (e.g., collaborative, online, smart). To do so, we focused on the main elements described in the selected paper; for instance, a paper that described the system as wiki-based or indicated that the system was Second Life was ranked as such, rather than being added to collaborative systems or social learning respectively. We did this because we wanted to capture all the available information since it gave us additional insights (e.g., Second Life is both a social and a VR system).

To investigate the connection between the various technologies used to enhance organizational learning and their application in the various organizations, we utilized the coding (see Appendix ) and mapped the various e-learning technologies (or their affordances) with the research industries to which they applied (Fig.  3 ). There was occasionally a lack of detailed information about the capabilities of the e-learning systems applied (e.g., generic, or a web application, or an online system), which limited the insights. Figure 3 provides a useful mapping of the confluence of e-learning technologies and their application in the various industries.

figure 3

Association of the different e-learning technologies with the industries to which they are applied in the various studies. Note: The size of the circles depicts the frequency of studies, with the smallest circle representing one study and the largest representing six studies. The mapping is extracted from the data in the Appendix , which outlines the papers that belong in each of the circles

To investigate the connection between the various technologies used to enhance organizational learning and their intended objectives, we utilized the coding of the articles (see Appendix ) and mapped the various e-learning technologies (or their affordances) with the intended objectives, as reported in the various studies (Fig.  4 ). The results in Fig.  4 show the objectives that are central in eOL research (e.g., performance, fostering learning, adoption, and usage) as well as those objectives on which few studies have focused (e.g., alignment, feasibility, behavioral change). In addition, the results also indicate the limited utilization of the various e-learning capabilities (e.g., social, collaborative, smart) to achieve objectives connected with those capabilities (e.g., social learning and behavioral change, collaborative learning, and barriers).

figure 4

Association of the different e-learning technologies with the objectives investigated in the various studies. Note: The size of the circles depicts the frequency of studies, with the smallest circle representing one study and the largest representing five studies. The mapping is extracted from the data in the Appendix , which outlines the papers that belong in each of the circles

5 5. Discussion

After reviewing the 47 identified articles in the area of eOL, we can observe that all the works acknowledge the importance of the affordances offered by different e-learning technologies (e.g., remote collaboration, anytime anywhere), the importance of the relationship between eOL and employees’ satisfaction and performance, and the benefits associated with organizational value and outcome. Most of the studies agree that eOL provides employees, managers, and even clients with opportunities to learn in a more differentiated manner, compared to formal and face-to-face learning. However, how the organization adopts and puts into practice these capabilities to leverage them and achieve its goals are complex and challenging procedures that seem to be underexplored.

Several studies (Lee et al. 2015a ; Muller Queiroz et al. 2018 ; Tsai et al. 2010 ) focused on the positive effect of perceived managerial support, perceived usefulness, perceived ease of use, and other technology acceptance model (TAM) constructs of the e-learning system in supporting all three levels of learning (i.e., individual, collaborative, and organizational). Another interesting dimension highlighted by many studies (Choi and Ko 2012 ; Khalili et al. 2012 ; Yanson and Johnson 2016 ) is the role of socialization in the adoption and usage of the e-learning systems that offer these capabilities. Building connections and creating a shared learning space in the e-learning system is challenging but also critical for the learners (Yanson and Johnson 2016 ). This is consistent with the expectancy-theoretical explanation of how social context impacts on employees’ motivation to participate in learning (Lee et al. 2015a ; Muller Queiroz et al. 2018 ).

The organizational learning literature suggests that e-learning may be more appropriate for the acquisition of certain types of knowledge than others (e.g., procedural vs. declarative, or hard-skills vs. soft-skills); however, there is no empirical evidence for this (Yanson and Johnson 2016 ). To advance eOL research, there is a need for a significant move to address complex, strategic skills by including learning and development professionals (Garavan et al. 2019 ) and by developing strategic relationships. Another important element is to utilize e-learning technology that addresses and integrates organizational, individual, and social perspectives in eOL (Wang  2011 ). This is also identified in our literature review since we found only limited specialized e-learning systems in domain areas that have traditionally benefited from such technology. For instance, although there were studies that utilized VR environments (Costello and McNaughton 2018 ; Muller Queiroz et al. 2018 ) and video-based learning systems (Wei et al. 2013 ; Wei and Ram 2016 ), there was limited focus in contemporary eOL research on how specific affordances of the various environments that are used in organizations (e.g., Carnetsoft, Outotec HSC, and Simscale for simulations of working environments; or Raptivity, YouTube, and FStoppers to gain specific skills and how-to knowledge) can benefit the intended goals or be integrated with the unique qualities of the organization (e.g., IT, healthcare).

For the design and the development of the eOL approach, the organization needs to consider the alignment of individual learning needs, organizational objectives, and the necessary resources (Wang  2011 ). To achieve this, it is advisable for organizations to define the expected objectives, catalogue the individual needs, and select technologies that have the capacity to support and enrich learners with self-directed and socially constructed learning practices in the organization (Wang  2011 ). This needs to be done by taking into consideration that on-demand eOL is gradually replacing the classic static eOL curricula and processes (Dignen and Burmeister 2020 ).

Another important dimension of eOL research is the lenses used to approach effectiveness. The selected papers approached effectiveness with various objectives, such as fostering learning, usage of the e-learning system, employees’ performance, and the added organizational value (see Appendix ). To measure these indices, various metrics (quantitative, qualitative, and mixed) have been applied. The qualitative dimensions emphasize employees’ satisfaction and system usage (e.g., Menolli et al. 2020 ; Turi et al. 2019 ), as well as managers’ perceived gained value and benefits (e.g., Lee et al. 2015b ; Xiang et al. 2020 ) and firms’ perceived effective utilization of eOL resources (López-Nicolás and Meroño-Cerdán 2011 ). The quantitative dimensions focus on usage, feasibility, and experience at different levels within an organization, based on interviews, focus groups, and observations (Costello and McNaughton 2018 ; Michalski 2014 ; Stoffregen et al. 2016 ). However, it is not always clear the how eOL effectiveness has been measured, nor the extent to which eOL is well aligned with and is strategically impactful on delivering the strategic agenda of the organization (Garavan et al. 2019 ).

Research on digital technologies is developing rapidly, and big data and business analytics have the potential to pave the way for organizations’ digital transformation and sustainable development (Mikalef et al. 2018 ; Pappas et al. 2018 ); however, our review finds surprisingly limited use of big data and analytics in eOL. Despite contemporary e-learning systems adopting data-driven mechanisms, as well as advances in learning analytics (Siemens and Long 2011 ), the results of our analysis indicate that learner-generated data in the context of eOL are used in only a few studies to extract very limited insights with respect to the effectiveness of eOL and the intended objectives of the respective study (Hung et al. 2015 ; Renner et al. 2020 ; Rober and Cooper 2011 ). Therefore, eOL research needs to focus on data-driven qualities that will allow future researchers to gain deeper insights into which capabilities need to be developed to monitor the effectiveness of the various practices and technologies, their alignment with other functions of the organization, and how eOL can be a strategic and impactful vehicle for materializing the strategic agenda of the organization.

5.1 Status of eOL Research

The current review suggests that, while the efficient implementation of eOL entails certain challenges, there is also a great potential for improving employees’ performance as well as overall organizational outcome and value. There are also opportunities for improving organizations’ learning flow, which might not be feasible with formal learning and training. In order to construct the main research dimensions of eOL research and to look more deeply at the research objectives of the studies (the information we coded as objectives in the Appendix ), we performed a content analysis and grouped the research objectives. This enabled us to summarize the contemporary research on eOL according to five major categories, each of which is describes further below. As the research objectives of the published work shows, the research on eOL conducted during the last decade has particularly focused on the following five directions.

Investigating the capabilities of different technologies in different organizations.

Research has particularly focused on how easy the technology is to use, on how useful it is, or on how well aligned/integrated it is with other systems and processes within the organization. In addition, studies have used different learning technologies (e.g., smart, social, personalized) to enhance organizational learning in different contexts and according to different needs. However, most works have focused on affordances such as remote training and the development of static courses or modules to share information with learners. Although a few studies have utilized contemporary e-learning systems (see Appendix ), even in these studies there is a lack of alignment between the capabilities of those systems (e.g., open online course, adaptive support, social and collaborative learning) and the objectives and strategy of the organization (e.g., organizational value, fostering learning).

Enriching the learning flow and learning potential in different levels within an organization.

The reviewed work has emphasized how different factors contribute to different levels of organizational learning, and it has focused on practices that address individual, collaborative, and organizational learning within the structure of the organization. In particular, most of the reviewed studies recognize that organizational learning occurs at multiple levels: individual, team (or group), and organization. In other words, although each of the studies carried out an investigation within a given level (except for Garavan et al. 2019 ), there is a recognition and discussion of the different levels. Therefore, the results align with the 4I framework of organizational learning that recognizes how learning across the different levels is linked by social and psychological processes: intuiting, interpreting, integrating, and institutionalizing (the 4Is) (Crossan et al. 1999 ). However, most of the studies focused on the institutionalizing-intuiting link (i.e., top-down feedback); moreover, no studies focused on contemporary learning technologies and processes that strengthen the learning flow (e.g., self-regulated learning).

Identifying critical aspects for effective eOL.

There is a considerable amount of predominantly qualitative studies that focus on potential barriers to eOL implementation as well as on the risks and requirements associated with the feasibility and successful implementation of eOL. In the same vein, research has emphasized the importance of alignment of eOL (both in processes and in technologies) within the organization. These critical aspects for effective eOL are sometimes the main objectives of the studies (see Appendix ). However, most of the elements relating to the effectiveness of eOL were measured with questionnaires and interviews with employees and managers, and very little work was conducted on how to leverage the digital technologies employed in eOL, big data, and analytics in order to monitor the effectiveness of eOL.

Implementing employee-centric eOL.

In most of the studies, the main objective was to increase employees’ adoption, satisfaction, and usage of the e-learning system. In addition, several studies focused on the e-learning system’s ability to improve employees’ performance, increase the knowledge flow in the organization, and foster learning. Most of the approaches were employee-centric, with a small amount of studies focusing on managers and the firm in general. However, employees were seen as static entities within the organization, with limited work investigating how eOL-based training exposes employees to new knowledge, broadens their skills repertoire, and has tremendous potential for fostering innovation (Lin and Sanders 2017 ).

Achieving goals associated with the value creation of the organization.

A considerable number of studies utilized the firm (rather than the individual employee) as the unit of analysis. Such studies focused on how the implementation of eOL can increase employee performance, organizational value, and customer value. Although this is extremely helpful in furthering knowledge about eOL technologies and practices, a more granular investigation of the different e-learning systems and processes to address the various goals and strategies of the organization would enable researchers to extract practical insights on the design and implementation of eOL.

5.2 Research Agenda

By conducting an SLR and documenting the eOL research of the last decade, we have identified promising themes of research that have the potential to further eOL research and practice. To do so, we define a research agenda consisting of five thematic areas of research, as depicted in the research framework in Fig.  5 , and we provide some suggestions on how researchers could approach these challenges. In this visualization of the framework, on the left side we present the organizations as they were identified from our review (i.e., area/topic category in the Appendix ) and the multiple levels where organizational learning occurs (Costello and McNaughton 2018 ). On the right side, we summarize the objectives as they were identified from our review (i.e., the objectives category in the Appendix ). In the middle, we depict the orchestration that was conducted and how potential future research on eOL can improve the orchestration of the various elements and accelerate the achievement of the intended objectives. In particular, our proposed research agenda includes five research themes discussed in the following subsections.

figure 5

E-learning capabilities to enhance organizational research agenda

5.2.1 Theme 1: Couple E-learning Capabilities With the Intended Goals

The majority of the eOL studies either investigated a generic e-learning system using the umbrella term “e-learning” or did not provide enough details about the functionalities of the system (in most cases, it was simply defined as an online or web system). This indicates the very limited focus of the eOL research on the various capabilities of e-learning systems. In other words, the literature has been very detailed on the organizational value and employees’ acceptance of the technology, but less detailed on the capabilities of this technology that needs to be put into place to achieve the intended goals and strategic agenda. However, the capabilities of the e-learning systems and their use are not one-size-fits-all, and the intended goals (to obtain certain skills and competences) and employees’ needs and backgrounds play a determining role in the selection of the e-learning system (Al-Fraihat et al. 2020 ).

Only in a very few studies (Mueller et al. 2011 ; Renner et al. 2020 ) were the capabilities of the e-learning solutions (e.g., mobile learning, VR) utilized, and the results were found to significantly contribute to the intended goals. The intended knowledge can be procedural, declarative, general competence (e.g., presentation, communication, or leadership skills) or else, and its particularities and the pedagogical needs of the intended knowledge (e.g., a need for summative/formative feedback or for social learning support) should guide the selection of the e-learning system and the respective capabilities. Therefore, future research needs to investigate how the various capabilities offered by contemporary learning systems (e.g., assessment mechanisms, social learning, collaborative learning, personalized learning) can be utilized to adequately reinforce the intended goals (e.g., to train personnel to use a new tool, to improve presentation skills).

5.2.2 Theme 2: Embrace the Particularities of the Various Industries

Organizational learning entails sharing knowledge and enabling opportunities for growth at the individual, group, team, and organizational levels. Contemporary e-learning systems provide the medium to substantiate the necessary knowledge flow within organizations and to support employees’ overall learning. From the selected studies, we can infer that eOL research is either conducted in an industry-agnostic context (either generic or it was not properly reported) or there is a focus on the IT industry (see Appendix ). However, when looking at the few studies that provide results from different industries (Garavan et al. 2019 ; Lee et al. 2014 ), companies indicate that there are different practices, processes, and expectations, and that employees have different needs and perceptions with regards to e-learning systems and eOL in general. Such particularities influence the perceived dimensions of a learning organization. Some industries noted that eOL promoted the development of their learning organizations, whereas others reported that eOL did not seem to contribute to their development as a learning organization (Yoo and Huang 2016 ). Therefore, it is important that the implementation of organizational learning embraces the particularities of the various industries and future research needs to identify how the industry-specific characteristics can inform the design and development of organizational learning in promoting an organization’s goals and agenda.

5.2.3 Theme 3: Utilize E-learning Capabilities to Implement Employee-centric Approaches

For efficient organizational learning to be implemented, the processes and technologies need to recognize that learning is linked by social and psychological processes (Crossan et al. 1999 ). This allows employees to develop learning in various forms (e.g., social, emotional, personalized) and to develop elements such as self-awareness, self-control, and interpersonal skills that are vital for the organization. Looking at the contemporary eOL research, we notice that the exploration of e-learning capabilities to nurture the aforementioned elements and support employee-centric approaches is very limited (e.g., personalized technologies, adaptive assessment). Therefore, future research needs to collect data to understand how e-learning capabilities can be utilized in relation to employees’ needs and perceptions in order to provide solutions (e.g., collaborative, social, adaptive) that are employee-centric and focused on development, and that have the potential to move away from standard one-size-fits-all e-learning solutions to personalized and customized systems and processes.

5.2.4 Theme 4: Employ Analytics-enabled eOL

There is a lot of emphasis on measuring, via various qualitative and quantitative metrics, the effectiveness of eOL implemented at different levels in organizations. However, most of these metrics come from surveys and interviews that capture employees’ and managers’ perceptions of various aspects of eOL (e.g., fostering of learning, organizational value, employees’ performance), and very few studies utilize analytics (Hung et al. 2015 ; Renner et al. 2020 ; Rober and Cooper 2011 ). Given how digital technologies, big data, and business analytics pave the way towards organizations’ digital transformation and sustainable development (Mikalef et al. 2018 ; Pappas et al. 2018 ), and considering the learning analytics affordances of contemporary e-learning systems (Siemens and Long 2011 ), future work needs to investigate how learner/employee-generated data can be employed to inform practice and devise more accurate and temporal effectiveness metrics when measuring the importance and impact of eOL.

5.2.5 Theme 5: Orchestrate the Employees’ Needs, Resources, and Objectives in eOL Implementation

While considerable effort has been directed towards the various building blocks of eOL implementation, such as resources (intangible, tangible, and human skills) and employees’ needs (e.g., vision, growth, skills development), little is known so far about the processes and structures necessary for orchestrating those elements in order to achieve an organization’s intended goals and to materialize its overall agenda. In other words, eOL research has been very detailed on some of the elements that constitute efficient eOL, but less so on the interplay of those elements and how they need to be put into place. Prior literature on strategic resource planning has shown that competence in orchestrating such elements is a prerequisite to successfully increasing business value (Wang et al. 2012 ). Therefore, future research should not only investigate each of these elements in silos, but also consider their interplay, since it is likely that organizations with similar resources will exert highly varied levels in each of these elements (e.g., analytics-enabled, e-learning capabilities) to successfully materialize their goals (e.g., increase value, improve the competence base of their employees, modernize their organization).

5.3 Implications

Several implications for eOL have been revealed in this literature review. First, most studies agree that employees’ or trainees’ experience is extremely important for the successful implementation of eOL. Thus, keeping them in the design and implementation cycle of eOL will increase eOL adoption and satisfaction as well as reduce the risks and barriers. Another important implication addressed by some studies relates to the capabilities of the e-learning technologies, with easy-to-use, useful, and social technologies resulting in more efficient eOL (e.g., higher adoption and performance). Thus, it is important for organizations to incorporate these functionalities in the platform and reinforce them with appropriate content and support. This should not only benefit learning outcomes, but also provide the networking opportunities for employees to broaden their personal networks, which are often lost when companies move from face-to-face formal training to e-learning-enabled organizational learning.

5.4 Limitations

This review has some limitations. First, we had to make some methodological decisions (e.g., selection of databases, the search query) that might lead to certain biases in the results. However, tried to avoid such biases by considering all the major databases and following the steps indicated by Kitchenham and Charters ( 2007 ). Second, the selection of empirical studies and coding of the papers might pose another possible bias. However, the focus was clearly on the empirical evidence, the terminology employed (“e-learning”) is an umbrella term that covers the majority of the work in the area, and the coding of papers was checked by two researchers. Third, some elements of the papers were not described accurately, leading to some missing information in the coding of the papers. However, the amount of missing information was very small and could not affect the results significantly. Finally, we acknowledge that the selected methodology (Kitchenham and Charters 2007 ) includes potential biases (e.g., false negatives and false positives), and that different, equally valid methods (e.g., Okoli and Schabram 2010 ) might have been used and have resulted in slightly different outcomes. Nevertheless, despite the limitations of the selected methodology, it is a well-accepted and widely used literature review method in both software engineering and information systems (Boell and Cecez-Kecmanovic 2014 ), providing certain assurance of the results.

6 Conclusions and Future Work

We have presented an SLR of 47 contributions in the field of eOL over the last decade. With respect to RQ1, we analyzed the papers from different perspectives, such as research methodology, technology, industries, employees, and intended outcomes in terms of organizational value, employees’ performance, usage, and behavioral change. The detailed landscape is depicted in the Appendix and Figs.  3 and 4 ; with the results indicating the limited utilization of the various e-learning capabilities (e.g., social, collaborative) to achieve objectives connected with those capabilities (e.g., social learning and behavioral change, collaborative learning and overcoming barriers).

With respect to RQ2, we categorized the main findings of the selected papers into five areas that reflect the status of eOL research, and we have discussed the challenges and opportunities emerging from the current review. In addition, we have synthesized the extracted challenges and opportunities and proposed a research agenda consisting of five elements that provide suggestions on how researchers could approach these challenges and exploit the opportunities. Such an agenda will strengthen how e-learning can be leveraged to enhance the process of improving actions through better knowledge and understanding in an organization.

A number of suggestions for further research have emerged from reviewing prior and ongoing work on eOL. One recommendation for future researchers is to clearly describe the eOL approach by providing detailed information about the technologies and materials used, as well as the organizations. This will allow meta-analyses to be conducted and it will also identify the potential effects of a firm’s size or area on the performance and other aspects relating to organizational value. Future work should also focus on collecting and triangulating different types of data from different sources (e.g., systems’ logs). The reviewed studies were conducted mainly by using survey data, and they made limited use of data coming from the platforms; thus, the interpretations and triangulation between the different types of collected data were limited.

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Literature Review – Employee Training and Development

Introduction.

Human resources are considered by many to be the most important asset of an organization, yet very few employers are able to harness the full potential from their employees (Radcliffe, 2005). Human resource is a productive resource consisting of the talents and skills of human beings that contribute to the production of goods and services (Kelly, 2001). Lado and Wilson (1994) define human resource system as a set of distinct but interrelated activities, functions, and processes that are directed at attracting, developing, and maintaining a firm’s human resources. According to Gomez-Mejia, Luis R., David B. Balkin and Robert L. Cardy, (2008), it is the process of ensuring that the organization has the right kind of people in the right places at the right time. The objective of Human Resources is to maximize the return on investment from the organization’s human capital and minimize financial risk. It is the responsibility of human resource managers to conduct these activities in an effective, legal, fair, and consistent manner (Huselid, 1995).

Employee Training and Development

Training and development is a subsystem of an organization that emanate from two independent yet interdependent words training and development. Training is often interpreted as the activity when an expert and learner work together to effectively transfer information from the expert to the learner (to enhance a learner’s knowledge, attitudes or skills) so the learner can better perform a current task or job. Training activity is both focused upon, and evaluated against, the job that an individual currently holds (Learner R., 1986). On the other hand development is often viewed as a broad, ongoing multi-faceted set of activities (training activities among them) to bring someone or an organization up to another threshold of performance. This development often includes a wide variety of methods, e.g., orienting about a role, training in a wide variety of areas, ongoing training on the job, coaching, mentoring and forms of self-development. Some view development as a life-long goal and experience. Development focuses upon the activities that the organization employing the individual, or that the individual is part of, may partake in the future, and is almost impossible to evaluate (Nadler Leonard, 1984).

Training and development ensures that randomness is reduced and learning or behavioral change takes place in structured format. In the field of human resource management , training and development is the field concerned with organizational activity aimed at bettering the performance of individuals and groups in organizational settings. It has been known by several names, including employee development, human resource development , and learning and development (Harrison Rosemary, 2005).

As the generator of new knowledge, employee training and development is placed within a broader strategic context of human resources management , i.e. global organizational management, as a planned staff education and development, both individual and group, with the goal to benefit both the organization and employees. To preserve its obtained positions and increase competitive advantage , the organization needs to be able to create new knowledge , and not only to rely solely on utilization of the existing (Vemic, 2007). Thus, the continuous employee training and development has a significant role in the development of individual and organizational performance . The strategic procedure of employee training and development needs to encourage creativity, ensure inventiveness and shape the entire organizational knowledge that provides the organization with uniqueness and differentiates it from the others.

The Value of Training and Development

According to Beardwell & Holden (1997) human resource management has emerged as a set of prescriptions for managing people at work. Its central claim is that by matching the size and skills of the workforce to the productive requirements of the organization, and by raising the quality of individual employee contributions to production, organizations can make significant improvements on their performance.

The environment of an organization refers to the sum total of the factors or variables that may influence the present and future survival of an organization (Armstrong, 1998). The factors may be internal or external to the organization. Cascio W. F, (1995), uses the terms societal environment to define the varying trends and general forces that do not relate directly to the company but could impact indirectly on the company at some point in time. Four of these forces are identified as economic, technological, legal and political and socio-cultural and demographic forces. The second type of environment is the task environment that comprises elements directly influencing the operations and strategy of the organization. These may include the labour market, trade unions, competition and product markets comprising customers, suppliers and creditors. The task environment elements are directly linked to the company and are influenced by the societal environment.

However, variables in the task, competitive or operative environment as they are variously referred to, affect organizations in a specific industry and it is possible to control them to some extent. As such, environmental change, whether remote or task, disrupts the equilibrium that exists between the organization’s strategy and structure, necessitating adjustment to change. Pfeffer (1998) proposes that there is evidence demonstrating that effectively managed people can produce substantially enhanced economic performance. Pfeffer extracted from various studies, related literature, and personal observation and experience a set of seven dimensions that seem to characterize most if not all of the systems producing profits through people. He named them the seven practices of successful organizations and they are: employment security, selective hiring of new personnel, self-managed teams and decentralization of decision making as the basic principles of organizational design, comparatively high compensation contingent on organizational performance , extensive training, reduced status distinctions and barriers, including dress, language, office arrangements, and wage differences across levels, and extensive sharing of financial and performance information throughout the organization.

Effect of Training and Development on Employee Productivity

McGhee (1997) stated that an organization should commit its resources to a training activity only if, in the best judgment of managers, the training can be expected to achieve some results other than modifying employee behavior. It must support some organizational goals , such as more efficient production or distribution of goods and services, product operating costs, improved quality or more efficient personal relations is the modification of employees behavior affected through training should be aimed at supporting organization objectives.

Effect of Training and Development on Employee Motivation

Motivation is concerned with the factors that influence people to behave in certain ways. Arnold etal (1991), have listed the components as being, direction-what a person is trying to do, effort- how hard a person is trying to and persistence- how long a person keeps on trying. Motivating other people is about getting them to move in the direction you want them to go in order to achieve a result, well motivated people are those with clearly defined goals who take action that they expect will achieve those goals. Motivation at work can take place in two ways. First, people can motivate themselves by seeking, finding and carrying out that which satisfies their needs or at least leads them to expect that their goals will be achieved. Secondly, management can motivate people through such methods as pay, promotion, praise and training (Synderman 1957). The organization as a whole can provide the context within which high levels of motivation can be achieved training the employees in areas of their job performance.

Effect of Training and Development on Competitive Advantage

Competitive advantage is the essence of competitive strategy . It encompasses those capabilities, resources, relationships, and decisions, which permits an organization to capitalize on opportunities in the marketplace and to avoid threats to its desired position, (Lengnick-Hall 1990). Boxall and Purcell (1992) suggest that ‘human resource advantage can be traced to better people employed in organizations with better processes.’ This echoes the resource based view of the firm, which states that ‘distinctive human resource practices help to create the unique competences that determine how firms compete’ (Capelli and Crocker- Hefter, 1996). Intellectual capital is the source of competitive advantage for organizations. The challenge is to ensure that firms have the ability to find, assimilate, compensate, and retain human capital in shape of talented individual who can drive a global organization that both responsive to its customer and ‘the burgeoning opportunities of technology’ (Armstrong, 2005)

Effect of Training and Development on Customer Relations

William Edward Deming , one of the quality Gurus defines quality as a predictable degree of uniformity and dependability at low costs and suitable to the market, he advises that an organization should focus on the improvement of the process as the system rather than the work is the cause of production variation (Gale 1994). Many service organizations have embraced this approach of quality assurance by checking on the systems and processes used to deliver the end product to the consumer.  Essentially this checks on; pre-sale activities which encompass the advice and guidance given to a prospective client, customer communications ( how well the customers are informed of the products and services, whether there are any consultancy services provided to help the customers assess their needs and any help line available for ease of access to information on products), the speed of handling a client’s transactions and processing of claims, the speed of handling customers calls and the number of calls abandoned or not answered, on the selling point of Products/Services a customer would be interested to know   about the opening   hours of the organization, the convenience of the location and such issues (Gale 1994). This is only possible when employees are well trained and developed to ensure sustainability of the same.

  • Armstrong, M (1998): Human Resource Management: Strategy and Action, Irwin, Boston
  • Betcherman, G., K. McMullen and K. Davidman (1998), Training for the New Economy: A Synthesis Report, Canadian Policy Research Network, Ottawa, pp. 117
  • Cascio, W. F. (1995). Whither industrial and organizational psychology in a changing world of work?American Psychologist, 50, 928—939
  • Harrison Rosemary (2005). Learning and Development.CIPD Publishing. pp.  5
  • Huselid, M. A. (1995) The impact of human resource management practices on turnover, productivity and corporate financial performance, Academy of Management Journal, 38(3), 635-672
  • Kelly D, (2001), Dual Perceptions of HRD: Issues for Policy: SME’s, Other Constituencies, and the Contested Definitions of Human Resource Development,
  • Lado, A., & Wilson, M. (1994) Human resource systems and sustained competitive advantage: A competency-based perspective, Academy of Management Journal, 19(4), 699-727
  • Learner, R. (1986).Concepts and Theories of Human Development (2nd ed.). New York: Random House).
  • Nadler, Leonard (1984). The Handbook of Human Resource Development (Glossary). New York: John Wiley & Sons.
  • Pfeffer J., (1998), The Human Equation; Building Profits by Putting People First, HBS press, Boston
  • Tessema, M. and Soeters, J. (2006) Challenges and prospects of HRM in developing countries: testing the HRM-performance link in Eritrean civil service, International Journal of Human Resource Management, 17(1), 86 -105

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REVIEW article

Developing competencies in public health: a scoping review of the literature on developing competency frameworks and student and workforce development.

Melissa MacKay

  • Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada

Effective and precise public health practice relies on a skilled and interdisciplinary workforce equipped with integrated knowledge, values, skills, and behaviors as defined by competency frameworks. Competency frameworks inform academic and professional development training, support performance evaluation, and identify professional development needs. The aim of this research was to systematically identify and examine trends in the extent, nature, and range of the literature related to developing competencies in public health. This includes developing public health competency frameworks, and how competencies are developed and maintained in students and practitioners. We used a scoping review methodology to systematically identify and report on trends in the literature. Two independent reviewers conducted title and abstract and full-text screening to assess the literature for relevance. Articles were included if they were original primary research or gray literature and published in English. No date or geographic restrictions were applied. Articles were included if they focused on developing competency statements or frameworks for public health and/or training public health students or practitioners to develop competencies. The review encompassed a range of methods and target populations, with an emphasis on building competencies through student and professional development. Foundational competency development was a primary focus, and we found a gap in discipline-specific competency research, especially within developing discipline-specific competency statements and frameworks. Several evidence-based practices for competency development were highlighted, including the importance of governance and resources to oversee competency framework development and implementation, and workforce planning. Experiential learning and competency-based training were commonly identified as best practices for building competencies. A comprehensive understanding of public health competency development—through developing and incorporating foundational and discipline-specific competencies, mapping student and practitioner training to competency frameworks, and incorporating best practices—will enable public health to create skills and an adaptable workforce capable of addressing complex public health issues.

1 Introduction

Health professionals require a specific set of skills, values, and knowledge–commonly referred to as core competencies–to be highly effective in their respective sectors. Core competencies represent a baseline standard of the knowledge, skills, and attitudes required among professionals in the field ( 1 ). Competency statements and frameworks are generated by allied health organizations and regulatory bodies globally, including core competencies for incoming medical students ( 2 ), entry-to-practice competencies for registered nurses ( 3 , 4 ), core competencies for clinical pharmacists ( 5 ), and professional competencies for veterinarians ( 6 , 7 ). While they vary across professions, these competencies encompass a set of agreed-upon interpersonal, clinical, scientific, and logical reasoning skills, values, and knowledge that practitioners should exhibit.

Specific to public health, competency statements and frameworks exist across public health organizations and institutions internationally. Governing bodies in Canada ( 1 ), the United States ( 8 ), the United Kingdom ( 9 ), the European Union ( 10 ), and Australia ( 11 ) have all released competency frameworks to guide public health workforce planning. Although each framework differs in its content and context, they all aim to strengthen the public health sector’s ability to prepare for and respond to public health challenges-- today and in the future ( 12 ).

Core competencies provide standards for training the public health workforce ( 1 , 8 , 12 ). These frameworks can inform academic and professional development curricula ( 1 ), assess workforce performance ( 13 ), and identify professional development needs. Core competencies can also help public health organizations maintain consistency, identify program needs, and help facilitate interdisciplinary work ( 1 , 8 ). Competency statements and frameworks can ensure public health practitioners have a shared understanding of their roles and responsibilities and that they are working toward a common goal of contributing to a healthier population ( 1 ).

Competency frameworks can become outdated as workforce needs evolve in response to changing population health demands. As such, there have been global calls ( 14 – 16 ) to strengthen and transform the public health workforce to adapt to evolving public health challenges like the COVID-19 pandemic ( 17 ), climate change ( 18 ), and economic inflation ( 17 ). In Canada, the Canadian Public Health Association (CPHA) explicitly called on the government to refresh their public health core competency framework to encompass these challenges ( 14 ). Further, Canada’s Chief Public Health Officer called for modernized and improved core competencies and curriculum in graduate public health education, and transformation of the public health workforce to better address complex public health challenges such as climate change and the COVID-19 pandemic ( 16 , 18 ).

Competency-based education and training guide curriculum planning, accreditation, and performance evaluation to enhance outcomes of public health practice ( 19 , 20 ). Further, competency frameworks bridge the gap between evidence and practice and allow for interdisciplinary skill set development ( 12 , 21 ). Flexible learning, mentorship, and feedback are key factors in effective competency-based education in healthcare ( 20 ). Experiential learning, practice-based learning, and reflective practices are also effective pedagogical practices for developing competencies in public health students ( 22 ). In Canada, graduate-level training matched to public health competencies is especially important given the lack of a national, comprehensive competency training program ( 23 ).

As Canada and other countries around the world begin to transform their public health workforce, agencies must draw on evidence-based practices for building competencies. There are currently gaps in understanding best practices for developing, implementing, and evaluating public health competency frameworks, and there are no published reviews that capture the breadth of this process. Related research has described the process for developing public health competency frameworks including the need for transparent reporting, validated instruments, and consensus-building approaches ( 24 ), but no such reviews exist examining the full scope of research on building competencies in public health. Thus, there is a need for synthesized results to inform evidence-based practices related to developing public health competencies going forward.

The aim of this research is to systematically identify and examine trends in the extent and nature of the literature related to developing competencies in public health. The scope includes developing public health competency frameworks, and identifying how competencies are developed and maintained in students and practitioners. The objectives of this research are:

1. To conduct a scoping review of the relevant literature regarding developing competencies in public health; and,

2. To report on trends in the extent and nature of the literature identified.

2 Materials and methods

This scoping review followed the framework by Arskey and O’Malley ( 25 ), and updated by Levac et al. ( 26 ) to examine the extent, nature, and range of research related to developing public health competencies. Initially, the review intended to focus on health communication competencies; however, the literature was limited in this area, so the review reflects the full range of developing public health competencies. This research adheres to the PRISMA for Scoping Reviews reporting guidelines ( 27 ).

2.1 Search strategy

A comprehensive search strategy was developed by the research team in collaboration with a Specialist Librarian from the University of Guelph. The search contained three concepts: health communication, public health, and pedagogy/education/competencies ( Table 1 ). The search was piloted in Ovid via MEDLINE, as well as by screening the reference lists of relevant articles. Six databases were searched including Ovid via MEDLINE, PsycINFO, Web of Science, Communication and Mass Media Complete, ERIC, and CAB Direct to identify relevant literature. The search was conducted on November 24, 2022. The search was verified by hand searching the following journals, which were most often cited in the database search results: Journal of Public Health Management & Practice, American Journal of Public Health, Journal of Health Communication, Health Promotion Practice , and Health Communication .

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Table 1 . Search concepts, controlled vocabulary, and keywords.

Using appropriate combinations of concepts and keywords, Google was also searched to identify relevant gray literature. The first 10 pages were searched for each of the following keyword/phrase combinations: “public health” AND core competencies, core competencies AND “public health workforce,” core competencies AND “public health students,” and teaching and learning AND “public health.”

All citations obtained in the database and gray literature search were downloaded to Mendeley ( 28 ), where the deduplication tool was used. The deduplicated references were then uploaded into DistillerSR ( 29 ) to manage the screening and data extraction processes.

2.2 Study inclusion criteria

To be included, literature had to be published in English from any location, and focused on competency development in public health practitioners and/or students Literature also had to focus on developing competency statements or frameworks for public health and/or training public health practitioners or students to develop competencies. No date restrictions were used, and original primary research and gray literature were included.

Studies were excluded if they focused on competency development in other disciplines (e.g., nursing, medicine, dentistry, etc.) unless it was public health competency-focused (e.g., public health nursing). Research on public health interventions aimed at individuals, communities, groups, etc. that did not feed into knowledge about effective training or education of students and the workforce were also excluded. Finally, descriptive studies about a public health program, training opportunity, or workshop without some basis in program development theory or frameworks were excluded.

2.3 Study selection

Two researchers conducted a pilot test before screening began. Articles were screened in two stages by two independent reviewers. First, the title and abstracts of each article were screened for relevance to the inclusion criteria using a structured form. Kappa was 0.81 for this screening stage, indicating high agreement ( 30 ). All conflicts were resolved through discussion.

Next, the full text of articles found to be potentially relevant to this review were independently screened by the same reviewers. Several steps were taken to obtain the full text of all articles if they were not available through traditional means including searching the University of Guelph Library, using Google and Google Scholar, and contacting the researcher directly via ResearchGate and/or their institutional email if available. Articles were screened using a structured form that contained the following criteria: literature type, language, population, measurement, evaluation, or detailed report of competency statement or framework development in public health students and/or the workforce. Kappa was 0.80 for this stage of screening, indicating high agreement ( 30 ), and all conflicts were resolved through discussion.

2.4 Data extraction and analysis

This review focused on mapping prominent trends in developing competencies in public health, including competency frameworks and competency development in students and practitioners. The data extraction form was developed by the lead researcher, piloted by the two independent reviewers, and finalized by the research team prior to data extraction. The following fields were extracted using a structured form in DistillerSR: title, author(s), year, article type, country/region of origin, study design, study aim, methods, theories or frameworks included, pedagogy identified, institutions involved in the research, institutions involved in the professional or student training, existing competency frameworks included, focus of competency development (e.g., developing a competency framework, workforce development, student development), focus of competency framework (e.g., general or discipline-specific), target population (e.g., graduate student, general public health practitioner), focus of practitioner or student training, focus on public health communication, recommendations for improved competency development, bias identified, and future research directions.

The dataset was split in half so that the two reviewers were each the primary extractors for half and verified the other half. The recommendations for improved competency development were captured deductively based on a related review ( 24 ), and inductively to capture approaches for developing competencies in public health practitioners and students found in the included literature. The inductively captured approaches were analyzed by uploading the open text responses into NVivo Plus 12 ( 31 ) and conducting an automatic thematic analysis. Codes with multiple references were explored by the lead researcher to generate overall promising approaches for developing competencies and were verified by a second researcher and the research team.

Descriptive statistics demonstrate the trends in the collected information relevant to the review aim. Microsoft Excel (Microsoft, Redmond, USA) was used to analyze and visualize the data.

The search identified 3,716 articles after deduplication and a total of 120 articles met the inclusion criteria and underwent data extraction and analysis ( Figure 1 ). Generally, articles either described the development of competency statements and frameworks or the development of competencies through training and professional development.

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Figure 1 . PRISMA flow diagram of scoping review process.

Most included articles used mixed method designs (44%) and were conducted in the United States of America (64%) ( Table 2 ). Supplementary Table S1 provides the authors, year, article title, and study details of all the included literature ( n  = 120). General public health practitioners (48%), followed by Master of Public Health (MPH) students (22%), were the common target populations for the included articles. Other public health practitioners commonly cited (12%) include public health leaders, environmental public health practitioners, public health nurses, public health nutritionists, and Indigenous health workers. Universities (89%) conducted most of the included research, followed by collaborations between the cited institutions and organizations (e.g., universities and governments) (34%). Other organizations (9%), including research funders and networks, government-funded and/or operated training centers, and consultants also conducted the research. Existing competency frameworks were often not included (45%) in the articles, but when they were, the framework by the US Core Competencies for Public Health Professionals (35%) was the most frequently incorporated. Finally, bias was not identified (44%) in some studies; however, other studies cited participant bias (27%).

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Table 2 . Article attributes summary.

Table 3 describes the range of competency development work in public health from building competency statements and frameworks to student education and professional training. Professional development (49%) was often the focus of the included literature, followed by developing competencies in public health students (36%). Of the studies that assessed competencies, general surveys (55%) and pre- and post-surveys (33%) were employed as a step within building competency frameworks, as well as to assess self-rated competency changes after student and professional training. Universities (76%) were the institutions that most commonly conducted training in the included articles; however, collaborations (28%) between cited institutions and organizations were also common. Other organizations (9%) who participated in developing and conducting training included federally funded organizations and consultants. Of the studies that included professional development/training opportunities, some are conducted online (39%) and focused on a variety of other topics (45%) including cultural competency, health equity and literacy, and epidemiology. When student training was the focus, it was often centered within an academic course (41%) and incorporated experiential learning (40%). Articles that focused on development of competency statements and frameworks are limited (10%), but when present are focused on foundational competencies, epidemiology, leadership, and other discipline-specific areas. One article (1%) focused on refreshing an existing competency framework.

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Table 3 . Range of competency development work depicted in literature.

Evidence-based practices and recommendations for developing competency frameworks and for building competencies within public health students and practitioners are identified ( Table 4 ). Of the studies that identified evidence-based practices and approaches, the need for governance and resources to oversee competency framework development, refreshing, implementation across the workforce, and evaluation are included (18%). Multi-step and consensus-building approaches (14%) are recommended by studies that focused on building competency statements and frameworks, followed by a call for additional approaches to address complex public health issues (e.g., health inequities, Indigenous health). Experiential learning (26%), including practicum and community-based learning, was found to be best for developing competencies in public health students and practitioners. Matching training curricula to competencies (23%) was also found to be an effective practice for developing competencies in public health students and practitioners.

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Table 4 . Evidence-based practices and recommendations for developing competencies in public health.

4 Discussion

Effective public health practice requires a skilled interdisciplinary public health workforce. Competencies describe the integrated knowledge, values, skills, and behaviors required for practitioners and organizations across the practice of public health ( 1 , 8 , 10 , 16 , 32 ). Competency frameworks allow for workforce planning and development, including identifying gaps in training, evaluating performance, and developing professional development opportunities ( 32 ). Key components in developing the public health workforce include developing competency-based curricula and a comprehensive and coordinated lifelong learning system ( 33 ). Understanding successes, shortfalls, and lessons learned from previous literature on competency development is important for developing a strong public health workforce going forward. However, the lack of consistency in methods across the body of literature on this topic, the focus on foundational competencies and general practitioners, along with limited reporting on best practices, may make it challenging for public health researchers and organizations to develop competencies effectively and efficiently within the workforce.

The included literature was divided into literature that describes how competency statements and frameworks have been developed and refreshed ( n  = 13, 11%) and literature describing how to develop competencies within public health students and practitioners ( n  = 107, 89%). Studies were most often mixed methods research conducted in the United States by academia and spanned various target populations within public health students and professionals. Most of the included literature focused on building competencies through student and professional development, with the former most often offered through academic courses and the latter most often offered online. Surveys, including pre- and post-surveys, are used for self-assessment of competency development among students and practitioners. The included literature had a limited focus on approaches and best practices for developing competency statements and frameworks. Within the relevant literature, experiential and adult learning were the most applied pedagogical approaches.

4.1 Competency literature focused on a general public health audience and foundational competencies

The focus on foundational and core competencies is necessary to provide the cross-cutting knowledge, values, and skills integrated by a practitioner to perform broad functions across public health ( 34 ). These studies provide the basis by which practitioners and students can engage in critical thinking and reflection and allow for the development of more advanced and discipline-specific knowledge, values, skills, and behaviors ( 35 ). Thus, as more is understood about developing core competency frameworks and foundational competencies across the workforce, discipline-specific competency frameworks and training programs that support depth in knowledge, skills, and behaviors can be built more efficiently.

Specific public health audiences varied across the included literature, with the majority focusing on general public health practitioners. The public health workforce is comprised of practitioners who engage in public health work as the primary part of their role ( 33 ). Within the public health workforce, there are diverse disciplines and professional backgrounds that require unifying competencies and professional development to ensure optimal individual and organizational performance ( 14 ). A focus on general public health practitioners provides the evidence-base required for understanding and applying best practices for workforce development across the public health system.

Similarly, most of the literature focused on approaches for developing foundational or core competency frameworks, and professional development across foundational competencies. There was an overall lesser focus on professional development on the topics of cultural competency, evidence-informed decision-making, crisis and risk communication, epidemiology, and public health leadership. However, discipline-specific competencies, coupled with foundational competencies are necessary to address complex and multifaceted public health issues including social determinants of health, health equity, and climate change ( 36 , 37 ).

4.2 Gaps exist in competency research including a limited focus on discipline-specific competency research

The modest amount of discipline-specific competency research uncovered in the current review was mainly in the realm of professional development, including a focus on health equity, health literacy, epidemiology, evidence-informed decision-making, crisis and risk communication, and cultural competency. System-level improvement requires developing and maintaining a trained public health workforce. Key aspects of providing effective professional development include mapping competencies to curricula and understanding training gaps in the workforce ( 38 ). A recent study identified communication, change management, budgeting, and reporting as gaps in the U.S. public health workforce ( 39 ). Similarly, communication was identified as a gap among Israeli public health practitioners, along with responding to current and emerging public health issues ( 38 ). However, studies examining competency gaps in public health practitioners at the country level are limited. An understanding of the strengths and gaps in public health competencies both today and in the future is required to be able to map professional development opportunities, such as in public health communication, to strengthen the workforce.

Further, few included studies focused on discipline-specific competency statements and framework development. Both foundational and discipline-specific competencies are necessary to provide the cross-cutting knowledge, values, skills, and behaviors necessary for core public health functions. Public health encompasses a range of disciplines and backgrounds and requires not only a grounding foundational set of competencies, but also discipline-specific competencies used by public health specialists ( 12 ). Key public health disciplines requiring a specialized competency framework through which the foundational competencies are integrated include epidemiology, infectious disease, global health, nursing, communication, and others ( 40 ). Additional literature on developing discipline-specific competency frameworks, including health communication competencies, and competency-based training would add to our understanding of additional professional development and workforce planning requirements across the public health workforce.

Within the literature on developing competency statements and frameworks, gaps were also identified related to the trustworthiness of the research. More than half the included literature emphasized the need for validated instruments to measure change in competencies as a result of education or professional development to increase the trustworthiness of research. Further, studies reporting on competency statements and framework development should aim to increase their transparency to allow for replication and the ability to better contextualize and understand the results. Recent recommendations for reporting competency framework development in health professions (CONFRED-HP) echo this as demonstrated by the inclusion of a number of key items related to transparency in their checklist ( 41 ). Finally, evaluation of the utility and impact of competency frameworks is lacking and should be undertaken to better understand how to develop frameworks that meet the needs of public health organizations. Very few evaluations of public health competency frameworks exist, but those that do provide an important contribution to related literature and workforce development ( 42 ). Evaluation and implementation are also areas of focus within the CONFRED-HP checklist to support this key recommendation ( 41 ).

4.3 Evidence-based practices and approaches for competency development point toward a need for resources, governance, and real-world application

Several best practices and approaches for public health competency development were found in the included literature. The need for resources and governance to oversee competency framework development, revitalization, and workforce planning was commonly identified. Governance of competency frameworks by federal governments is needed to provide oversight and resources for public health workforce planning and development and to ensure equal access to an effective workforce for all communities. In Canada, the Canadian Public Health Association and others have called on the federal government to create a new Public Health Act that supports public health, oversees competency framework development and implementation, and provides resources to the system ( 14 , 43 ). In the United States, the Council on Linkages between Academia and Public Health Practice is comprised of 24 national organizations that collaborate to support public health competence development in the workforce ( 44 ). Governance ensures competency frameworks stay up to date, are incorporated into practice, are adequately financed, and are mapped to professional development opportunities. Ensuring the public health workforce has modern competencies to fulfill its functions requires dedicated resources and an interdisciplinary and collaborative governance structure to oversee the renewal, implementation, and evaluation of competency frameworks.

Multi-step and consensus-building approaches for developing competency statements and frameworks in public health are identified as best practices by all the related included literature. The Delphi technique is a method often used for competency framework development to gain consensus from a group of experts ( 45 ). Delphi techniques contain a number of rounds that may include expert consultation, surveys, interviews, and focus groups, depending on the literature in the area and the number of rounds required for consensus or agreement. There are no specific recommendations on the number of rounds or the order of methods included in Delphi technique; however, it is clear that a multi-step process that builds consensus is key to ensure rigor ( 45 , 46 ).

Within competency development for public health students and practitioners, experiential learning and curriculum-based training were commonly identified as evidence-based practices. Experiential learning provides real-world public health experiences as a means of developing competencies beyond the classroom and is often incorporated in practicums, capstone courses, and sometimes within individual courses and assessments ( 47 , 48 ). Experiential learning centers practical experiences within the education, allowing for the integration of knowledge, values, skills, and behaviors ( 47 ). Further, competency-based training embeds competencies within the curricula, assessments, and structure of the training opportunity. Competency-based education and training complements experiential learning through orienting the application of knowledge, values, skills, and behaviors in the real world ( 49 ). Importantly, it also accelerates graduate’s to job readiness, thereby enhancing public health capacity. The focus on the real-world application of competency-based education and training and experiential learning bridges the gap between the public health workplace, evidence, and the education or training setting ( 49 ).

4.4 Limitations and future research

With regards to the included literature, selection bias and generalizability of results were identified within the included literature as the most common biases. These biases can threaten the validity of the research and impact the generalizability of findings.

Within the methods used to conduct the scoping review, limitations also exist including the selection of databases. Language bias is also present as included articles needed to be in English.

Future research using robust mixed methods to assess how competencies are developed and the best instrument to measure changes in competencies reliably and efficiently is needed. Additional research on developing, implementing, and evaluating discipline-specific competency frameworks is also needed. With regards to experiential learning, additional evaluation of various opportunities in public health should be conducted to increase the validity of findings and contribute to an increased understanding of best practices.

5 Conclusion

This scoping review highlights the importance of competency frameworks and professional development in improving public health practice and developing a skilled, interdisciplinary public health workforce. Competency frameworks are essential tools for workforce planning and development, including identifying training gaps, evaluating performance, and creating professional development opportunities. Much of the included literature focused on foundational competency framework development and professional development for general public health practitioners. While this provides an important broad understanding of the essential competencies needed, there is a gap in discipline-specific research. Public health is a diverse field comprising a range of disciplines that require competency development and frameworks for specialized areas of practice. This review also highlights several best practices for competency development, including the importance of governance and resources to oversee competency framework development and workforce planning, as well as experiential learning and competency-based training for students and practitioners.

Author contributions

MM: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. CF: Writing – review & editing, Formal analysis, Investigation, Validation. LG: Funding acquisition, Validation, Writing – review & editing. AP: Conceptualization, Funding acquisition, Validation, Writing – review & editing. JM: Writing – review & editing, Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Funding for this research was provided by the Canadian Institutes for Health Research (CIHR) in the form of a CIHR Catalyst Grant (FRN 184647).

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

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2024.1332412/full#supplementary-material

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Keywords: public health, competencies, competency frameworks, professional development, competency-based education, pedagogy, training

Citation: MacKay M, Ford C, Grant LE, Papadopoulos A and McWhirter JE (2024) Developing competencies in public health: a scoping review of the literature on developing competency frameworks and student and workforce development. Front. Public Health . 12:1332412. doi: 10.3389/fpubh.2024.1332412

Received: 02 November 2023; Accepted: 19 February 2024; Published: 04 March 2024.

Reviewed by:

Copyright © 2024 MacKay, Ford, Grant, Papadopoulos and McWhirter. 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: Jennifer E. McWhirter, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

  • Open access
  • Published: 01 March 2024

Application of practice-based learning and improvement in standardized training of general practitioners

  • Bin Yang 1 , 2  

BMC Medical Education volume  24 , Article number:  214 ( 2024 ) Cite this article

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In the context of standardized training for general practitioners, the emphasis is still primarily on clinical skills, which does not fully encompass the overall development of general practitioners. This study implemented a practice-based learning and improvement (PBLI) project among students and evaluated its effectiveness based on indicators such as learning outcomes, students’ subjective experiences, and annual grades. This study offers recommendations for optimizing general practitioners’ teaching and residential training programs.

60 residents who participated in the regular training of general practitioners at the First Clinical College of Tongji Medical College of Huazhong University of Science and Technology from January 2019 to January 2022 were selected for this study. They were randomly divided into two groups, the PBLI group, and the control group, using a random number table method. Out of the 60 residents, 31 were assigned to the control group and 29 were assigned to the PBLI group. The participants in the PBLI group received additional PBLI training along with their daily residential training, while the participants in the control group only took part in the latter. The effectiveness of the PBLI program was analyzed by conducting a baseline survey, administering questionnaires, and evaluating examination results.

After implementing the program, the PBLI group scored significantly higher than the control group ( p  < 0.05). Throughout the implementation process, students in the PBLI group expressed high satisfaction with the learning project, particularly with its content and alignment with the training objective. The teacher’s evaluation of the PBLI group students surpassed that of the control group in various areas, including literature retrieval, self-study, courseware development, speech ability, and clinical thinking.

Conclusions

The PBLI program aims to encourage resident-centered study in standardized residency training. This approach is beneficial because it motivates students to engage in active learning and self-reflection, ultimately enhancing the effectiveness of standardized residency training.

Peer Review reports

A family doctor is a specialist who provides comprehensive and continuous medical care to individuals of all ages, genders, and conditions [ 1 , 2 , 3 ]. Internationally, primary health care systems rely on general practitioners to act as ‘gatekeepers’ and handle 90–95% of patients’ complaints on a long-term basis [ 4 , 5 ]. This highlights the crucial need for well-trained general practitioners. In China, as the healthcare service model undergoes transformation, general practitioners are assuming an increasingly important role in basic healthcare services [ 6 ]. The objective of China’s healthcare reform is to establish an efficient hierarchical diagnosis and treatment system, with a focus on strengthening the role of primary healthcare organizations and general practitioners as the ‘gatekeepers’ of health [ 7 , 8 ]. There are various types of general-practice residential training teaching models in China. However, most of these models are based on the traditional approach of transferring theoretical knowledge and lack the cultivation of comprehensive abilities such as medical humanities, teamwork, and evidence-based medical concepts. Practice-based learning and improvement (PBLI) is considered a crucial competency throughout a physician’s career, as identified by the Accreditation Council for Graduate Medical Education (ACGME) [ 9 ]. While many residency training programs have started recognizing the importance of teaching PBLI programs to residents, there is a lack of studies on PBLI programs specifically designed for general practice residents. This study aims to address this gap by developing a primary care PBLI project that aligns with the existing teaching model and fulfills the requirements of PBLI, which are essential competencies for residents. The project will evaluate the impact of the primary care PBLI project on enhancing physicians’ comprehensive abilities in a general hospital setting.

General information

This study included residency trainees in general practice who underwent standardized training at our general practice specialty base between January 2019 and January 2022. The inclusion criteria were as follows: completion of at least 1 year of residency training, participation in the practice competency-based learning and improvement program in the second year, willingness to cooperate with the study, and provision of informed consent. Trainees who were unable to actively cooperate with the Practice-Based Competency Learning and Improvement Program were excluded. Sixty trainees were enrolled in the study, with 30 trainees enrolled in 2019 and 30 trainees enrolled in 2020. The enrolled trainees were randomly divided into two groups: the PBLI group and the control group. The control group consisted of 31 trainees, with an average age of 30.45 ± 3.07 years. Among them, there were 19 males and 12 females, and 23 trainees passed the Licensing Examination (LE) at the end of the first year of residency training. The PBLI group consisted of 29 trainees, with an average age of 29.55 ± 3.21 years. Among them, there were 15 males and 14 females, and 21 trainees passed the Licensing Examination (LE) at the end of the first year of training. All of the enrolled trainees were socialized trainees without work units or selected trainees from outside units, and they all had a bachelor’s degree. There were no statistically significant differences between the two groups in terms of gender, age, or passing rate of the first year’s medical Licensing Examination (LE) ( P  > 0.05). See Table  1 .

Intervention

The general practice residency trainees were categorized based on the residency syllabus requirements. This study focused on the second year of trainee residency training. Trainees in the control group underwent daily general practice residency training. In contrast, trainees in the PBLI group, who were also in their second year of training, participated in an additional learning and improvement program based on practice competence. This program was implemented alongside the regular daily general practice residency training without affecting the original activities, such as difficult case discussions, mini-lectures, teaching rounds, and teaching in the outpatient clinic.

The program followed the ADDIE model (Analysis, Design, Develop, Implement, Evaluate) [ 10 ]. In the second year of training, all trainees have become familiar with the training site and determined their learning goals. Conceive and implement PBLI projects through interactive and participatory workshops. Participants in the PBLI group were allowed to form multiple study groups consisting of 3 to 4 participants. Each group was supervised by a general practice faculty member. In accordance with the requirements of the general practice residency training syllabus and in conjunction with their work in residency training bases and primary practice bases, participants reviewed the information on their own. The project settings varied according to different outpatient and inpatient settings (Table  2 ). Several interactive seminars were organized by the base faculty. The seminars served as a platform for discussions, where the results of the discussions were analyzed to determine the PBLI learning theme. The chosen theme should reflect the characteristics of general medicine and address the necessary and weak learning content of general medicine at present. The general practice faculty played a supportive role throughout the process, ensuring that the trainees’ ideas were appropriate and feasible and that the theme’s content aligned with the actual work of general practitioners. The PBLI theme should be focused on enhancing the competence of general practitioners. Once the theme is selected, a corresponding learning plan should be designed and submitted to the instructor for review. The project lasted for 9 months.

PBLI project implementation method:

Step 1, PBLI project design and guidance. For example, if a trainee notices a high number of patients with metabolic syndrome in their primary practice site, they may choose to address the challenge of managing these patients. After group discussions and consultation with the instructor, the trainee decides to take on the theme of ‘Community Management of Metabolic Syndrome’ for their PBLI project and creates an implementation plan.

Step 2, all students form a team of 3 to 4 participants and complete at least one Plan-Do-Study-Act (PDSA) cycle as a group, which takes 2 to 3 months. Throughout the project, the group regularly reports progress to their assigned mentor. The first step involves conducting a community survey to determine the epidemiological status of metabolic syndrome in the local population. The second step includes addressing relevant questions such as the diagnostic criteria for metabolic syndrome, goals for blood pressure, blood glucose, blood lipids, and weight management, as well as evidence-based and guideline recommendations. ① Plan: General practitioners announce the primary care PBLI project, and the research team selects one or uses a self-designed case as the report title. ② Do: Based on the selected PBLI project, review information, discuss in groups, and complete a reading report. The content includes the current status of case community management, the latest research results, development trends, existing deficiencies and suggestions for improvement, and the results of the group discussion will be reported at the book report meeting. ③ Study: Accept feedback and evaluation from different groups of research subjects and instructors, self-reflect, understand deficiencies and guide self-learning. ④ Act: Modify the plan and put forward suggestions for improvement. Enter the next PDSA cycle. Completion of at least 2 PDSA cycles is the requirement for the end of this training. The PBLI project has a set timeframe, and participants regularly report their progress to the instructor. At the end of the project, the group of trainees presents a completion report to all general practice training trainees and faculty. The new program is then implemented, and feedback and evaluation are provided.

Step 3: Evaluation of PBLI project training effects. The implementation effects of the PBLI project were evaluated using three dimensions: satisfaction evaluation, objective results, and teacher evaluation.

Data collection

The Plan-Do-Study-Act (PDSA) model is used as the learning framework for PBLI. After completing the PBLI project in the second grade, we evaluated the implementation effect using the self-made questionnaires ‘Resident PBLI Ability Training Satisfaction Questionnaire’ and ‘Teacher Satisfaction Evaluation Questionnaire’.

General practitioners’ satisfaction evaluation: The Resident PBLI Ability Training Satisfaction Questionnaire was developed based on the PBLI competency training questionnaire from the University of Wisconsin School of Medicine and Public Health. The overall Cronbach’s alpha coefficient of the Index System was 0.982 [ 16 ]. A total of 342 questionnaires were distributed, and 319 valid questionnaires were collected, resulting in an effective recovery rate of 93.27%. All items in the evaluation form were rated on a 5-point Likert scale and were completed promptly after the program concluded. The questionnaire included items assessing satisfaction with the content, format, effectiveness, alignment with residency training objectives, and overall satisfaction with the PBLI program. Participants can rate their satisfaction on a scale of 1 to 5, with 1 indicating very dissatisfied, 2 indicating moderately dissatisfied, 3 indicating generally satisfied, 4 indicating moderately satisfied, and 5 indicating very satisfied.

Evaluation of all trainees by general practitioners: The Teachers Satisfaction Evaluation Questionnaire is a self-designed questionnaire. A total of 231 questionnaires were distributed, out of which 212 valid questionnaires were collected, resulting in an effective recovery rate of 96.10%. All assessments were performed using a 5-level Likert scoring method. The evaluation encompassed trainees’ competence in “literature research”, “case analysis ability”, “independent learning”, “courseware production skills”, “speaking ability”, and “clinical thinking ability”. Each question offered 5 options:‘very good’, ‘good’, ‘average’, ‘poor’, and ‘very poor’. Any result that could be classified as “very good” or “good” was considered as being part of the satisfaction result, and any other result was rated as a nonsatisfaction result (any score of “average”, “poor” or “very poor”).

The annual appraisal performance of the two groups of trainees in general practice specialty residency training was compared.

Data analysis

Data analysis was conducted using SPSS 27.0 statistical software. The count data are presented as frequencies and percentages, while the measurement data that followed a normal distribution are expressed as the mean ± SD ( \(\bar x\, \pm \,s\) ). The chi-square test and t test were used to analyze the count and measurement data, respectively. P <0.05 was considered statistically significant.

Ethical considerations

This study received approval from the Medical Ethics Committee of Union Hospital, Wuhan, China. Informed consent was obtained from all participants involved in the study. All methods were conducted in strict adherence to the applicable guidelines and regulations.

Self-assessment by participants in the PBLI group

A total of 29 trainees participated in the PBLI group, and 11 PBLI projects were finalized. A total of 319 valid questionnaires were received to evaluate the impact of the PBLI project implementation. Relative to the comparison group, the residents in the PBLI curriculum demonstrated a significant increase in the content of the project with a mean score of 4.64 ± 0.79, the project’s format with a mean score of 3.68 ± 0.81, and the effectiveness of the conducted project with a mean score of 3.95 ± 0.68. They also reported satisfaction with the alignment between the PBLI program and the objectives of residency training with a mean score of 4.35 ± 0.86, as well as overall satisfaction with the PBLI program with a mean score of 4.16 ± 0.78 (Table  3 ).

Instructor evaluation of the two groups of participants

The study included 60 participants in both groups, with each participant being evaluated by more than three instructors. A total of 212 valid evaluation surveys were collected, consisting of 102 evaluations for the trainees in the PBLI group and 110 evaluations for the trainees in the control group. The questionnaire survey revealed that the trainees in the PBLI group exhibited significantly better skills than the control group in terms of “literature searching skills” (LSS), “independent learning” (IL), “courseware production skills” (CPS), “speaking ability” (SA), and “clinical thinking ability” (CA), and the difference was significant ( P  < 0.05). However, there was no significant difference in “case analysis ability” (CAA) between the two groups (Table  4 ).

Comparison of annual achievements of the two groups of participants

The PBLI program was completed during the second year of the general practice residency, and an annual assessment was conducted at the end of the year. Comparing the scores from the annual assessment, it was found that the PBLI group scored higher than the control group [(120.35 ± 8.98) points vs. (84.97 ± 12.26) points]. The difference between the two groups was statistically significant ( t  = 12.84, P  < 0.001).

The quantity, quality, and service level of general practitioners have a direct impact on the quality of healthcare services in China [ 11 ]. Even though the utilization rate of family doctors in China is only 6.9% [ 12 ], a significant number of residents are in a state of being ‘signed but not contracted’. The main reason for the low rate of effective signing is that primary general practitioners (family doctors) lack the necessary skills to attract residents to prefer primary health care institutions. This study focuses on a problem-oriented and patient-centered primary care PBLI project for general practice residents. The project aims to enhance medical safety, promote health, and improve the job competency of general practice residents. Additionally, it aims to enhance the ability of residents to continuously learn and develop their skills in practice. The project involves various activities such as literature search, independent learning, cultural exchange, and teaching general practice-related knowledge. By enhancing the general medical clinical response ability of general practitioners, the project also fosters the development of self-reflection and independent learning abilities in general practitioners [ 13 , 14 ]. PBLI is one of the six core competency standards for US residents, developed by the ACGME and other organizations [ 9 ]. ACGME subdivides competence into six core competencies: (1) Medical Knowledge; (2) Patient Care; (3) Communication Skills; (4) Practice-based Learning and Improvement; (5) Systems based Practice; (6) Professionalism. This study demonstrates that the PBLI program enhances trainees’ abilities in various aspects of self-learning and self-improvement. These include literature searching ability, independent learning ability, courseware production ability, presentation ability, and embodiment of clinical thinking. Trainees who have participated in the PBLI program maintain their interest in learning for a longer period compared to other trainees [ 15 , 16 ]. The New Century Medical and Health Talent Cultivation Report, published by the 21st century, emphasizes that the core focus of the medical education model based on job competency is to enhance learning ability and develop problem-solving skills through the application of medical knowledge. The standardized training of residents in the United States focuses on student-centered teaching, which requires clinical teachers to tailor their instruction to meet the needs of students and guide their career development. In contrast, the standardized training of residents in China primarily emphasizes the evaluation of teachers’ teaching and professional abilities, with less attention given to the individual needs of students. This project helps to remedy this gap in GP training.

This study conducted 11 PBL projects and found that the residency trainees were highly satisfied with the format and content of the program. They believed that the implementation of the PBLI program helped them achieve the goals of general practice residency training and expressed their willingness to continue participating in its activities. Feedback from the instructors indicated that the PBLI program enhanced the multifaceted competencies of the residency trainees and addressed gaps in current clinical teaching methods for general practice. The PBLI project aims to transform the teaching mode from traditional preaching training to a practice-based approach. It utilizes the Plan-Do-Study-Act (PDSA) model as a systematic experimental learning framework to enhance the practical abilities of general practitioners. The objective evaluation primarily focuses on the annual performance of all students. Following the training, the annual assessment scores of all trainees were higher compared to those of the control group. This suggests that the PBLI training program can effectively enhance the clinical diagnosis and treatment abilities and performance of general trainees. These findings align with Xu Jiao ’s research, which concluded that innovative models can improve the clinical diagnosis and treatment capabilities of general medicine residents through self-evaluation and objective testing [ 17 ]. The project aims to shift the training mode for recent graduates from a biomedical model to a comprehensive understanding of diseases from biological, psychological, and social perspectives. This transformation aims to improve the post-competency training of general practitioners. This study examines the impact of the PBLI project on the comprehensive abilities of all students, taking into account teacher evaluation. “Literature searching skills”, “case analysis ability” and “independent learning” are important components of the general practitioner’s job competence [ 18 ]. The results indicate that participation in the PBLI project leads to improvements in students’ abilities in literature retrieval, independent learning, courseware production, and speaking. These findings highlight the advantages of different traditional teaching models and demonstrate successful teaching outcomes. This aligns with previous research conducted by Zheng Shuping on the innovative TEST teaching model, which is based on problem-based learning, group teaching, and other teaching models [ 19 ]. It can be seen that the PBLI program is closely integrated with the actual content of primary health services, focuses on solving the difficulties in actual work, and provides a method of self-learning and self-experience for general practice residency trainees, which is conducive to the improvement of the competency of general practice positions.

The subjective survey results of this study indicate that there was no significant improvement in ‘case analysis ability’ after training. The analysis suggests that this may be due to limited opportunities for general trainees to handle complex cases and inadequate training. The patients they encountered were non-standardized, and the resident doctors lacked experience in managing such cases. In Zhou Lin’s study [ 20 ], the use of standardized patients proved to be more effective in enhancing the clinical diagnosis and treatment abilities of general practitioners in training, particularly in doctor-patient communication skills. To address this issue, it is recommended to select more typical cases for study during the implementation of the PBLI project. Additionally, increasing the exposure of general trainees to standardized patients is necessary to improve their doctor-patient communication skills.

However, it is important to note some limitations of this study. It was conducted in a single center and included only 60 standardized training students of general practice residents, resulting in a small sample size and limited generalizability of the conclusions. In future research, efforts will be made to enhance and promote the development of PBLI projects, improve teacher training, and allocate more spare time for students to participate in PBLI projects. Furthermore, larger-scale research involving multiple centers will be conducted.

This document discusses the application value of PBLI in standardized training for general practitioners. Students have the freedom to choose their own PBLI topics, search for information, and report on them, which stimulates their enthusiasm for self-directed learning. The results indicate that students who have undergone PBLI training maintain a longer interest in learning compared to other students. The PBLI group also showed higher satisfaction in terms of their ability to retrieve literature, engage in self-learning, produce courseware, express themselves in speech, and think clinically. Feedback from the guidance teacher indicates that the PBLI project has helped develop diverse abilities in residential trainees, which addresses the current shortcomings in general clinical teaching. The study concludes that the implementation of PBLI promotes student-centered learning and improves the effectiveness of resident physician training.

Data availability

The datasets analyzed in the current study are available from the corresponding author on reasonable request.

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Yang, B. Application of practice-based learning and improvement in standardized training of general practitioners. BMC Med Educ 24 , 214 (2024). https://doi.org/10.1186/s12909-024-05195-7

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The Importance of Access to Comprehensive Sex Education

Comprehensive sex education is a critical component of sexual and reproductive health care.

Developing a healthy sexuality is a core developmental milestone for child and adolescent health.

Youth need developmentally appropriate information about their sexuality and how it relates to their bodies, community, culture, society, mental health, and relationships with family, peers, and romantic partners.

AAP supports broad access to comprehensive sex education, wherein all children and adolescents have access to developmentally appropriate, evidence-based education that provides the knowledge they need to:

  • Develop a safe and positive view of sexuality.
  • Build healthy relationships.
  • Make informed, safe, positive choices about their sexuality and sexual health.

Comprehensive sex education involves teaching about all aspects of human sexuality, including:

  • Cyber solicitation/bullying.
  • Healthy sexual development.
  • Body image.
  • Sexual orientation.
  • Gender identity.
  • Pleasure from sex.
  • Sexual abuse.
  • Sexual behavior.
  • Sexual reproduction.
  • Sexually transmitted infections (STIs).
  • Abstinence.
  • Contraception.
  • Interpersonal relationships.
  • Reproductive coercion.
  • Reproductive rights.
  • Reproductive responsibilities.

Comprehensive sex education programs have several common elements:

  • Utilize evidence-based, medically accurate curriculum that can be adapted for youth with disabilities.
  • Employ developmentally appropriate information, learning strategies, teaching methods, and materials.
  • Human development , including anatomy, puberty, body image, sexual orientation, and gender identity.
  • Relationships , including families, peers, dating, marriage, and raising children.
  • Personal skills , including values, decision making, communication, assertiveness, negotiation, and help-seeking.
  • Sexual behavior , including abstinence, masturbation, shared sexual behavior, pleasure from esx, and sexual dysfunction across the lifespan.
  • Sexual health , including contraception, pregnancy, prenatal care, abortion, STIs, HIV and AIDS, sexual abuse, assault, and violence.
  • Society and culture , including gender roles, diversity, and the intersection of sexuality and the law, religion, media, and the arts.
  • Create an opportunity for youth to question, explore, and assess both personal and societal attitudes around gender and sexuality.
  • Focus on personal practices, skills, and behaviors for healthy relationships, including an explicit focus on communication, consent, refusal skills/accepting rejection, violence prevention, personal safety, decision making, and bystander intervention.
  • Help youth exercise responsibility in sexual relationships.
  • Include information on how to come forward if a student is being sexually abused.
  • Address education from a trauma-informed, culturally responsive approach that bridges mental, emotional, and relational health.

Comprehensive sex education should occur across the developmental spectrum, beginning at early ages and continuing throughout childhood and adolescence :

  • Sex education is most effective when it begins before the initiation of sexual activity.
  • Young children can understand concepts related to bodies, gender, and relationships.
  • Sex education programs should build an early foundation and scaffold learning with developmentally appropriate content across grade levels.
  • AAP Policy outlines considerations for providing developmentally appropriate sex education throughout early childhood, middle childhood, adolescence, and young adulthood.

Most adolescents report receiving some type of formal sex education before age 18. While sex education is typically associated with schools, comprehensive sex education can be delivered in several complementary settings:

  • Schools can implement comprehensive sex education curriculum across all grade levels
  • The Sexuality Information and Education Council of the United States (SIECUS) provides guidelines for providing developmentally appropriate comprehensive sex education across grades K-12.
  • Pediatric health clinicians and other health care providers are uniquely positioned to provide longitudinal sex education to children, adolescents, and young adults.
  • Bright Futures: Guidelines for Health Supervision of Infants, Children, and Adolescents outlines clinical considerations for providing comprehensive sex education at all developmental stages, as a part of preventive health care.
  • Research suggests that community-based organizations should be included as a source for comprehensive sexual health promotion.
  • Faith-based communities have developed sex education curricula for their congregations or local chapters that emphasize the moral and ethical aspects of sexuality and decision-making.
  • Parents and caregivers can serve as the primary sex educators for their children, by teaching fundamental lessons about bodies, development, gender, and relationships.
  • Many factors impact the sex education that youth receive at home, including parent/caregiver knowledge, skills, comfort, culture, beliefs, and social norms.
  • Virtual sex education can take away feelings of embarrassment or stigma and can allow for more youth to access high quality sex education.

Comprehensive sex education provides children and adolescents with the information that they need to:

  • Understand their body, gender identity, and sexuality.
  • Build and maintain healthy and safe relationships.
  • Engage in healthy communication and decision-making around sex.
  • Practice healthy sexual behavior.
  • Understand and access care to support their sexual and reproductive health.

Comprehensive sex education programs have demonstrated success in reducing rates of sexual activity, sexual risk behaviors, STIs, and adolescent pregnancy and delaying sexual activity. Many systematic reviews of the literature have indicated that comprehensive sex education promotes healthy sexual behaviors:

  • Reduced sexual activity.
  • Reduced number of sexual partners.
  • Reduced frequency of unprotected sex.
  • Increased condom use.
  • Increased contraceptive use.

However, comprehensive sex education curriculum goes beyond risk-reduction, by covering a broader range of content that has been shown to support social-emotional learning, positive communication skills, and development of healthy relationships.

A 2021 review of the literature found that comprehensive sex education programs that use a positive, affirming, and inclusive approach to human sexuality are associated with concrete benefits across 5 key domains:

Benefits of comprehensive sex education programs 

Benefits of Comprehensive sex education programs.jpg

When children and adolescents lack access to comprehensive sex education, they do not get the information they need to make informed, healthy decisions about their lives, relationships, and behaviors.

Several trends in sexual health in the US highlight the need for comprehensive sex education for all youth.

Education about condom and contraceptive use is needed:

  • 55% of US high school students report having sexual intercourse by age 18 .
  • Self-reported condom use has decreased significantly among high school students.
  • Only 9% of sexually active high school students report using both a condom for STI-prevention and a more effective form of birth control to prevent pregnancy .

STI prevention is needed:

  • Adolescents and young adults are disproportionately impacted by STIs.
  • Cases of chlamydia, gonorrhea, and syphilis are rising rapidly among young people.
  • When left untreated , these infections can lead to infertility, adverse pregnancy and birth outcomes, and increased risk of acquiring new STIs.
  • Youth need comprehensive, unbiased information about STI prevention, including human papillomavirus (HPV) .

Continued prevention of unintended pregnancy is needed:

  • Overall US birth rates among adolescent mothers have declined over the last 3 decades.
  • There are significant geographic disparities in adolescent pregnancy rates, with higher rates of pregnancy in rural counties and in southern and southwestern states.
  • Social drivers of health and systemic inequities have caused racial and ethnic disparities in adolescent pregnancy rates.
  • Eliminating disparities in adolescent pregnancy and birth rates can increase health equity, improve health and life outcomes, and reduce the economic impact of adolescent parenting.

Misinformation about sexual health is easily available online:

  • Internet use is nearly universal among US children and adolescents.
  • Adolescents report seeking sexual health information online .
  • Sexual health websites that adolescents visit can contain inaccurate information .

Prevention of sex abuse, dating violence, and unhealthy relationships is needed:

  • Child sexual abuse is common: 25% of girls and 8% of boys experience sexual abuse during childhood .
  • Youth who experience sexual abuse have long-term impacts on their physical, mental, and behavioral health.
  • 1 in 11 female and 1 in 14 male students report physical DV in the last year .
  • 1 in 8 female and 1 in 26 male students report sexual DV in the last year .
  • Youth who experience DV have higher rates of anxiety, depression, substance use, antisocial behaviors, and suicide risk.

The quality and content of sex education in US schools varies widely.

There is significant variation in the quality of sex education taught in US schools, leading to disparities in attitudes, health information, and outcomes. The majority of sex education programs in the US tend to focus on public health goals of decreasing unintended pregnancies and preventing STIs, via individual behavior change.

There are three primary categories of sex educational programs taught in the US :

  • Abstinence-only education , which teaches that abstinence is expected until marriage and typically excludes information around the utility of contraception or condoms to prevent pregnancy and STIs.
  • Abstinence-plus education , which promotes abstinence but includes information on contraception and condoms.
  • Comprehensive sex education , which provides medically accurate, age-appropriate information around development, sexual behavior (including abstinence), healthy relationships, life and communication skills, sexual orientation, and gender identity.

State laws impact the curriculum covered in sex education programs. According to a report from the Guttmacher Institute :

  • 26 US states and Washington DC mandate sex education and HIV education.
  • 18 states require that sex education content be medically accurate.
  • 39 states require that sex education programs provide information on abstinence.
  • 20 states require that sex education programs provide information on contraception.

US states have varying requirements on sex education content related to sexual orientation :

  • 10 states require sex education curriculum to include affirming content on LGBTQ2S+ identities or discussion of sexual health for youth who are LGBTQ2S+.
  • 7 states have sex education curricular requirements that discriminate against individuals who are LGBTQ2S+.Youth who live in these states may face additional barriers to accessing sexual health information.

Abstinence-only sex education programs do not meet the needs of children and adolescents.

While abstinence is 100% effective in preventing pregnancy and STIs, research has conclusively shown that abstinence-only sex education programs do not support healthy sexual development in youth.

Abstinence-only programs are ineffective in reaching their stated goals, as evidenced by the data below:

  • Abstinence-only programs are unsuccessful in delaying sex until marriage .
  • Abstinence-only sex education programs do not impact the rates of pregnancy, STIs, or HIV in adolescents .
  • Youth who take a “virginity pledge” as part of abstinence-only education programs have the same rates of premarital sex as their peers who do not take pledges, but are less likely to use contraceptives .
  • US states that emphasize abstinence-only education have higher rates of adolescent pregnancy and birth .

Abstinence-only programs can harm the healthy sexual and mental development of youth by:

  • Withholding information or providing inaccurate information about sexuality and sexual behavior .
  • Contributing to fear, shame, and stigma around sexual behaviors .
  • Not sharing information on contraception and barrier protection or overstating the risks of contraception .
  • Utilizing heteronormative framing and stigma or discrimination against students who are LGBTQ2S+ .
  • Reinforcing harmful gender stereotypes .
  • Ignoring the needs of youth who are already sexually active by withholding education around contraception and STI prevention.

Abstinence-plus sex education programs focus solely on decreasing unintended pregnancy and STIs.

Abstinence-plus sex education programs promote abstinence until marriage. However, these programs also provide information on contraception and condom use to prevent unintended pregnancy and STIs.

Research has demonstrated that abstinence-plus programs have an impact on sexual behavior and safety, including:

  • HIV prevention.
  • Increase in condom use .
  • Reduction in number of sexual partners .
  • Delay in initiation of sexual behavior .

While these programs add another layer of education, they do not address the broader spectrum of sexuality, gender identity, and relationship skills, thus withholding critical information and skill-building that can impact healthy sexual development.

AAP and other national medical and public health associations support comprehensive sex education for youth.

Given the evidence outlined above, AAP and other national medical organizations oppose abstinence-only education and endorse comprehensive sex education that includes both abstinence promotion and provision of accurate information about contraception, STIs, and sexuality.

National medical and public health organizations supporting comprehensive sex education include:

  • American Academy of Pediatrics .
  • American Academy of Family Physicians.
  • American College of Obstetricians and Gynecologists .
  • American Medical Association .
  • American Public Health Association .
  • Society for Adolescent Health and Medicine .

Pediatric clinics provide a unique opportunity for comprehensive sex education.

Pediatric health clinicians typically have longitudinal care relationships with their patients and families, and thus have unique opportunities to address comprehensive sex education across all stages of development.

The clinical visit can serve as a useful adjunct to support comprehensive sex education provided in schools, or to fill gaps in knowledge for youth who are exposed to abstinence-only or abstinence-plus curricula.

AAP policy and Bright Futures: Guidelines for Health Supervision of Infants, Children, and Adolescents provide recommendations for comprehensive sex education in clinical settings, including:

  • Encouraging parent-child discussions on sexuality, contraception, and internet/media use.
  • Understanding diverse experiences and beliefs related to sexuality and sex education and meeting the unique needs of individual patients and families.
  • Including discussions around healthy relationships, dating violence, and intimate partner violence in clinical care.
  • Discussing methods of contraception and STI/HPV prevention prior to onset of sexual intercourse.
  • Providing proactive and developmentally appropriate sex education to all youth, including children and adolescents with special health care needs.

Perspective

what is literature review of training and development

Karen Torres, Youth activist

There were two cardboard bears, and a person explained that one bear wears a bikini to the beach and the other bear wears shorts – that is the closest thing I ever got to sex ed throughout my entire K-12 education. I often think about that bear lesson because it was the day our institutions failed to teach me anything about my body, relationships, consent, and self-advocacy, which became even more evident after I was sexually assaulted at 16 years old. My story is not unique, I know that many young people have been through similar traumas, but many of us were also subjected to days, months, and years of silence and embarrassment because we were never given the knowledge to know how to spot abuse or the language to ask for help. Comprehensive sex ed is so much more than people make it out to be, it teaches about sex but also about different types of experiences, how to respect one another, how to communicate in uncomfortable situations, how to ask for help and an insurmountable amount of other valuable lessons.

From these lessons, people become well-rounded, people become more empathetic to other experiences, and people become better. I believe comprehensive sex ed is vital to all people and would eventually work as a part to build more compassionate communities.

Many US children and adolescents do not receive comprehensive sex education; and rates of formal sex education have declined significantly in recent decades.

Barriers to accessing comprehensive sex education include:

Misinformation, stigma, and fear of negative reactions:

  • Misinformation and stigma about the content of sex education curriculum has been the primary barrier to equitable access to comprehensive sex education in schools for decades .
  • Despite widespread parental support for sex education in schools, fears of negative public/parent reactions have led school administrators to limit youth access to the information they need to make healthy decisions about their sexuality for nearly a half-century.
  • In recent years, misinformation campaigns have spread false information about the framing and content of comprehensive sex education programs, causing debates and polarization at school board meetings .
  • Nearly half of sex education teachers report that concerns about parent, student, or administrator responses are a barrier to provision of comprehensive sex education.
  • Opponents of comprehensive sex education often express concern that this education will lead youth to have sex; however, research has demonstrated that this is not the case . Instead, comprehensive sex ed is associated with delays in initiation of sexual behavior, reduced frequency of sexual intercourse, a reduction in number of partners, and an increase in condom use.
  • Some populations of youth lack access to comprehensive sex education due to a societal belief that they are asexual, in need of protection, or don’t need to learn about sex. This barrier particularly impacts youth with disabilities or special health care needs .
  • Sex ed curricula in some schools perpetuate gender/sex stereotypes, which could contribute to negative gender stereotypes and negative attitudes towards sex .

Inconsistencies in school-based sex education:

  • There is significant variation in the content of sex education taught in schools in the US, and many programs that carry the same label (eg, “abstinence-plus”) vary widely in curriculum.
  • While decisions about sex education curriculum are made at the state level, the federal government has provided funding to support abstinence-only education for decades , which incentivizes schools to use these programs.
  • Since 1996, more than $2 billion in federal funds have been spent to support abstinence-only sex education in schools.
  • 34 US states require schools to use abstinence-only curriculum or emphasize abstinence as the main way to avoid pregnancy and STIs.
  • Only 16 US states require instruction on condoms or contraception.
  • It is not standard to include information on how to come forward if a student is being sexually abused, and many schools do not have a process for disclosures made.
  • Because of this, abstinence-only programs are commonly used in US schools, despite overwhelming evidence that they are ineffective in delaying sexual behavior until marriage, and withhold critical information that youth need for healthy sexual and relationship development.

Need for resources and training:

  • Integration of comprehensive sex education into school curriculum requires financial resources to strengthen and expand evidence-based programs.
  • Successful implementation of comprehensive sex education requires a trained workforce of teachers who can address the curriculum in age-appropriate ways for students in all grade-levels.
  • Education, training, and technical assistance are needed to support pediatric health clinicians in addressing comprehensive sex education in clinical settings, as a complement to school-based education.

Lack of diversity and cultural awareness in curricula:

  • A history of systemic racism, discrimination, and long-standing health, social and systemic inequities have created racial and ethnic disparities in access to sexual health services and representation in sex education materials. The legacy of intergenerational trauma in the medical system should be acknowledged in sex education curricula.
  • Sex education curriculum is often centered on a white audience, and does not address or reflect the role of systemic racism in sexuality and development .
  • Traditional abstinence-focused sex education programs have a heteronormative focus and do not address the unique needs of youth who are LGBTQ2S+ .
  • Sex education programs often do not address reproductive body diversity, the needs of those with differences in sex development, and those who identify as intersex .
  • Sex education programs often do not reflect the unique needs of youth with disabilities or special health care needs .
  • Sex education programs are often not tailored to meet the religious considerations of faith communities.
  • There is a need for sex education programs designed to help youth navigate sexual health and development in the context of their own culture and community .

Disparities in access to comprehensive sex education.

The barriers listed above limit access to comprehensive sex education in schools and communities. While these barriers impact youth across the US, there are some populations who are less likely to have access to comprehensive to sex education.

Youth who are LGBTQ2S+:

  • Only 8% of students who are LGBTQ2S+ report having received sexual education that was inclusive .
  • Students who are LGBTQ2S+ are 50% more likely than their peers who are heterosexual to report that sex education in their schools was not useful to them .
  • Only 13% of youth who are bisexual+ and 10% of youth who are transgender and gender expansive report receiving sex education in schools that felt personally relevant.
  • Only 20% of youth who are Black and LGBTQ2S+ and 13% of youth who are Latinx and LGBTQ2S+ report receiving sex education in schools that felt personally relevant.
  • Only 10 US states require affirming content on LGBTQ2S+ relationships in sex education curriculum.

Youth with disabilities or special health care needs:

  • Youth with disabilities or special health care needs have a particular need for comprehensive sex education, as these youth are less likely to learn about sex or sexuality form their parents , healthcare providers , or peer groups .
  • In a national survey, only half of youth with disabilities report that they have participated in sex education .
  • Typical sex education may not be sufficient for youth with Autism Spectrum Disorder, and special methods and curricula are necessary to match their needs .
  • Lack the desire or maturity for romantic or sexual relationships.
  • Are not subject to sexual abuse.
  • Do not need sex education.
  • Only 3 states explicitly include youth with disabilities within their sex education requirements.

Youth from historically underserved communities:

  • Students who are Black in the US are more likely than students who are white to receive abstinence-only sex education , despite significant support from parents and students who are Black for comprehensive sex education.
  • Youth who are Black and female are less likely than peers who are white to receive education about where to obtain birth control prior to initiating sexual activity.
  • Youth who are Black and male and Hispanic are less likely than their peers who are white to receive formal education on STI prevention or contraception prior to initiating sexual activity.
  • Youth who are Hispanic and female are less likely to receive instruction about waiting to have sex than youth of other ethnicities.
  • Tribal health educators report challenges in identifying culturally relevant sex education curriculum for youth who are American Indian/Alaska Native.
  • In a 2019 study, youth who were LGBTQ2S+ and Black, Latinx, or Asian reported receiving inadequate sex education due to feeling unrepresented, unsupported, stigmatized, or bullied.
  • In survey research, many young adults who are Asian American report that they received inadequate sex education in school.

Youth from rural communities:

  • Adolescents who live in rural communities have faced disproportionate declines in formal sex education over the past two decades, compared with peers in urban/suburban areas.
  • Students who live in rural communities report that the sex education curriculum in their schools does not serve their needs .

Youth from communities and schools that are low-income:

  • Data has shown an association between schools that are low-resource and lower adolescent sexual health knowledge, due to a combination of fewer school resources and higher poverty rates/associated unmet health needs in the student body.
  • Youth with family incomes above 200% of the federal poverty line are more likely to receive education about STI prevention, contraception, and “saying no to sex,” than their peers below 200% of the poverty line.

Youth who receive sex education in some religious settings:

  • Most adolescents who identify as female and who attended church-based sex education programs report instructions on waiting until marriage for sex, while few report receiving education about birth control.
  • Young people who received sex education in religious schools report that education focused on the risks of sexual behavior (STIs, pregnancy) and religious guilt; leading to them feeling under-equipped to make informed decisions about sex and sexuality later in life.
  • Youth and teachers from religious schools have identified a need for comprehensive sex education curriculum that is tailored to the needs of faith communities .

Youth who live in states that limit the topics that can be covered in sex education:

  • Students who live in the 34 states that require sex education programs to stress abstinence are less likely to have access to critical information on STI prevention and contraception.
  • Prohibitions on addressing abortion in sex education or mandates that sex education curricula include medically inaccurate information on abortion designed to dissuade youth from terminating a pregnancy.
  • Limitations on the types of contraception that can be covered in sex education curricula.
  • Requirements that sex education teachers promote heterosexual, monogamous marriage in sex education.
  • Lack of requirements to address healthy relationships and communication skills.
  • Lack of requirements for teacher training or certification.

Comprehensive sex education has significant benefits for children and adolescents.

Youth who are exposed to comprehensive sex education programs in school demonstrate healthier sexual behaviors:

  • Increased rates of contraception and condom use.
  • Fewer unplanned pregnancies.
  • Lower rates of STIs and HIV.
  • Delayed initiation of sexual behavior.

More broadly, comprehensive sexual education impacts overall social-emotional health , including:

  • Enhanced understanding of gender and sexuality.
  • Lower rates of homophobia and related bullying.
  • Lower rates of dating violence, intimate partner violence, sexual assault, and child sexual abuse.
  • Healthier relationships and communication skills.
  • Understanding of reproductive rights and responsibilities.
  • Improved social-emotional learning, media literacy, and academic achievement.

Comprehensive sex education curriculum goes beyond risk reduction, to ensure that youth are supported in understanding their identity and sexuality and making informed decisions about their relationships, behaviors, and future. These benefits are critical to healthy sexual development.

Impacts of a lack of access to comprehensive sex education.

When youth are denied access to comprehensive sex education, they do not get the information and skill-building required for healthy sexual development. As such, they face unnecessary barriers to understanding their gender and sexuality, building positive interpersonal relationships, and making informed decisions about their sexual behavior and sexual health.

Impacts of a lack of comprehensive sex education for all youth can include :

  • Less use of condoms, leading to higher risk of STIs, including HIV.
  • Less use of contraception, leading to higher risk of unplanned pregnancy.
  • Less understanding and increased stigma and shame around the spectrum of gender and sexual identity.
  • Perpetuated stigma and embarrassment related to sex and sexual identity.
  • Perpetuated gender stereotypes and traditional gender roles.
  • Higher rates of youth turning to unreliable sources for information about sex, including the internet, the media, and informal learning from peer networks.
  • Challenges in interpersonal communication.
  • Challenges in building, maintaining, and recognizing safe, healthy peer and romantic relationships.
  • Lower understanding of the importance of obtaining and giving enthusiastic consent prior to sexual activity.
  • Less awareness of appropriate/inappropriate touch and lower reporting of child sexual abuse.
  • Higher rates of dating violence and intimate partner violence, and less intervention from bystanders.
  • Higher rates of homophobia and homophobic bullying.
  • Unsafe school environments.
  • Lower rates of media literacy.
  • Lower rates of social-emotional learning.
  • Lower recognition of gender equity, rights, and social justice.

In addition, the lack of access to comprehensive sex education can exacerbate existing health disparities, with disproportionate impacts on specific populations of youth.

Youth who identify as women, youth from communities of color, youth with disabilities, and youth who are LGBTQ2S+ are particularly impacted by inequitable access to comprehensive sex education, as this lack of education can impact their health, safety, and self-identity. Examples of these impacts are outlined below.

A lack of comprehensive sex education can harm young women.

  • Female bodies are more prone to STI infection and more likely to experience complications of STI infection than male bodies.
  • Female bodies are disproportionately impacted by long-term health consequences of STIs , including pelvic inflammatory disease, infertility, and ectopic pregnancy.
  • Female bodies are less likely to have or recognize symptoms of certain STI infections .
  • Human papillomavirus (HPV) is the most common STI in young women , and can cause long-term health consequences such as genital warts and cervical cancer.
  • Women bear the health and economic effects of unplanned pregnancy.
  • Comprehensive sex education addresses these issues by providing medically-accurate, evidence based information on effective strategies to prevent STI infections and unplanned pregnancy.
  • Students who identify as female are more likely to experience sexual or physical dating violence than their peers who identify as male. Some of this may be attributed to underreporting by males due to stigma.
  • Students who identify as female are bullied on school property more often than students who identify as male.
  • Young women ages 16-19 are at higher risk of rape, attempted rape, or sexual assault than the general population.
  • Comprehensive sex education addresses these issues by guiding the development of healthy self-identities, challenging harmful gender norms, and building the skills required for respectful, equitable relationships.

A lack of comprehensive sex education can harm youth from communities of color.

  • Youth of color benefit from seeing themselves represented in sex education curriculum.
  • Sex education programs that use a framing of diversity, equity, rights, and social justice , informed by an understanding of systemic racism and discrimination, have been found to increase positive attitudes around reproductive rights in all students.
  • There is a critical need for sex education programs that reflect youth’s cultural values and community .
  • Comprehensive sex education can address these needs by developing curriculum that is inclusive of diverse communities, relationships, and cultures, so that youth see themselves represented in their education.
  • Racial and ethnic disparities in STI and HIV infection.
  • Racial and ethnic disparities in unplanned pregnancy and births among adolescents.
  • Nearly half of youth who are Black ages 13-21 report having been pressured into sexual activity .
  • Adolescent experience with dating violence is most prevalent among youth who are American Indian/Alaska Native, Native Hawaiian/Pacific Islander, and multiracial.
  • Adolescents who are Latinx are more likely than their peers who are non-Latinx to report physical dating violence .
  • Youth who are Black and Latinx and who experience bullying are more likely to suffer negative impacts on academic performance than their white peers.
  • Students who are Asian American and Pacific Islander report bullying and harassment due to race, ethnicity, and language.
  • Comprehensive sex education addresses these issues by guiding the development of healthy self-identities, challenging harmful stereotypes, and building the skills required for respectful, equitable relationships.
  • Young people of color—specifically those from Black , Asian-American , and Latinx communities– are often hyper-sexualized in popular media, leading to societal perceptions that youth are “older” or more sexually experienced than their white peers.
  • Young men of color—specifically those from Black and Latinx communities—are often portrayed as aggressive or criminal in popular media, leading to societal perceptions that youth are dangerous or more sexually aggressive or experienced than white peers.
  • These media portrayals can lead to disparities in public perceptions of youth behavior , which can impact school discipline, lost mentorship and leadership opportunities, less access to educational opportunities afforded to white peers, and greater involvement in the juvenile justice system.
  • Comprehensive sex education addresses these issues by including positive representations of diverse youth in curriculum, challenging harmful stereotypes, and building the skills required for respectful relationships.

A lack of comprehensive sex education can harm youth with disabilities or special health care needs.

  • Youth with disabilities need inclusive, developmentally-appropriate, representative sex education to support their health, identity, and development .
  • Youth with special health care needs often initiate romantic relationships and sexual behavior during adolescence, similar to their peers.
  • Youth with disabilities and special health care needs benefit from seeing themselves represented in sex education to access the information and skills to build healthy identities and relationships.
  • Comprehensive sex education addresses this need by including positive representation of youth with disabilities and special health care needs in curriculum and providing developmentally-appropriate sex education to all youth.
  • When youth with disabilities and special health care needs do not get access to the comprehensive sex education that they need, they are at increased risk of sexual abuse or being viewed as a sexual offender.
  • Youth with disabilities and special health care needs are more likely than peers without disabilities to report coercive sex, exploitation, and sexual abuse.
  • Youth with disabilities and special health care needs report more sexualized behavior and victimization online than their peers without disabilities.
  • Youth with disabilities are at greater risk of bullying and have fewer friend relationships than their peers.
  • Comprehensive sex education addresses these issues by providing education on healthy relationships, consent, communication, and bodily autonomy.

A lack of comprehensive sex education can harm youth who are LGBTQ2S+.

  • Most sex education curriculum is not inclusive or representative of LGBTQ2S+ identities and experiences.
  • Because school-based sex education often does not meet their needs, youth who are LGBTQ2S+ are more likely to seek sexual health information online , and thus are more likely to come across misinformation.
  • The majority of parents support discussion of sexual orientation in sex education classes.
  • Comprehensive sex education addresses these issues by including positive representation of LGBTQ2S+ individuals, romantic relationships, and families.
  • Sex education curriculum that overlooks or stigmatizes youth who are LGBTQ2S+ contributes to hostile school environments and harms the healthy sexual and mental development .
  • Youth who are LGBTQ2S+ face high levels of discrimination at school and are more likely to miss school because of bullying or victimization .
  • Ongoing experiences with stigma, exclusion, and harassment negatively impact the mental health of youth who are LGBTQ2S+.
  • Comprehensive sex education provides inclusive curriculum and has been shown to improve understanding of gender diversity, lower rates of homophobia, and reduce homophobic bullying in schools.
  • Youth who are LGBTQ2S+ are more likely than their heterosexual peers to report not learning about HIV/STIs in school .
  • Lack of education on STI prevention leaves LGBTQ2S+ youth without the information they need to make informed decisions, leading to discrepancies in condom use between LGBTQ2S+ and heterosexual youth.
  • Some LGBTQ2S+ populations carry a disproportionate burden of HIV and other STIs: these disparities begin in adolescence , when youth who are LGBTQ2S+ do not receive sex education that is relevant to them.
  • Comprehensive sex education provides the knowledge and skills needed to make safe decisions about sexual behavior , including condom use and other forms of STI and HIV prevention.
  • Youth who are LBGTQ2S+ or are questioning their sexual identity report higher rates of dating violence than their heterosexual peers.
  • Youth who are LGBTQ2S+ or are questioning their sexual identity face higher prevalence of bullying than their heterosexual peers.
  • Comprehensive sex education teaches youth healthy relationship and communication skills and is associated with decreases in dating violence and increases in bystander interventions .

A lack of comprehensive sex education can harm youth who are in foster care.

  • More than 70% of children in foster care have a documented history of child abuse and or neglect.
  • More than 80% of children in foster care have been exposed to significant levels of violence, including domestic violence.
  • Youth in foster care are racially diverse, with 23% of youth identifying as Black and 21% of identifying as Latinx, who will have similar experiences as those highlighted in earlier sections of this report.
  • Removal is emotionally traumatizing for almost all children. Lack of consistent/stable placement with a responsive, nurturing caregiver can result in poor emotional regulation, impulsivity, and attachment problems.
  • Comprehensive sex education addresses these issues by providing evidence-based, culturally appropriate information on healthy relationships, consent, communication, and bodily autonomy.

Sex education is often the first experience that youth have with understanding and discussing their gender and sexual health.

Youth deserve to a strong foundation of developmentally appropriate information about gender and sexuality, and how these things relate to their bodies, community, culture, society, mental health, and relationships with family, peers, and romantic partners.

Decades of data have demonstrated that comprehensive sex education programs are  effective  in reducing risk of STIs and unplanned pregnancy. These benefits are critical to public health. However, comprehensive sex education goes even further, by instilling youth with a broad range of knowledge and skills that are  proven  to support social-emotional learning, positive communication skills, and development of healthy relationships.

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Practice-Based Coaching (PBC)

Practice-Based Coaching (PBC) is a professional development strategy that uses a cyclical process. This process supports teachers’ use of effective teaching practices that lead to positive outcomes for children. PBC occurs in the context of collaborative partnerships. View the videos below to learn more about PBC .

Practice-Based Coaching Overview

[Music] 

[Inaudible]

Narrator: Michelle Noelle engages with pre-K learners at her home day care. Preschool teacher Tiffany Powers starts her day in the classroom, and Lisa Brown discusses educational strategies with parents of a young learner. While their settings are different, these practitioners have the same goal: best outcomes for the children they engage with. Best outcomes are achieved when practitioners, like Michelle, are provided with professional development that supports their learning and growth.

Michelle Noelle: OK. All done? OK. Let's drink some milk so we can go and play. OK?

Narrator: One professional development coaching model used with education staff working with children and families is called practice-based coaching.

Dr. Patricia Snyder: Practice-based coaching is an approach we use to support practitioners' implementation of interactional and teaching practices with young children to support their development and learning.

Narrator: Colleagues develop practice-based coaching for practitioners, including Head Start educators, after looking at the effectiveness of other development support.

Woman: Who cooks at home?

Maureen Conroy: What we found in our research is, when you go in and you do a one-day training that might last an hour, you just gain awareness. Teachers don't actually learn the practice.

Mary Louise Hemmeter: As we were developing this model, we were really concerned about an approach to professional development for early childhood educators that would result in practice change. So practice-based coaching is a cyclical process for supporting teachers, home visitors, other early childhood practitioners to implement any set of practices that are identified and defined well.

Narrator: The coaching model can complement other forms of professional development.

Kathleen Artman Meeker: So we really see practice-based coaching as being job-embedded ongoing support for those providers in their workplaces, and it's a nice follow-up in many cases to other forms of professional development, so staff may go to a workshop or go to a conference or be in a course at their local college, and practice-based coaching can be used to help take those practices that they learn in one setting, that they learn to recognize, learn to identify, maybe role-play with other adults, and then practice-based coaching brings it into their actual practice.

Narrator: Developers compared practice-based coaching to traditional forms of professional development around different sets of targeted teaching practices.

Patricia: We've done quite a bit of research on the impacts of practice-based coaching on teachers' implementation of evidence-based interactional and teaching practices. The teaching practices that we've specifically studied in our research include practices designed to support young children's social-emotional development and to promote positive behavior as part of the pyramid model, and we've used practice-based coaching to support practitioners' implementation of embedded instruction practices, which are practices that support the development and learning of young children with disabilities in the context of everyday activities and routines, either in their classrooms or in their homes. We've also used practice- based coaching to support people to implement what we call "best in class," which is a social- emotional set of practices designed to address young children who are at elevated risk for exhibiting persistent and sustained challenging behavior.

Man: Then it wouldn't be any fun. What we need to do is, if you know the answer, put your finger on your nose as fast as possible, so if I...

Narrator: Developers have researched whether practice-based coaching results in implementation of identified practices.

Mary: As professional development models go, practice-based coaching has a strong research base. We have evidence that it not only changes teacher practice, but we also have evidence that teachers continue to implement those practices even after their coaching has ended.

Joyce Escorcia: The Head Start Program Performance Standards provide some guidance regarding coaching. The Performance Standards tell us that each program must implement a research-based coordinated coaching strategy. Practice-based coaching is one specific research- based coordinated coaching strategy that a program could choose to implement.

Narrator: So what does practice-based coaching look like? The model is a cycle with distinct components, shared goals and action planning, focused observation, and reflection and feedback. Each component is integral to achieving positive outcomes.

Woman: And that seemed to spark a lot of conversation. How did that go?

Narrator: Practice-based coaching can happen face-to-face between a coach and coachee or at a distance, thanks to technology.

Joyce: The Head Start Coaching Companion is a web-based video-sharing and coaching feedback application, and so it's a really great tool for early childhood education staff to use when they're participating in coaching, so it's an opportunity for them to – kind of walk through the practice-based coaching cycle. They can share video, provide feedback, and kind of work on and develop their action plans.

Julie Gretchen: Education professionals are often really strapped for time. We see that as being one of the biggest obstacles for them in even building a coaching relationship is, "When are we going to do that?" Because they're so busy, and this provides them a way when they have 10 minutes sit down and watch a video, or when they have 10 minutes, look at maybe some notes I've made on an observation that they may have uploaded as a video and have that without having to set aside an hour here, an hour there. They can do it really as they have time.

Narrator: Practice-based coaching is a cyclical professional development strategy that can be used on its own or in conjunction with other forms of professional development to support practitioners in a variety of settings with a goal of implementing a defined practice to achieve best outcomes for children.

Practice-Based Coaching ( PBC ) supports education staff to use effective teaching practices in context. Learn about the basics of PBC and its research basis as a professional development strategy. Find out how PBC connects to the Head Start Program Performance Standards.

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National Centers: Early Childhood Development, Teaching and Learning

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  • Published: 26 February 2024

Using transfer learning-based plant disease classification and detection for sustainable agriculture

  • Wasswa Shafik   ORCID: orcid.org/0000-0002-9320-3186 1 ,
  • Ali Tufail   ORCID: orcid.org/0000-0003-4871-4080 1 ,
  • Chandratilak De Silva Liyanage   ORCID: orcid.org/0000-0001-7128-5945 1 &
  • Rosyzie Anna Awg Haji Mohd Apong 1  

BMC Plant Biology volume  24 , Article number:  136 ( 2024 ) Cite this article

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Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's “Zero Hunger,” “Climate Action,” and “Responsible Consumption and Production” sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the training process, how early signs of green attacks can be identified or classified remains uncertain. Most pests and disease symptoms are seen in plant leaves and fruits, yet their diagnosis by experts in the laboratory is expensive, tedious, labor-intensive, and time-consuming. Notably, how plant pests and diseases can be appropriately detected and timely prevented is a hotspot paradigm in smart, sustainable agriculture remains unknown. In recent years, deep transfer learning has demonstrated tremendous advances in the recognition accuracy of object detection and image classification systems since these frameworks utilize previously acquired knowledge to solve similar problems more effectively and quickly. Therefore, in this research, we introduce two plant disease detection (PDDNet) models of early fusion (AE) and the lead voting ensemble (LVE) integrated with nine pre-trained convolutional neural networks (CNNs) and fine-tuned by deep feature extraction for efficient plant disease identification and classification. The experiments were carried out on 15 classes of the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 categories. Hyperparameter fine-tuning was done with popular pre-trained models, including DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, ResNet18, EfficientNetB7, NASNetMobile, and ConvNeXtSmall. We test these CNNs on the stated plant disease detection and classification problem, both independently and as part of an ensemble. In the final phase, a logistic regression (LR) classifier is utilized to determine the performance of various CNN model combinations. A comparative analysis was also performed on classifiers, deep learning, the proposed model, and similar state-of-the-art studies. The experiments demonstrated that PDDNet-AE and PDDNet-LVE achieved 96.74% and 97.79%, respectively, compared to current CNNs when tested on several plant diseases, depicting its exceptional robustness and generalization capabilities and mitigating current concerns in plant disease detection and classification.

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Introduction

Agriculture, as a significant driver of the global economy, serves as the primary provider of food, income, revenue, and employment opportunities. Different human societies have been capable of producing food to adequately cater to the current and growing population using advanced technology in the agricultural sector [ 1 ]. However, depending on the season or environmental factors, plant pests and diseases are caused by nematodes , fungi , viruses , protozoa , and bacteria [ 2 , 3 ]. These severely influence plant health, structure quality, production, quantity, and the economy. One of the highly complex tasks regarding plant protection is the timely identification of plant symptoms, pests , and diseases [ 4 ]. Traditional approaches used in underdeveloped or developing nations are through human eye inspection, which is inaccurate, tedious, and time-consuming. Furthermore, smart agricultural gadgets are costly, and understanding these obtained classifications and detection on large farms needs agronomists and specialists is expensive [ 5 ].

Employing intelligent technologies capable of automatically detecting plant pests and diseases presents a promising approach to reducing total expenses in agriculture [ 6 ]. Therefore, academia and industry have used transfer learning (TL) and CNNs, particularly in the agricultural sector, for instance, in plant leaves, fruit, and disease classification, among other applications [ 7 ]. However, deep learning (DL) demands an increased number of parameters, thus increasing the training time and resulting in implementing small devices becoming complex and impractical [ 8 ]. Furthermore, properly extracting relevant characteristics from any given dataset is vital to the CNN-based model performance; for example, the studies utilized the widely used PlantVillage dataset, with various species of plant diseases across distinct categories [ 9 , 10 ].

There has been a growing emphasis on rapid plant disease identification and classification using TL architectures. The complexity and required parameters of the TL model are determined by the level of model sophistication and the number of filters utilized [ 11 ]. Although TL methods often require advanced image processing techniques, they have simplified the procedure, making it more efficient in terms of time, especially when the model has no starting weights [ 12 ]. In addition, TL models require minimal computational resources compared to traditional learning approaches. However, implementing these models on small devices with limited resources can be challenging and limitation of the traditional learning approach [ 13 ].

Several studies demonstrate that some current models are developed using the idea of TL to attain better results compared to other well-developed approaches using DL architectures through potent computing equipment, such as graphics processing units (GPUs) and servers [ 14 ]. Because of the high cost, it is not practical to use advanced equipment that includes GPUs in the agriculture field that traditional farmers cannot afford. Therefore, there is a need for applications with a reduced number of parameters and reduced levels of computation and power consumption [ 15 ].

A survey on adopting computer vision and soft computing methods for disease identification and classification from plant leaves was conducted. It demonstrated that Computer vision techniques enhance plant growth, increasing productivity, quality, and economic value [ 16 ]. They are critical in medical, defense, agriculture, remote sensing, and business analysis applications. Digital image processing methods simulate human visual capabilities, providing automatic monitoring, disease management, and water management [ 17 ].

Another proposed system used a neural network to segment mango leaves for disease. It involved real-time images, preprocessing, feature extraction, training, and extraction of diseased regions. The system achieved high-level accuracy for anthracnose disease segmentation, with an average Specificity of 0.9115 and Sensitivity of 0.9086. The system demonstrated an intuitive and user-friendly interface and is being developed for precision agriculture [ 18 ]. Similarly, a hybrid Fuzzy Competitive Learning-based Counter Propagation Network (FCPN) was proposed for image segmentation of natural scene images. Fuzzy Competitive Learning (FCL) was used to train the instar layer of FCPN, whereas Grossberg learning was used to train the Outstar layer. The region-developing method was utilized for seed point selection, clustering, and estimating the number of crop seeds. The FCPN method produced a lower convergence ratio and greater precision than alternative methods [ 19 ].

Pattern recognition and machine vision are indispensable for the resolution of complex problems. Combining conventional and optimization methods, like Nature-Inspired Algorithms (NIA) or Bio-inspired methods, can enhance precision and decrease computational time. One such application is image segmentation, for which the Bacterial Foraging Optimization Algorithm (BFOA) is a promising method [ 20 ]. The efficiency of the BFO-ANN method was demonstrated through comparison with other approaches. IPM was developed using an automated Radial Basis Function Neural Network (RBFNN) system to detect plant diseases. The system uses leaf images from the IPM agriculture database repository. The RBFNN achieves higher segmentation accuracy than other methods, making it a promising solution for detecting diseases in plants with biotic elements [ 21 ].

While a considerable number of studies availed some plant disease classification and detection models, there are notable deficiencies in these studies [ 4 , 15 , 17 , 20 ], including training on limited dataset size leading to model overfitting and generalization complexity to diverse environments. Training models under controlled backgrounds and environmental conditions, in contrast to the natural setting that makes these models impractical in the natural environment, the accuracy and robustness of models. Computation-related issues, for example, overfitting and difficulties in accurately extracting fine features during training, have impacted the efficiency and usefulness of DL models in the identification and classification of plant diseases. The conventional laboratory diagnosis of plant diseases is expensive, laborious, and time-intensive, which restricts its feasibility for prompt prevention in agriculture. Several current models, like [ 22 , 23 ], encounter challenges in terms of resilience and ability to apply them to diverse plant diseases since most are only trained on a single crop. Moreover, early classification and detection models proposed were done with restricted image constraints, like images containing colors. However, in the controlled environment, the background and the foreground are put in binary format [ 24 , 25 ]. However, in this scenario, the approaches employed in many earlier experiments are unsuitable for real-world smart-based agricultural system deployment that employ images that vary with natural-world backdrops.

Such shortcomings leave a gap in the availability of a robust and generalized model trained on the big dataset to detect and classify plant diseases trained on images without restriction on the background to increase the growth truth, thus being practical and implementable on small devices. As illustrated in this study, it employs transfer learning with pre-trained CNNs to improve performance and solve the issue of data scarcity; fine-tuning and deep feature extraction techniques on current cutting-edge CNNs are used to cater to background complexities. Moreover, it tackles computational issues by introducing two models, namely early fusion and lead voting ensemble, that incorporate several pre-trained CNNs; these models assist in overfitting reduction and improving feature extraction.

This study proposes two plant disease detection (PDD) and classification for CNN architecture with considerably reduced parameters. The TL uses nine comparative models: EfficienceNetB7, NASNetMobile, ConvNeXtSmall, DenseNet201, DenseNet101, ResNet50, GoogleNet, ResNet18, and AlexNet. These architectures employ numerous convolutions with changing filter sizes, resulting in superior feature extraction. We have turned to residual connections to address early disease detection problems. We opted for depth-wise separable convolution over conventional convolution because it reduces computational complexity, size, and parameter set without compromising the performance. This study uses a real-time PlantVillage image containing natural image traits. Therefore, this research contributes the following:

Propose two detection models (PDDNet) CNN architectures integrating the top six common CNNs that extract significant features and perform better. These models are demonstrated concisely. Arithmetic average ensemble (PDDNet-AAE) integrated fine-tuned network outputs. For the use of ensemble feature attraction (PDDNet-AE), the early average fusion method is used. In this instance, we combined deep traits collected from multiple DNNs and then trained with the LR classifier on these combined features. Lead voting ensemble class labels (PDDNet-LVE).

The study uses a logistic regression classifier to assess the proposed model performance compared to its counterparts (nine pretrained CNNs) that were used to extract deep features. Ultimately, all class labels that were the highest (lead) were voted for, and the system's decision was the most predicted class label.

The suggested architecture needs minimal parameters and is faster than traditional ML models tested on DenseNet201, DenseNet101, ResNet50, GoogleNet, ResNet18, AlexNet, EfficientNet, NASNet, and ConvNet.

Since the PlantVillage dataset is the most significant plant disease dataset currently publicly available, it was used to assess the proposed approaches. PDDNet-LVE outperformed the other current network models.

The proposed models achieved 96.74% and 97.79% accuracy on the early fusion and lead (majority) voting methods for this plant disease detection and classification, respectively. The CNN-LR combination of PDDNet-AE and PDDNet-LVE outperformed the simple averages of CNNs and has demonstrated improved results.

The remainder of this research is arranged into four sections following the introduction. Section " Related Literature " sheds light on the related literature on plant pest and disease classification and detection utilizing TL models, demonstrating the classification techniques used, the type of crop studied, and the reported accuracy. Section " Material and Methods " illustrates the study materials and methodology, including the PlantVillage description and plant diseases within the dataset. Section " Obtained Results and Discussion " demonstrates the results, and related discussions are presented, illustrating performance evaluations on classifiers, sampled deep learning, PDDNet-AE, PDDNet-EA, and PDDNet-LVE, and a comparison based on state-of-art models. Finally, Section " Conclusion " discusses the research conclusion and future research directions.

Related literature

The section discusses some DL methods for plant pests and disease detection and classification. Traditional ML approaches are based on creating features and segmentation, and DL techniques are based on learning from data in its raw form.

Using pre-trained CNNs like GoogleNet and AlexNet could classify twenty-six pests and diseases within fourteen plant species [ 7 ]; 99.34% was obtained through GoogleNet. AlexNet, GoogleNet, VGG, feat, and AlexNetOWTBn could recognize 58 leaf diseases [ 9 ]. A nine-layered deep convolutional network was used for plant disease detection, and 96.46% achieved accuracy [ 26 ]. Similarly, AlexNet's fully connected layer with GoogLeNet's inception layer to classify four diseases of apple leaves, and the average accuracy score reported 97.62% [ 27 ]. For the model optimization, InceptionV3, VGG19, VGG16, and ResNet detected tomato leaf disease and obtained 93.70% field accuracy and 99.60% laboratory accuracy [ 28 ]. VGG16 identified eggplant diseases using the super vector machine classifier for red, green, and blue; YCbCr and HSV were tested for robustness with 99.4% RGB [ 29 ]. Entirely improved pretrained DL plant disease with 99.75% model classification accuracy.

The authors demonstrated that using the SVM classifier on rice leaf disease classification could categorize eleven deep CNN model features and obtain an average of 98.38% using ResNet50 depth SVM [ 1 ]. Authors in [ 30 ] identified ten diseases of four plant species using six pretrained TL architectures, VGG16 corrected 90% of test datasets, and the authors found three cassava plant diseases and two pest damages using InceptionV3 transfer learning noticed six cassava illnesses using mobile devices [ 31 ]. In [ 32 ], the study determined that a 50-layer residual neural network can detect three wheat diseases using the ReLU activation and batch normalization following convolution and pooling. Using the German real-time field images, they reported a 96% accuracy. SqueezeNet 227.6MB and SequenzeNet 2.9MB obtained four tea leaf diseases after being tested Cifar ten fast CNN model depthwise separable convolution [ 33 ].

A well-trained VGG model identified and classified rice and agricultural diseases [ 8 ]. Two inception layers replaced VGGNet's fully connected layers: corn with 80.38% and rice with 92%, respectively. Singh and Misra [ 34 ] detailed how the soft computing methods and segmentation of images aid in plant, pest, and disease identification and classification in mostly grown plants like Malus domestica (apple), Zea mays , and genus Vitis diseases using pre-trained CNNs like VGG16 model, some other metaheuristic-inspired algorithms like genetic algorithm.

Gray level co-occurrence matrix (GLCM) with a moveable client-to-server structure for leaf disease detection and their classifications through Gabor wavelet transform (GWT) was used. In the mobile disease diagnosis system, feature vectors represent disease regions that can indicate many resolutions and directions. The mobile client preprocesses leaf photos, segments , and the affected leaf sections and sends them to the Pathology Server, lowering transmission costs. The Server extracts GWT–GLCM features and classifies K-Nearest Neighbors. Short message service displayed results with 93% accuracy under ideal conditions [ 35 ]. Table 1 summarizes the conventional methods, datasets, and the reported performance accuracy corresponding to those methods. In most cases, to summarize this, these studies are presented in three primary stages:

Plant pests and disease image segmentation is based on applying techniques like mathematical morphology , edge detection, color transformation, and pattern classification.

Detection of plant pests and diseases using traditional ML techniques.

Representative feature extraction from the segmented images that were obtained utilizing approaches that were based on color, texture, and shape.

The presented models specified in Table 1 are classification algorithms that utilized minimal datasets to differentiate between a limited number of species . Some studies used datasets from apple, Solanum lycopersicum , R. groenlandicum , and maize plants, and most of the reported accuracy ranged from 84% to 97%. Several plant disease detection studies have employed DL as demonstrated. These systems, datasets, and outcomes are demonstrated in Table 2 . Most of these experiments included deep network fine-tuning and pretrained CNN feature extraction. To illustrate this, Sabrol and Satish based their study on the tomato disease classification; they used TL to extract features from the images, for example, shape, texture, color, and features with constrained image appearance, and reported a 94% accuracy [ 40 ]. The algorithms described in the literature utilize varied datasets and categorize two to four plant species; hence, they cannot be compared directly.

Material and methods

This section entails the background of the deep learning techniques, the PlantVillage dataset, and the proposed methodology.

Deep learning techniques

Deep learning has been applied extensively in several arenas; its approach to plant disease detection and classification has been extensively used through pretrained deep networks [ 73 , 74 , 75 , 76 ]. Within this study, we use nine edge-cutting pretrained networks for deep feature extraction for our classification model to have a starting training weight. Table 3 demonstrates the nine pretrained deep CNNs (namely, EfficientNetB7, NASNet, ConvNet, DenseNet201, DenseNet101, ResNet50, GoogleNet, ResNet18, and AlexNet), showing their distinct characteristics on size, accuracy, parameters, depth, and GPU requirements.

PlantVillage dataset

There is a considerable number of plant pests and disease datasets publicly available, including strawberry [ 79 ], rice [ 80 ], NLB dataset for maize plant, Turkey-PlantDataset [ 81 ], apple, AES-CD9214, PlantVillage, among others. According to the available datasets, we consider the PlantVillage dataset since it has several plant species and over thirty categories with almost all plant characteristics from different datasets. The rest of the datasets checked were found to focus on a single crop that narrows the classification base, and the number of plant leaf images was limitedly low compared to the PlantVillage dataset. Using the pretrained CNNs on a big dataset like PlantVillage assumes proper deep feature extraction. DenseNet models are comparable to ResNet models, except that each layer receives information from all preceding layers. Each Densenet layer feeds forward as early as demonstrated [ 82 ]. This study employs DL with six models to extract features to categorize plant diseases. CNNs that have been previously trained and proficient at extracting features and training deep networks. This approach is exceptional since it is more precise in using the LR classifier as a substitute for the output layers of these CNNs.

The PlantVillage dataset was developed to provide effective methods for identifying 38 distinct plant disease classes. It comprises 61,486 plant images in three versions: color, gray-scaled, and segmented. However, we consider 15 categories containing 54,303 PlantVillage images for this experiment. The study considered the PlantVillage dataset with 15 categories since it is more evenly distributed across the different classes than 38 categories. Uneven data distribution can lead to class imbalance issues, where some classes have significantly fewer samples than others. This significantly impacts ML models' performance when accurately predicting the underrepresented classes. Notably, the source of this dataset ( https://plantvillage.psu.edu ) no longer exists. However, our open-source platforms, including Kaggle and GitHub, have datasets available as linked.

Deep features were extracted using nine different pretrained CNNs to make the dataset more diverse and show a wide range of details. During this process, numerous modifications were made employing three channels as well. These enhancements included gamma correction, principal component analysis, noise injection, scaling, image flipping, rotation, and color augmentation. In addition, scaling, rotation, and image flipping (RGB) were used. Figure 1 presents image samples from the PlantVillage plant disease species.

figure 1

Plantvillage selected leaf image samples from the considered plant dataset in this study. (Legend: D1) Pepper bell bacterial spot , D2) potato early blight , D3) potato late blight , D4) Tomato bacterial spot , D5) Tomato early blight , D6) Tomato Lead mold , D7) Tomato Septoria leaf spot , D8) Spider mites Two-spotted spider mite , D9) Tomato target spot , D10) Tomato Yellow Leaf Curl Virus , D11) Tomato mosaic virus , D12) Apple Scrab , D13) Grape black rot , D14) Orange Huanglongbing (Citrus_greening), and D15) Squash powdery mildew

Methodology

To tackle the challenge of plant disease identification and classification, we consider feature extraction and fine-tuning approaches among the existing TL approaches, including the intermediate layers, fine-tuning, and feature extraction. The selected pre-trained CNNs are used as a feature extractor. The output of the last convolutional layer is used as a feature vector for the new task. Then, the CNNs are fine-tuned on the new dataset. The weights of the lower layers are frozen, and only the weights of the upper layers are updated. TL can save resources, as the model does not need to be trained from scratch.

Therefore, we consider TL for the nine most recent pretrained deep networks: DenseNet201, DenseNet101, ResNet50, ResNet18, GoogleNet, AlexNet, EfficientNetB7, NASNetMobile, and ConvNeXtSmall for feature extraction to aid in the classification problem process. Then, the LR classifier will evaluate the performance at an individual model level, utilizing the weights obtained from these networks. A comparison is then made based on the arithmetic average (AAE), initial (early), amalgamation or fusion (EA), and lead voting ensemble (LVE), commonly referred to as majority voting. Finally, we use the LR classifier to replace a superficial network block for fusion in the PDDNet technique coupled with the final layers of deep neural network in the PDDNet-LVE method.

The image input size is often different depending on the selected pretrained deep network architecture, as the second last column of Table 3 illustrates. For example, AlexNet and DesNet201 require different data inputs of 227 x 227 x 3 and 224 x 224 x 3, among others, at the input layer. Furthermore, due to the diverse CNNs selected for these experiments, the initial convolutional layer and the subsequent convolutional layers use different kernels; for instance, DenseNet201 with all convolutional layers use 3x3 kernels; ResNet101 utilized the initial convolutional layer uses a 7x7 kernel and the all-subsequent convolutional layers use 3x3 kernels.

ResNet50 at the initial convolutional layer uses a 7x7 kernel, and all subsequent convolutional layers use 3x3 kernels. GoogleNet uses 7x7 kernels at the initial convolutional layer uses, and most of the subsequent convolutional layers use 1x1 kernels, while a few layers use 3x3 kernels. AlexNet considers that the initial convolutional layer uses an 11x11 kernel, and the subsequent convolutional layers use 3x3 kernels. Lastly, ResNet18 at the initial convolutional layer uses a 7x7 kernel, and all subsequent convolutional layers use 3x3 kernels, as used during the experiment.

The proposed model approaches were executed using the MATLAB 2022b DL toolbox Footnote 1 . The PlantVillage dataset was divided into training, validation, and testing. Adaptive Moment Estimation (Adam) is applied as the optimizer since it employs stochastic optimization, like ML and TL. The recursive nature of the method enables the efficient solving of noisy linear systems and the estimation of extreme values of functions that are only accessible over noisy annotations. Incorporating square propagation in stochastic gradient descent, adaptive gradient, and root mean, Adam combines the benefits of stochastic gradient descent with momentum and root mean square propagation. In addition, the batch sizes varied depending on a step size of 10 within the range of 10 to 100, and it was saturated at 10 epochs. The selected networks were configured with a 1 gradient threshold, and the learning rate ranged between 0.1 to 0.001.

In this method, we experimented based on an arithmetic ensemble average that included late fusion. Initially, TL was applied to architectures, including DenseNet201, DenseNet101, ResNet50, ResNet18, GoogleNet, AlexNet, EfficientNetB7, NASNetMobile, and ConvNeXtSmall. In this instance, the focal contribution of this study is to substitute the last three layers of these CNNs, that is to say, a fully connected (designed to learn features from the images), a softmax (sometimes called a normalized exponential function that presents covert real numbers to probability function to approximate outcomes), and a classification (follows the softmax layer, it detects, classify mutually exclusive classes (categories) via the cross entropy function) layers with new layer definition. After fine-tuning procedure, the effectiveness of every transfer learning pretrained model was analyzed employing the data prepared for testing. Finally, the results of the PDDNet-AAE ensemble were agreed upon with the rest of the finely adjusted networks.

For the early fusion, this model is trained with the LR classifier with features produced from numerous deep networks with fully connected layers and then concatenates these features using the methodology presented (Section " PDDNet‑AAE "). Figure 2 demonstrates an overview of the method's flow diagram.

figure 2

General overview of the PDDNet-EA model

Considering the demonstrated flow within Fig.  4 , the classifier trains the deep features aggregated after being assembled from numerous pretrained networks. Additionally, we employed various combinations of six defined networks to ascertain the class label with the PDDNet model that we suggested. It is significant to mention that these pretrained networks were utilized in this ensemble.

We started by extracting deep features from the layers of these fully connected architectures. Then, the final three layers were changed to the LR classifier of previously trained deep network architectures. The deep features accumulated from every architecture were utilized during classifier training. Finally, the approach of lead voting by a majority (LVE) was employed for all existing labels within the PlantVillage dataset. Only the class label considered to have the highest level of accuracy served as the final selection for the method (LVE), as depicted in Fig.  3 .

figure 3

General overview of the PDDNet-LVE model

Obtained results and discussion

This section mainly demonstrates the obtained results and the corresponding discussions of proposed models of an integrated ensemble LR model classifier that uses deep features and averages of the CNN models. The proposed models are based on deep feature extraction, and then we tested three model approaches, namely AAE, EA, and LVE, employing pretrained networks.

We used the PlantVillage dataset to test the suggested approach described in subsection " Methodology ". This dataset includes color, gray-scaled, and segmented image categories, encompassing healthy and unhealthy plant left species collected and utilized in their natural ecological setting. Table 4 provides the dataset class literature according to disease names. Table 5 depicts the plant type and image sampling quantities used in this research's training and testing phases; the computer and simulation parameters are presented in Table 6 .

We discuss the results and performance assessments in detail in the following subsections. The experiments were used using Matlab2022b simulator Footnote 2 and NVIDIA Footnote 3 with GeForce RTX 2070 and DirectX runtime version 12.0.

Performance evaluation of classifiers

There are five standard classifiers, for instance, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), LR, and Naive Bayes (NB), that are often used in deep learning methodologies to evaluate these pretrained CNNs networks. Table 7 illustrates the testing accuracy for every class using different classifiers. Moreover, the model performances are further assessed in terms of F1 scores, accuracy, recall, and precision using False Positives (FP), False Negatives (FN), True Negatives (TN), and True Positives (TP).

TP represents the number of instances correctly predicted as positive by the model. In other words, it corresponds to the case where the model predicts the positive class correctly. TN represents the number of instances correctly predicted as negative by the proposed model. It corresponds to the case where the model predicted the negative class correctly. FP epitomizes the number of instances incorrectly predicted as positive by the model. It corresponds to the case where the model predicted the positive class when the actual class was negative. Finally, FN denotes the number of instances incorrectly predicted as negative by the model. It corresponds to the case where the model predicted the negative class when the actual class was positive.

The term "accuracy" is the proportion of correct predictions completed compared to the total number of data points collected (T). In scientific literature, it is referred to as recognition, correctness, or success rate and expressed as Eq. 1 .

The proportion of actual positive samples found to the total samples anticipated to be positive calculated as presented in Eq. 2 .

Sensitivity and recall

The term "sensitivity" or "recall" refers to the proportion of correctly anticipated positives to the total number of actual positive results (Eq. 3 ).

The F1-score refers to the harmonic mean of precision and sensitivity (recall), expressed in Eq. 4 .

Based on the testing accuracies presented in Table 6 , on average, LR obtained 93.88 %, NB with 70.98%, KNN with 84.93 %, SVM with 91.55%, and RF with an accuracy performance of 78.43%, thus making us select LR to be used during the experiments leading to the conclusion that increasing the data size improves exceptionally the performance accuracies. Table 7 presents the precision and recall values and F1 scores. Finally, Table 8 illustrates the accuracy scores obtained with different batch sizes in the LR classifier.

Performance evaluation on deep learning

We performed fine-tuning for previously trained CNN models using the DL methodology to evaluate these DL networks. The process of fine-tuning was accomplished by transferring new layers to our plant disease detection and classification problem to replace the deep CNN's last three layers, as described earlier. We examined the accuracy of fine-tuning to observe the effect of TL on the overall performance of the counterparts. After using the hyperparameter fine turning, we considered the minimum batch capacity to be sixteen, the max epochs were put to 10, 0.0001 on the weight decay adjustments, and the learning rate primarily ranged from 0.001 to 0.01. Similarly, for the learning optimization approach, a Mini Batch Stochastic Gradient Descent (MB-SGD) was applied for the deep neural networks to optimize their performance. As a result, 5000 iterations were fully completed for the training procedure, and the obtained accuracies are presented in Table 9 . The bold figures within all tables denote the best-performing model.

According to Table 9 , the DenseNet201 achieved the highest accuracy among pretrained models based on transfer learning, achieving 93.48%, while the AlexNet achieved the lowest performance with 86.93%. Both results can be compared to those attained using transfer learning on the DenseNet201 architecture. It is further observed that an increment in the complexity improves the accuracies. According to these reported results, the last layer of these models is replaced with the LR classifier. Consequently, the LR was fed with deep features extracted from pretrained CNN networks, presented in Table 10 .

The LR classifier parameters used were quadratic kernel functions, cubic and tenfold cross-validation approach, and the "one versus all" strategy, which was proven to be the most effective evaluator. According to Table 11 , the DenseNet201 model demonstrated an accuracy of 94.86% when detecting plant diseases. Depending on the results, this was the maximum level of accuracy that could be attained after several fine turns. More interestingly, the presented findings in Table 9 are improved to those in Table 10 , demonstrating that utilizing the LR as the last layer is advantageous. As a result, we use LR with the other pretrained models with deep features for the remaining part of the experiments.

Performance evaluation on PDDNet‑ AAE model

To evaluate this proposed model, a combination of the above-mentioned pretrained CNNs is used by calculating the average scores from these networks for each class as early as demonstrated in [ 64 ]. The accuracy score was calculated using the score-based fusion technique of the deep CNNs with the finest performance, as Table 11 demonstrates. Based on the class distribution, the weighted average accuracy was 93.7%.

Performance evaluation on the PDDNet‑EA model

The early fusion that was hypothesized, the CNN-LR model, was initially developed based on an early fusion combining the information gathered from the deep CNNs (as Fig.  2 demonstrated). Through several combinations of the six selected CNNs, we achieved the outcomes provided in Table 12 in the subsequent columns, determined by the average accuracy and the standard deviation of those scores. For example, based on Table 13 , the PDD-AAE model's maximum accuracy score was 96.79% using DenseNet201, ResNet101, AlexNet, ResNet50, and GoogleNet networks. Because of this, utilizing a pretrained version of ResNet18 in the presence of ResNet50 and ResNet101 is not productive, as most networks provide the most significant results without being used.

Performance evaluation on the PDDNet‑LVE model

The results were produced with the PDDNet-LVE model, based on the lead (majority) votes obtained from detecting the class labels acquired from the LR classifier with deep features presented in Fig.  3 and the last column of Table 12 . Moreover, the maximum accuracy score possible with the PDD-LVE model was attained when a mixture of AlexNet, DenseNet201, ResNet50, ResNet101, and GoogleNet was used. This resulted in accuracy scores of 96.94% and 97.79% for the EA and LVE models, respectively. These findings are consistent with those seen in Table 13 , which shows that the best outcomes were achieved with all CNNs.

Comparison with edge-cutting models

As demonstrated earlier, CNNs have widely been used in object class label classification, object recognition patterns, and objection detection most recently. Since the most pretrained deep networks were considered, DenseNet201, DenseNet101, ResNet50, GoogleNet, ResNet18, AlexNet, EfficientNetB7, NASNetMobile, and ConvNet have been compared based on the documented accuracy with the most recent published results about plant disease classification [ 83 ]. Table 13 demonstrates the accuracy of the models used during the experiment, and Table 14 shows the recently proposed model using some or all used pretrained models during the study.

The study considers tomato class with 16,703 plant images obtained from the PlantVillage dataset entailing 1,591 healthy leaves, 373 Mosaic Virus , 3,209 Yellow Leaf Curl Virus , 1,404 Target Spot , 1,676 Spider Mites Two Spotted Spider Mite , 1,771 Septoria leaf spot , 952 Leaf Mold , 1,909 Late Blight , 1,000 Late Blight , 2,127 Bacterial Spots images as presented within the dataset. After 10 epochs, the classification results are demonstrated in Fig.  4 , utilizing some of the considered pre-trained models, namely ResNet101 and DesnseNet201. Figure 5 presents a confusion matrix after replacing the first and second modified layers (i.e., a fully connected and a softmax layer) of EfficientNet and ConvNet. Figure 6 presents a confusion matrix of the proposed two models. Note: 1 through 10 on the horizontal axis depict the ten tomato leaf image categories.

figure 4

Tomato leaves (PlantVillage) classification results of the best performed amongst the selected CNNs

figure 5

Tomato leaves (PlantVillage) classification results after replace layer replacement

figure 6

Tomato leaves (PlantVillage) classification results by proposed models classification

In this research, early fusion and lead voting ensembles were introduced, combined with nine pretrained CNNs, and fine-tuned for deep feature extraction. Using TL and 15 classes of PlantVillage Dataset, the models outperformed CNNs in plant and disease detection with 96.74% and 97.79% accuracy. These models are robust and generalizable, providing practical solutions to improve plant disease detection and classification accuracy and effectiveness, improving agricultural practices and sustainable food production as the population grows. The research's findings emphasize the significance of advanced technology in mitigating concerns associated with plant disease classification and detection.

In future research, focus on resolving issues related to real-time data collecting and creating a multi-object deep learning model capable of identifying plant illnesses based on a cluster of leaves rather than just a single leaf amidst comparative statistical analysis. Moreover, we are striving to implement a mobile application or web-enabled service utilizing the trained model derived from this research to support a wider plant disease research community to benefit the agricultural sector. Also, to move toward a more lightweight disease classification, model quantization, and object localization networks are critical to better spot the species leaves in a complex background using the trending vision transformers.

Availability of data and materials

The datasets generated during and analyzed during the current study are available at: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset .

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This research was fully funded by the School of Digital Science, Universiti Brunei Darussalam, and the Ministry of Education (MoE), Universiti Brunei Darussalam.

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Wasswa Shafik, Ali Tufail, Chandratilak De Silva Liyanage & Rosyzie Anna Awg Haji Mohd Apong

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Wasswa Shafik: Conceptualization, Methodology, Investigation (lead), Data Curation, Formal Analysis (lead), Writing – original draft, Software (lead). Ali Tufail: Funding Acquisition, Methodology, Resources, Supervision, Visualization, Writing – review and editing. Chandratilak De Silva Liyanage: Conceptualization (supporting); Supervision, Writing – review and editing (equal). Rosyzie Anna Awg Haji Mohd Apong: Supervision, Conceptualization (supporting); Writing – review and editing (equal). All authors read and approved the final manuscript.

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Wasswa Shafik (Member, IEEE) received a Bachelor of Science degree in information technology engineering with a minor in mathematics from Ndejje University, Kampala, Uganda, in 2016 and a Master of Engineering degree in information technology engineering (MIT) from Yazd University, Yazd, Iran, in 2020. He is pursuing a Ph.D. in computer science with the School of Digital Science, Universiti Brunei Darussalam, Brunei Darussalam. He is also the Founder and a Principal Investigator of the Dig Connectivity Research Laboratory (DCRLab) after serving as a Research Associate at the Network Interconnectivity Research Laboratory at Yazd University. Prior to this, he worked as a Community Data Analyst at Population Services International (PSI-Uganda), Community Data Officer at Programme for Accessible Health Communication (PACE-Uganda), Research Assistant at the Socio-Economic Data Centre (SEDC-Uganda), Prime Minister’s Office, Kampala, Uganda, an Assistant Data Officer at TechnoServe, Kampala, IT Support at Thurayya Islam Media, Uganda, and Asmaah Charity Organization. He has more than 60 publications in renowned journals and conferences. His research interests include plant pathology, computer vision, AI-enabled IoT/IoMTs, IoT/IIoT/OT security, cyber security, and privacy.

Ali Tufail is currently working as a Senior Assistant Professor of Distributed and Cyber-Physical Systems at the School of Digital Science (SDS), Universiti Brunei Darussalam. He completed his Ph.D. in Information and Communication Engineering at Ajou University South Korea in 2011, Master of Science in Advanced Computing at the University of Bristol UK in 2006, and Bachelor's degree in Information Technology at the National University of Sciences and Technology Pakistan in 2005. Dr. Ali’s teaching and research specializations are in wireless sensor networks, the Internet of Things, and vehicular ad hoc networks. Dr. Ali has more than 10 years of teaching experience in countries such as Pakistan, South Korea, Turkey, and Saudi Arabia. He has 25+ publications in renowned journals and conferences. Dr. Ali is also serving as SDS Seminar Coordinator and Learning Technology Advisor.

Professor. Chandratilak De Silva Liyanage received a BSc Eng (Hons) degree from the University of Moratuwa Sri Lanka in 1985, an MPhil degree from The Open University of Sri Lanka in 1989, and MEng and PhD degrees from the University of Tokyo, Japan in 1992 and 1995, respectively. He was with the University of Tokyo, Japan, from 1989 to 1995. From April 1995 to March 1997, he pursued his postdoctoral research as a researcher at ATR (Advanced Telecommunication Research) Laboratories, Kyoto, Japan. In March 1997, he joined The National University of Singapore as a Lecturer, where he was an Assistant Professor till June 2003. He was with Massey University, New Zealand, from 2003 to 2007. Currently, he is the Professor of Engineering and the Deputy Dean of the Faculty of Integrated Technologies at the University of Brunei Darussalam. Liyanage has published over 160 technical papers in these areas in international conferences, journals, and Japanese national conventions and holds one Japanese national patent, which was successfully sold to Sony Corporation Japan for commercial utilization. He holds 1 US and 1 Brunei patent. Liyanage’s works have been cited as pioneering works in bimodal (audio and video signal-based) emotion recognition by many researchers. His papers so far have been cited more than 4500 times (according to scholar.google.com) with an h-index of 27. He received the Best Student Paper Award from SPIE (The International Society for Optical Engineering) for an outstanding paper contribution to the International Conference on Visual Communication and Image Processing (VCIP) in 1995. He also received the National University of Singapore Department of ECE Teaching commendation award in 2001 and 2002 consecutively. He is a senior member of IEEE USA and the interim chair of IEEE Brunei Darussalam Subsection. He was the General Chair of the 4th International Conference Computational Intelligence and Robotics and Autonomous Systems (CIRAS2007) held in New Zealand.

Rosyzie Anna Awg Haji Mohd Apong received her Ph.D. in Computer Science from Manchester University in 2018, her MSc in Multimedia and Internet Computing from Loughborough University in 2016, and her BSc in Computer Science from Strathclyde University in 2004. She is currently a lecturer at the School of Digital Science, Universiti Brunei Darussalam. Her research interests are Text Mining, the Internet of Things, and Information Retrieval. She has published and reviewed about ten papers.

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Shafik, W., Tufail, A., De Silva Liyanage, C. et al. Using transfer learning-based plant disease classification and detection for sustainable agriculture. BMC Plant Biol 24 , 136 (2024). https://doi.org/10.1186/s12870-024-04825-y

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Development and application of emotion recognition technology — a systematic literature review

  • Runfang Guo 1 , 2 ,
  • Hongfei Guo 4 ,
  • Liwen Wang 2 ,
  • Mengmeng Chen 3 ,
  • Dong Yang 2 &
  • Bin Li 1 , 2  

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There is a mutual influence between emotions and diseases. Thus, the subject of emotions has gained increasing attention.

The primary objective of this study was to conduct a comprehensive review of the developments in emotion recognition technology over the past decade. This review aimed to gain insights into the trends and real-world effects of emotion recognition technology by examining its practical applications in different settings, including hospitals and home environments.

This study followed the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines and included a search of 4 electronic databases, namely, PubMed, Web of Science, Google Scholar and IEEE Xplore, to identify eligible studies published between 2013 and 2023. The quality of the studies was assessed using the Critical Appraisal Skills Programme (CASP) criteria. The key information from the studies, including the study populations, application scenarios, and technological methods employed, was summarized and analyzed.

In a systematic literature review of the 44 studies that we analyzed the development and impact of emotion recognition technology in the field of medicine from three distinct perspectives: “application scenarios,” “techniques of multiple modalities,” and “clinical applications.” The following three impacts were identified: (i) The advancement of emotion recognition technology has facilitated remote emotion recognition and treatment in hospital and home environments by healthcare professionals. (ii) There has been a shift from traditional subjective emotion assessment methods to multimodal emotion recognition methods that are grounded in objective physiological signals. This technological progress is expected to enhance the accuracy of medical diagnosis. (iii) The evolving relationship between emotions and disease throughout diagnosis, intervention, and treatment processes holds clinical significance for real-time emotion monitoring.

These findings indicate that the integration of emotion recognition technology with intelligent devices has led to the development of application systems and models, which provide technological support for the recognition of and interventions for emotions. However, the continuous recognition of emotional changes in dynamic or complex environments will be a focal point of future research.

Peer Review reports

Introduction

Emotional expression plays a crucial role in human life and work. The earliest definition of “emotion” appeared in the writings of William James (1884), the founder of American psychology. He believed that emotions are sensations of physical change and that any emotion is inevitably accompanied by physiological changes, such as facial expressions, muscle tension, and visceral activity [ 1 ]. Similarly, Danish physiologist Lange (1885) presented a similar viewpoint: emotions are not only physiological states that integrate sensations, thoughts, and behaviors but also psychological responses generated by various external stimuli [ 2 ]. As a result, researchers in numerous fields have recognized the importance of accurately identifying emotions. In recent years, research on emotion recognition has been applied predominantly in fields such as psychology, affective computing, and clinical therapy.

According to the World Health Organization (WHO), approximately 280 million people worldwide experience depression, with more than 700,000 people dying from suicide [ 3 ]. There are many mood-related disorders, such as bipolar disorder (BD), which is characterized by recurrent episodes of alternating mania and depressive symptoms [ 4 , 5 ]. The manic and pathological states of BD can also be understood as extreme expressions of basic emotions such as sadness, happiness, and disgust. Emotions may be intentionally or unintentionally suppressed, and many individuals might struggle to differentiate between fear and anxiety and between guilt and shame, making it challenging for them to accurately describe complex emotions. Patients with mood-related disorders experience more severe emotional fluctuations than healthy individuals [ 6 ], which can, to some extent, reflect the progression of the disease, the risk of relapse, and impaired functioning [ 7 , 8 ]. Therefore, the continuous monitoring of emotional instability and other variables that may reflect disease activity (such as symptom duration, severity, and frequency) has clinical significance.

Self-monitoring is ubiquitous in the field of psychiatry research. Humans can describe emotions through text, language, or facial expressions and even reflect internal emotions through physiological signals. Emotional charting tools, such as the National Institute of Mental Health’s Life Chart Method (NIMH-LCM) [ 9 ], the Symptom Checklist-90-Revised (SCL-90-R) [ 10 ], and the Profile of Mood States (POMS), are often used to manage and monitor emotional changes [ 11 ]. Due to the sudden spread of COVID-19 and drastic societal changes, emotions are highly susceptible to external influences and are closely related to behavior during the pandemic [ 12 ]. To reduce the transmission rate of the novel coronavirus, various personal protective measures and policies aimed at reducing gatherings may pose challenges in measuring emotions [ 13 ]. Therefore, simple methods such as voice information or facial expressions may no longer be suitable for emotion monitoring in psychiatry, and perhaps social media could serve as an important source of data [ 14 , 15 ]. Combining emotional data with mobile phone movement data and linking policies with human behavior can reveal the immense potential of multimodal data in emotion detection [ 16 ].

Currently, several intelligent monitoring tools can provide standardized responses to language or behavior and help individuals understand the emotions underlying specific actions [ 17 ]. Advancements in wearable devices, mobile terminals, and the Internet of Things (IoT) have provided more efficient multidimensional applications for intelligent emotional monitoring. These methods, which are based on ecological momentary assessment techniques, play an important role in reminding patients to perform self-monitoring [ 18 ]. The integration of momentary assessment and sensor data holds significant potential for clinical research and treatment. Sandstrom (2016), in the context of momentary depression and anxiety assessments, combined behavioral data from GPS, accelerometers, and anonymous call records to reveal clinically relevant psychological and behavioral patterns [ 19 ]. Effectively constructing an emotion classification model using neurophysiological, facial feature, and behavioral data recorded from portable devices, along with machine learning methods, showcases a novel research area.

This study reviews relevant literature from the past decade to delineate current trends and hotspots in emotion recognition technology, elaborating on its practical applications for patients with mental/physical disorders in both hospital and home environments. Emotional monitoring during patients’ diagnosis, intervention, and treatment has been demonstrated to have a certain effect on reducing morbidity and mortality and improving quality of life. Our goal was to assess the importance and practical application of emotion recognition technology in the treatment of patients with psychological/physical illnesses in the development of psychosomatic medicine. The remaining structure is as follows: Sect.  2 provides a detailed account of the process of collecting and selecting articles for this review. Section  3 provides an overview of emotion recognition methods applied in hospitals and home environments, along with an analysis of their development. Section  4 discusses the contributions of emotion recognition technology to patient treatment and healthcare, highlights the positive and negative impacts, and suggests potential future directions for this research. Finally, Sect.  5 offers a summary of the paper.

Material collection and research methods

Retrieval strategy.

A literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 20 ]. MeSH terms in Medline were searched. Three categories of keywords were preliminarily identified based on the research question, namely, emotion, recognition, and patients. Emotional-related MeSH terms such as psychology and mental; recognition-related MeSH terms such as express and survey; and patient-related MeSH terms such as clinical et al. were identified. Searches were conducted in the PubMed, Web of Science, Google Scholar, and IEEE Xplore databases using the Boolean operators “AND” and “OR” to combine keywords. Search data were recorded throughout the process. A review of the initially retrieved articles involved summarizing the index titles and keywords, conducting a secondary collection of free terms in each database, and organizing free terms. The search scope was expanded to obtain more precise or comprehensive results. Three researchers conducted a one-week discussion in July 2023 to finalize the research topic and retrieval strategy. Two trained researchers screened the relevance of article titles and abstracts to the research topic, with cross-checking by another reviewer. The first and corresponding authors performed final full-text reviews of included articles and submitted the results for collective team discussion. The literature search results are shown in Table  1 .

Eligibility criteria

Inclusion criteria.

Papers published in English only.

Research published in 2013–2023.

Studies in which the participants were patients with mental or physical disorders or eligible patient populations were extracted from publicly available databases.

Articles that proposed or developed at least one method, model, procedure or system for emotion monitoring.

Exclusion criteria

Duplicate articles were retrieved from different databases.

Abstracts, conference minutes and reports that could not be obtained by searching or contacting the authors.

Abstracts and original articles that were not related to the topic of the study.

Studies that focused on emotions exhibited by patients in response to external stimuli rather than emotions identified using a certain method or technology.

A manual search was conducted across four databases (see Fig.  1 ). A total of 3736 articles were identified, and their titles and abstracts were transferred to the reference management software EndNote 20. After duplicates were removed ( n  = 502), 3234 unique studies were identified and screened using the inclusion/exclusion criteria. The majority ( n  = 2334) of studies were excluded at the title and abstract screening stage, with an additional 622 excluded during full-text screening. The documents excluded for other reasons included abstracts for which the full text could not be obtained through a search or by contacting authors, conference proceedings, or reports ( n  = 6); studies focusing on patients’ emotional responses to external stimuli ( n  = 221); and low-quality outcome literature based on the CASP assessment ( n  = 7). Finally, 44 articles were selected for review.

figure 1

Flow chart of research screening

Article quality evaluation tool

The Critical Appraisal Skills Programme (CASP) criteria were adapted from the 1994 version of the “Users’ Guides to the Medical Literature” published by the American Medical Association. In this study, the CASP criteria were employed to assess the quality of the studies [ 21 ]. The CASP criteria comprise 10 items, each with three response options: “yes,” “no,” and “unclear.” The greater the number of “Yes” responses is, the greater the quality of the literature. Based on the assessment outcomes, the included literature was categorized into three levels according to quality: high, moderate, and low. To ensure the quality of the systematic review, studies with low appraisal results (i.e., with more than 3 “no” and “unclear” responses) were excluded. The detailed CASP evaluation results can be found in Appendix 1 .

Data extraction

Data were extracted independently by two researchers who were trained in data extraction, and the data were cross-checked by another researcher. Relevant data were manually extracted, including the first author’s name, year of publication, country of publication, title, DOI number, type of research, research method, purpose of the emotion recognition method, emotion recognition technology, data collection device, sample set, application scenarios, modeling foundation, psychological/emotional categories, statistical analysis method, and results.

The bias risks and types assessed in the individual studies included those proposed by the Cochrane Collaboration, such as selection bias, performance bias, detection bias, attrition bias, reporting bias, and other biases [ 22 ]. Any discrepancies or uncertainties related to bias assessment were resolved through discussions between the authors and relevant experts.

Among the 44 selected articles, 24 were experimental studies, 18 were observational studies, and 2 were mixed-methods studies. The data from 10 articles were sourced from public datasets, while the data from 33 articles were obtained through institutional recruitment. The patient populations discussed in these articles included individuals with mental disorders (BD, autism spectrum disorder, depression), neurological conditions (stroke, epilepsy, facial paralysis, facial numbness), cancer, and genetic alopecia.

The primary application scenarios addressed in the selected articles were hospital treatment and home healthcare. Emotional recognition methods predominantly involve the utilization of scales, speech analysis, facial features, physiological signals, or multimodal techniques to construct models and systems. Research has indicated that through clinical validation (diagnosis, intervention, and treatment), certain emotion monitoring devices demonstrated good performance in reducing morbidity and mortality rates and enhancing quality of life [ 23 , 24 ]. For a detailed overview, please refer to Fig.  2 .

figure 2

Overview of the application of emotion recognition methods

Application of emotion recognition methods based on different scenes

Twenty-seven studies focused on hospital applications, 11 studies were conducted in outpatient or home monitoring settings, and the remaining 6 studies indicated applicability across all scenarios.

Application of emotion recognition technology in hospitals

In previous medical practices, most doctors or experts diagnosed patients’ emotional issues by using invasive devices and medical assessments. Automatic emotion recognition methods assist doctors not only in evaluating the overall condition of patients but also in accurately identifying diseases associated with emotional features in real time. In some studies, clinical disease features and emotional characteristics were combined as unique biomarkers that are involved in the clinical diagnostic process. They have also been used to assess patients’ performance during treatment and to aid in implementing psychological intervention therapies [ 25 , 26 ].

In a study involving psychiatric patients, Masulli (2022) introduced a data-driven eye-tracking model [ 25 ]. The focus of this study was on the use of a cross-diagnostic approach to link clinical dimensional scores with eye gaze behavior. A study by Quirien (2022) suggested that the regular use of the European Organization for Research and Treatment of Cancer Core Quality of Life questionnaire (EORTC QLQ-C30) emotional function (EF) scale for screening anxiety and depression symptoms in glioma patients contributes to the early identification of emotional disorders. This practice serves as a foundation for referrals and treatment decisions [ 26 ]. Overall, these studies indicate that psychological care and interventions can enhance patients’ mental well-being within a clinical practice setting. Hence, achieving accurate and efficient emotion recognition and continuous monitoring is the initial step toward improving patients’ conditions.

Application of emotion recognition technology in home environments

In recent years, the development of the IoT has driven rapid advancements in the field of healthcare, leading clinical practitioners to focus on home-centered care models. Medical devices connected through the IoT offer users the opportunity to receive in-home treatment and rehabilitation, thereby alleviating pressure on healthcare systems. The application of emotional recognition methods in the “home health” domain has sparked significant interest among researchers. Faccio (2018) developed an electronic health tool based on the Family Resilience (FaRe) questionnaire aimed at monitoring the emotional state of cancer patients while at home [ 27 ]. This tool not only provides diagnostic criteria for physicians but also allows for the formulation of corresponding intervention measures. Veerbeek (2013) created a web-based psychological monitoring data collection system called “Monitoring the Mental Health of the Elderly” [ 28 ].

These systems exhibit the novelty of coordinated hardware and software. Participants complete questionnaires or scales on smart devices at scheduled times each day while their sleep and behavioral activity are continuously monitored through devices such as pulse oximeters, cameras (smart mobile devices, home surveillance devices, computers, etc.), and wearable devices equipped with sensors (e.g., tracking phone and text message usage, social interactions, and generated movement data). All these data are stored on cloud servers and fed to the backend in real time, facilitating easy access to medical information and monitoring services and ultimately reducing diagnosis time. Overall, the research indicates that the intelligent emotion recognition systems used in home environments must possess smart terminals and home treatment platforms.

Emotion recognition technology based on different patterns

Emotion recognition based on psychometric scales.

Fifteen articles assessed the effectiveness of the scales for emotion recognition, as shown in Table  2 .

Emotional chart tools can assist patients in understanding their medical condition, identifying warning signs of adverse emotional episodes and relapses, and describing the instability of individual emotions. Embedding these scales in monitoring systems and applications based on momentary assessment tools can compensate for the limitations of traditional paper-based emotional chart tools due to environmental constraints. Moreover, retrospective reporting eliminates the impact of inaccurate assessment results caused by factors such as measurement outcomes, cognitive levels, and understanding errors, for example, low compliance and potential recall bias [ 29 , 30 , 31 ].

Tsanas (2016) [ 32 ] indicated that the Automated Monitoring of Symptom Severity (AMoSS) application system, which was embedded in smartphones and based on the mood zoom scale, enabled efficient, long-term, and effective daily emotional monitoring for patients with mood disorders. Throughout the entire process, participants exhibited good compliance, and the data were quantitatively processed and more easily preserved.

Emotion recognition based on speech

Three articles assessed the effectiveness of speech-based emotion recognition, as shown in Table  3 .

Mel-frequency cepstral coefficients (MFCCs) have been widely used in speech-based emotion recognition [ 33 , 34 , 35 , 36 , 37 ]. Several studies have shown that support vector machine (SVM) classifiers can group multidimensional datasets by identifying hyperplanes. Chin KC (2021) used the “MFCC + SVM” approach in their research, and the results showed that the prediction accuracy, positive predictive value, negative predictive value, sensitivity, and specificity were 92.87%, 84.62%, 93.57%, 52.38%, and 98.64%, respectively [ 38 ].

Furthermore, deep convolutional neural networks (DCNNs) group multidimensional datasets by recognizing data features through recursion and iteration. Rejaibi (2022) tested the Distress Analysis Interview Corpus/Wizard-of-Oz (DAIC-WOZ) database, Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, and Anonymized Videos from Diverse countries (AVi-D) dataset using the “MFCC + DCNN” framework, achieving an overall accuracy of 76.27% [ 39 ]. These consistent results demonstrate that speech-based emotion recognition technology has also become an independent and viable application.

Emotion recognition based on facial expression

Twelve articles evaluated the effectiveness of facial expression-based emotion recognition, and the results are shown in Table  4 .

Facial expression-based emotion recognition technology utilizes computer vision and artificial intelligence to identify a person’s psychological emotions [ 40 ]. Rapid and subtle microexpressions are among the most useful external indicators for detecting hidden emotional changes. Ekman annotated static and dynamic expression in microexpression videos within the Facial Action Coding System (FACS) [ 41 ] (related datasets include the Facial Expression Recognition 2013 (FER 2013) dataset [ 42 ] and the Real-world Affective Faces Database (RAF-DB) [ 43 ]).

Convolutional neural networks (CNNs) have the ability to rapidly capture changes in facial position and image scale, and they have made significant advancements in pattern recognition, particularly in tasks such as facial detection [ 44 ] and text recognition [ 45 ]. The visual transformer (ViT) is a powerful artificial intelligence technology capable of recognizing or classifying objects within images [ 46 , 47 ]. As the algorithmic performance of ViT has continued to improve and advance, it has gradually outperformed CNNs on small- and medium-sized image classification datasets [ 41 , 48 ]. Jiayu Ye (2022) proposed a depression vision transformer (Dep-ViT) model to address the facial expression recognition problem in patients with depression. Compared to four other excellent models (the deep-emotion, ResNet, SCN, and ViT models), the Dep-ViT model achieved the highest accuracy [ 49 ].

Emotion recognition based on physiological signals

Three articles assessed the effectiveness of emotion recognition based on physiological signals, and the results are shown in Table  5 .

Physiological signals can provide a relatively objective reflection of an individual’s emotional state, increasing the accuracy of emotion recognition systems based on physiological signals. These physiological signals include galvanic skin response (GSR) signals, electromyographic (EMG) signals, electroencephalogram (EEG) signals, heart rate, and respiration, among others. In their research, Verma Aakash (2018) developed an emotion recognition wearable system based on Arduino for individuals with behavioral disorders [ 50 ]. This system measures skin conductivity using a GSR sensor and skin transparency using a pulse sensor and provides real-time heart rate data.

Gentili’s (2016) research indicated that combining physiological parameters with behavioral data allows for more accurate identification of subtle emotional changes [ 51 ]. Compared to changes in speech and facial expressions, the rhythmic variations in behavioral data are more representative. At present, the available physiological signal data are limited, and it is necessary to establish a complete and high-quality physiological signal database and to explore emotion models based on cognitive mechanisms combined with physiological signals.

Emotion recognition based on multimodality

Ten articles assessed the effectiveness of multimodal emotion recognition, and the results are shown in Table  6 .

In previous research, most emotion recognition technologies relied primarily on single modalities and lacked multiple-dimensional parameters. An increasing number of studies are developing more comprehensive and optimized emotion recognition systems by incorporating various forms of data, such as psychometric questionnaire, audio signal, facial expression, EEG, and electrocardiogram (ECG) data. Hossain (2016) achieved a high recognition rate of 99.4% in a patient emotion recognition system based on a Gaussian mixture model (GMM) by combining facial expressions and audio signals [ 52 ]. In Yuying Tong’s (2020) work, a method that combines EEG and facial expression features to identify the emotions of patients with depression was proposed. This research validated the effectiveness of facial expression classification for different emotions in patients with depression and showed significant accuracy through repeated measurements [ 53 ].

Different clinical applications of emotion recognition

Emotional recognition technology has various applications in the clinical field, positively impacting clinical research and leading to precise diagnoses, interventions, and treatments, with the potential to enhance patients’ mental health and treatment outcomes. The results are summarized in Table  7 .

Modern healthcare and nursing prioritize not only fundamental medical treatment but also psychological therapy. Clinical practitioners and healthcare professionals utilize extensive emotional monitoring data to facilitate their understanding of clinical outcomes. Research indicates that rapid psychological diagnostic results can be obtained through smart applications and instantaneous assessment techniques [ 54 , 55 ]. This not only addresses the challenges faced by patients who must travel long distances for medical consultations but also streamlines the medical consultation process.

Clinical doctors can use emotional monitoring data to formulate coping strategies and relapse prevention plans. Research has shown that through self-monitoring and labeling emotional behaviors, patients can gain a better understanding of their emotions and take measures to prevent more severe emotional issues, thereby improving their mental health outcomes [ 56 ].

In the past decade, the field of intelligent emotion recognition has attracted the interest of numerous researchers, leading to the development of various methods based on single or multimodal approaches to effectively identify patients’ emotional states. The recognition of patients’ emotions plays a crucial role in healthcare, including in psychological counseling [ 57 ], anxiety and stress assessments [ 28 ], and pain assessments [ 52 ].

Evaluation of the application of emotion recognition methods

A comprehensive intelligent healthcare system enables patients to receive real-time condition monitoring, timely diagnosis and effective treatment. Through intelligent devices based on cloud computing and the IoT, patients’ emotions can be rapidly and accurately identified, with notifications sent to healthcare professionals to ensure patient safety. In addition, cloud data centers can provide data storage services, data analysis, and audiovisual data processing. These patients offer secure access to healthcare professionals when they need to evaluate patients’ emotional states [ 58 ]. Emotion recognition has evolved from initially targeting patients with mental disorders (such as depression and BD) to encompassing patients with neurological conditions (such as cerebrovascular diseases, peripheral neuropathies, and spinal cord lesions). The most extensively studied applications of emotion recognition in these patients are among patients with conditions such as epilepsy, stroke, facial paralysis, facial numbness, and coma. The common feature of such patients is that they cannot express real emotions through objective external features (such as language and facial expressions) and autonomous behaviors. Therefore, it is necessary to design an automated system to effectively detect the emotions of such patients.

Methods based on neural networks and facial features have shown good performance in recognizing the emotions of patients with facial paralysis and are highly valuable in the medical field [ 59 ]. Furthermore, it is important for healthcare professionals to consider disease severity, as the extent of organ damage can affect the ability to recognize emotions and feelings. Researchers strive to ensure that any recognition system can identify these behaviors effectively. In addition to identifying basic emotions (anger, disgust, fear, happiness, sadness, and surprise), it is important to consider the intensity of these emotions. This understanding can help healthcare professionals anticipate patients’ concerns and stress levels, facilitating appropriate treatment. EEG research has indicated that patients with depression exhibit hemispheric asymmetry in brain signals, and their EEGs show regular variations [ 53 ]. Continuous emotional monitoring can provide insights into the patterns of emotional fluctuations in patients, and comprehensive psychological interventions may be beneficial for the recovery of patients with depression. Emotional recognition systems based on cloud computing and the IoT can, to some extent, address the following four major healthcare issues for patients with emotional disturbances: the shortage of healthcare professionals, long outpatient waiting times, the inability to detect changes in patient emotions early, and the increase in additional treatment costs. Consequently, these systems can support higher-quality healthcare services, thereby enhancing patient care and treatment experiences.

Evaluation of emotion recognition technology

Positive and negative effects of emotion recognition technology.

The use of emotion recognition technology in healthcare offers numerous advantages. First, this technology provides a quick and convenient method for conducting emotional tests through smart devices, eliminating the delays associated with traditional paper questionnaires and increasing user compliance. Second, it enables continuous monitoring of emotional states, aiding in disease understanding and the identification of factors affecting emotions and early warnings of disease progression or relapse, thereby enhancing patient treatment and quality of life. Additionally, patients can provide timely feedback without treatment interruption, helping healthcare professionals gain a timelier understanding of their conditions and offer necessary support. Furthermore, this technology automates data storage and processing, making it easier for healthcare professionals to access and analyze patient emotion information, thereby enhancing treatment personalization. Finally, incorporating multimedia elements into emotion tests improves user engagement, ultimately enhancing the user experience and increasing participation and compliance rates.

However, there are notable concerns associated with the use of emotion recognition technology in healthcare. First, long-term emotional monitoring can put pressure on patients, especially when they are required to complete daily emotional questionnaires at specific times, potentially affecting their participation and willingness to cooperate. Second, privacy concerns loom large as patients worry that the technology could compromise their personal privacy, particularly when it relates to emotional and mental health issues, leading some patients to adopt a cautious approach and withhold information regarding their true emotional states. Additionally, an excessive range of features and options in emotion recognition applications may overwhelm patients, diverting their attention and hindering their ability to focus on the primary goal of emotional monitoring, thereby diminishing the effectiveness of these applications. These concerns necessitate careful consideration of patient well-being and privacy in the implementation of this technology.

Limitations of emotion recognition technology

The implementation of emotion recognition technology in healthcare involves several challenges. First, data access is a critical issue, as the early stages of technology development demand a substantial amount of data for training predictive and decision models. While public databases are widely used by researchers, common problems with research datasets, such as data imbalance and limited dataset size, can lead to disparities between the data used for training and experimentation. Second, cost is a significant consideration. While medical technology aims to reduce costs, the mining, storage, and analysis of data, along with human resource and hardware utilization, can be financially burdensome. Third, cultural differences pose challenges. Older individuals may lack access to or be unwilling to use smart devices, and participants’ engagement with online monitoring systems may vary in terms of time and extent. Additionally, differences in education levels may impact the quality of the data generated and necessitate validation efforts. Finally, there is a notable lack of consensus on security, ethics, and privacy concerns in this context, further complicating the implementation of emotion recognition technology in healthcare. Addressing these challenges is essential for harnessing the full potential of this technology while ensuring patient privacy, data quality, and cost-effectiveness.

Future development direction

In healthcare systems and health services, automatic emotion recognition technology is already being used to monitor the conditions of patients with mental health disorders. However, the future development of this technology will not only focus on psychological conditions such as depression and anxiety but also expand to monitor the severity of diseases and conditions such as cognitive impairment.

To advance emotion recognition technology, we need to overcome the limitations of currently available methods, which primarily involve the combination of questionnaires, speech analysis, facial expressions, and physiological signals. Instead, we should consider integrating a broader range of modalities to achieve more precise emotion recognition. This innovation might include incorporating data from other sensory inputs, such as touch and taste, as well as textual and image data from social media. Furthermore, as artificial intelligence and machine learning continue to advance, emotion recognition technology should move toward automation and real-time capabilities. This shift will aid in providing more personalized and immediate healthcare services, assisting patients in better managing their emotional well-being. Finally, issues related to security, ethics, and privacy remain areas that require further research and attention. It is essential to ensure that the development of emotion recognition technology complies with ethical and legal requirements while safeguarding patient privacy and data security.

Limitations

The limitations of this study mainly lie in the review process and the assessment criteria. During the review process, our study scope may have been constrained by the limitations of the search strategy used during the literature retrieval. Although we made efforts to cover as wide a range of literature as possible, there may still be cases where some relevant studies were overlooked. We were limited to four databases and manually searched English-language literature published in the past decade to observe and evaluate the latest international research results on this topic. However, we cannot determine whether research conducted before this time frame or in other languages or databases might contain more recent research findings. During the evaluation process, it was noted that some of the selected studies lacked sufficient detail or robustness in terms of system performance. We acknowledge that such studies may lack of representative significance. From the perspective of reviewers, the attractiveness of research methods and the novelty of performance sometimes take precedence. Although the extensive heterogeneity of the results prevented us from conducting a meta-analysis, we were able to synthesize data from many studies using a comprehensive approach with robust analytical processes, encompassing a range of different study designs. Furthermore, the included studies were assessed by the reviewers as moderate to high quality, which strengthens the conclusions that can be drawn from the synthesized results. In summary, we took measures to ensure that our search strategy was as robust as possible.

This study elaborated on the potential role of emotions in disease diagnosis and treatment. Emotional recognition technology based on intelligent devices and models can support the design and implementation of emotion recognition and intervention measures. By collecting patients’ physiological signals through intelligent devices and conducting real-time analysis with emotion recognition models, healthcare professionals can better understand patients’ psychological states, guiding the formulation of diagnosis and treatment plans. Real-time monitoring of patient emotions can also serve as an indicator for assessing treatment efficacy, providing a reference for optimizing and adjusting treatment plans and thereby improving patient satisfaction and recovery rates. Most studies were conducted when patients were in a static state and had sufficient time for testing. In dynamic or complex environments, continuous emotion recognition technology for addressing emotional changes still requires further research and improvement. This includes but is not limited to the following aspects. First, it is necessary to improve the robustness of emotion recognition models so that they can effectively recognize emotions in complex environments, such as noise interference and motion interference. Second, it is necessary to further explore and develop emotion recognition methods based on multimodal data that combine multiple information sources, such as physiological signals, speech, and body movements, to improve the accuracy and reliability of emotion recognition. This is an important area for future development.

Data availability

No datasets were generated or analysed during the current study.

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    Competency frameworks inform academic and professional development training, support performance evaluation, and identify professional development needs. The aim of this research was to systematically identify and examine trends in the extent, nature, and range of the literature related to developing competencies in public health.

  22. Application of practice-based learning and improvement in standardized

    In the context of standardized training for general practitioners, the emphasis is still primarily on clinical skills, which does not fully encompass the overall development of general practitioners. This study implemented a practice-based learning and improvement (PBLI) project among students and evaluated its effectiveness based on indicators such as learning outcomes, students' subjective ...

  23. The Importance of Access to Comprehensive Sex Education

    Human development, including anatomy, puberty, body image, ... A 2021 review of the literature found that comprehensive sex education programs that use a positive, ... and withhold critical information that youth need for healthy sexual and relationship development. Need for resources and training:

  24. Practice-Based Coaching (PBC)

    Narrator: Practice-based coaching is a cyclical professional development strategy that can be used on its own or in conjunction with other forms of professional development to support practitioners in a variety of settings with a goal of implementing a defined practice to achieve best outcomes for children. [Inaudible] Close

  25. (PDF) Literature review on staff training and development

    Based on a literature review and a survey of existing programs, this preventive outreach program emphasizes staff training in order to prepare college staff to implement a variety of outreach ...

  26. 10 Essential Managerial Skills and How to Develop Them

    Attend industry-related training, conferences, and workshops. Practice skills like active listening, delegating, and organization. Join a public speaking group or take a public speaking or business writing class. Look for opportunities to be a leader at work, home, class, or through volunteering or sports.

  27. Using transfer learning-based plant disease classification and

    Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's "Zero Hunger," "Climate Action," and "Responsible Consumption and Production" sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the ...

  28. Development and application of emotion recognition technology

    There is a mutual influence between emotions and diseases. Thus, the subject of emotions has gained increasing attention. The primary objective of this study was to conduct a comprehensive review of the developments in emotion recognition technology over the past decade. This review aimed to gain insights into the trends and real-world effects of emotion recognition technology by examining its ...