Most-read articles are from the articles published in 2023 during the last three month.
Reviews
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How to review and assess a systematic review and meta-analysis article: a methodological study (secondary publication)
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Seung-Kwon Myung
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J Educ Eval Health Prof. 2023;20:24. Published online August 27, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.24
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11,798
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826
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13
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Abstract
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Supplementary Material
- Systematic reviews and meta-analyses have become central in many research fields, particularly medicine. They offer the highest level of evidence in evidence-based medicine and support the development and revision of clinical practice guidelines, which offer recommendations for clinicians caring for patients with specific diseases and conditions. This review summarizes the concepts of systematic reviews and meta-analyses and provides guidance on reviewing and assessing such papers. A systematic review refers to a review of a research question that uses explicit and systematic methods to identify, select, and critically appraise relevant research. In contrast, a meta-analysis is a quantitative statistical analysis that combines individual results on the same research question to estimate the common or mean effect. Conducting a meta-analysis involves defining a research topic, selecting a study design, searching literature in electronic databases, selecting relevant studies, and conducting the analysis. One can assess the findings of a meta-analysis by interpreting a forest plot and a funnel plot and by examining heterogeneity. When reviewing systematic reviews and meta-analyses, several essential points must be considered, including the originality and significance of the work, the comprehensiveness of the database search, the selection of studies based on inclusion and exclusion criteria, subgroup analyses by various factors, and the interpretation of the results based on the levels of evidence. This review will provide readers with helpful guidance to help them read, understand, and evaluate these articles.
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Citations
Citations to this article as recorded by

- Testing the distinction between sadism and psychopathy: A metanalysis
Bruno Bonfá-Araujo, Gisele Magarotto Machado, Ariela Raissa Lima-Costa, Fernanda Otoni, Mahnoor Nadeem, Peter K. Jonason
Personality and Individual Differences.2025; 235: 112973. CrossRef - Impact of peripheral immune cells in experimental neonatal hypoxia-ischemia: A systematic review and meta-analysis
Ricardo Ribeiro Nunes, Luz Elena Durán-Carabali, Nícolas Heller Ribeiro, Dienifer Hermann Sirena, Isadora D’Ávila Tassinari, Carlos Alexandre Netto, Ana Helena Paz, Luciano Stürmer de Fraga
International Immunopharmacology.2025; 145: 113682. CrossRef - Systematic review and meta-analysis of the prevalence of frailty and pre-frailty amongst older hospital inpatients in low- and middle-income countries
Sean Lawlor Davidson, Jim Lee, Luke Emmence, Emily Bickerstaff, George Rayers, Elizabeth Davidson, Jenny Richardson, Heather Anderson, Richard Walker, Catherine Dotchin
Age and Ageing.2025;[Epub] CrossRef - Effect of Motivational Interviewing and Exercise on Chronic Low Back Pain: A Systematic Review and Meta‐Analysis
Olayinka Akinrolie, Uchechukwu B. Abioke, Francis O. Kolawole, Nicole Askin, Ebuka M. Anieto, Serena A. Itua, Oluwatoyin G. Akin, Blessing Eromosele, Opeyemi A. Idowu, Henrietta O. Fawole
Musculoskeletal Care.2025;[Epub] CrossRef - Smoking and Risk of Fatty Liver Disease: A Meta-Analysis of Cohort Studies
Moonhyung Lee, Seung-Kwon Myung, Sang Hee Lee, Yoosoo Chang
Gastroenterology Insights.2025; 16(1): 1. CrossRef - The Influence of Study Quality, Age, and Geographic Factors on PCOS Prevalence—A Systematic Review and Meta-analysis
Mina Amiri, Sana Hatoum, Richard P Buyalos, Ali Sheidaei, Ricardo Azziz
The Journal of Clinical Endocrinology & Metabolism.2025;[Epub] CrossRef - The Role of BIM in Managing Risks in Sustainability of Bridge Projects: A Systematic Review with Meta-Analysis
Dema Munef Ahmad, László Gáspár, Zsolt Bencze, Rana Ahmad Maya
Sustainability.2024; 16(3): 1242. CrossRef - The association between long noncoding RNA ABHD11-AS1 and malignancy prognosis: a meta-analysis
Guangyao Lin, Tao Ye, Jing Wang
BMC Cancer.2024;[Epub] CrossRef - The impact of indoor carbon dioxide exposure on human brain activity: A systematic review and meta-analysis based on studies utilizing electroencephalogram signals
Nan Zhang, Chao Liu, Caixia Hou, Wenhao Wang, Qianhui Yuan, Weijun Gao
Building and Environment.2024; 259: 111687. CrossRef - Efficacy of mechanical debridement with adjunct antimicrobial photodynamic therapy against peri-implant subgingival oral yeasts colonization: A systematic review and meta-analysis
Dena Ali, Jenna Alsalman
Photodiagnosis and Photodynamic Therapy.2024; 50: 104399. CrossRef - The effectiveness and usability of online, group-based interventions for people with severe obesity: a systematic review and meta-analysis
Madison Milne-Ives, Lorna Burns, Dawn Swancutt, Raff Calitri, Ananya Ananthakrishnan, Helene Davis, Jonathan Pinkney, Mark Tarrant, Edward Meinert
International Journal of Obesity.2024;[Epub] CrossRef - Non-invasive brain stimulation enhances motor and cognitive performances during dual tasks in patients with Parkinson’s disease: a systematic review and meta-analysis
Hajun Lee, Beom Jin Choi, Nyeonju Kang
Journal of NeuroEngineering and Rehabilitation.2024;[Epub] CrossRef
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Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review
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Tae Won Kim
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J Educ Eval Health Prof. 2023;20:38. Published online December 27, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.38
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10,112
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1,085
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15
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Abstract
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Supplementary Material
- This study aims to explore ChatGPT’s (GPT-3.5 version) functionalities, including reinforcement learning, diverse applications, and limitations. ChatGPT is an artificial intelligence (AI) chatbot powered by OpenAI’s Generative Pre-trained Transformer (GPT) model. The chatbot’s applications span education, programming, content generation, and more, demonstrating its versatility. ChatGPT can improve education by creating assignments and offering personalized feedback, as shown by its notable performance in medical exams and the United States Medical Licensing Exam. However, concerns include plagiarism, reliability, and educational disparities. It aids in various research tasks, from design to writing, and has shown proficiency in summarizing and suggesting titles. Its use in scientific writing and language translation is promising, but professional oversight is needed for accuracy and originality. It assists in programming tasks like writing code, debugging, and guiding installation and updates. It offers diverse applications, from cheering up individuals to generating creative content like essays, news articles, and business plans. Unlike search engines, ChatGPT provides interactive, generative responses and understands context, making it more akin to human conversation, in contrast to conventional search engines’ keyword-based, non-interactive nature. ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges. Nonetheless, ChatGPT is considered to be a transformative AI tool poised to redefine the future of generative technology. In conclusion, advancements in AI, such as ChatGPT, are altering how knowledge is acquired and applied, marking a shift from search engines to creativity engines. This transformation highlights the increasing importance of AI literacy and the ability to effectively utilize AI in various domains of life.
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Citations
Citations to this article as recorded by

- The Development and Validation of an Artificial Intelligence Chatbot Dependence Scale
Xing Zhang, Mingyue Yin, Mingyang Zhang, Zhaoqian Li, Hansen Li
Cyberpsychology, Behavior, and Social Networking.2025; 28(2): 126. CrossRef - Readability, quality and accuracy of generative artificial intelligence chatbots for commonly asked questions about labor epidurals: a comparison of ChatGPT and Bard
D. Lee, M. Brown, J. Hammond, M. Zakowski
International Journal of Obstetric Anesthesia.2025; 61: 104317. CrossRef - ChatGPT-4 Performance on German Continuing Medical Education—Friend or Foe (Trick or Treat)? Protocol for a Randomized Controlled Trial
Christian Burisch, Abhav Bellary, Frank Breuckmann, Jan Ehlers, Serge C Thal, Timur Sellmann, Daniel Gödde
JMIR Research Protocols.2025; 14: e63887. CrossRef - The effect of incorporating large language models into the teaching on critical thinking disposition: An “AI + Constructivism Learning Theory” attempt
Peng Wang, Kexin Yin, Mingzhu Zhang, Yuanxin Zheng, Tong Zhang, Yanjun Kang, Xun Feng
Education and Information Technologies.2025;[Epub] CrossRef - The Impact of Adaptive Learning Technologies, Personalized Feedback, and Interactive AI Tools on Student Engagement: The Moderating Role of Digital Literacy
Husam Yaseen, Abdelaziz Saleh Mohammad, Najwa Ashal, Hesham Abusaimeh, Ahmad Ali, Abdel-Aziz Ahmad Sharabati
Sustainability.2025; 17(3): 1133. CrossRef - Artificial Intelligence in Nursing: New Opportunities and Challenges
Estel·la Ramírez‐Baraldes, Daniel García‐Gutiérrez, Cristina García‐Salido
European Journal of Education.2025;[Epub] CrossRef - Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
Xiaojun Xu, Yixiao Chen, Jing Miao
Journal of Educational Evaluation for Health Professions.2024; 21: 6. CrossRef - Artificial Intelligence: Fundamentals and Breakthrough Applications in Epilepsy
Wesley Kerr, Sandra Acosta, Patrick Kwan, Gregory Worrell, Mohamad A. Mikati
Epilepsy Currents.2024;[Epub] CrossRef - A Developed Graphical User Interface-Based on Different Generative Pre-trained Transformers Models
Ekrem Küçük, İpek Balıkçı Çiçek, Zeynep Küçükakçalı, Cihan Yetiş, Cemil Çolak
ODÜ Tıp Dergisi.2024; 11(1): 18. CrossRef - Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases
Mohamad-Hani Temsah, Abdullah N. Alhuzaimi, Mohammed Almansour, Fadi Aljamaan, Khalid Alhasan, Munirah A. Batarfi, Ibraheem Altamimi, Amani Alharbi, Adel Abdulaziz Alsuhaibani, Leena Alwakeel, Abdulrahman Abdulkhaliq Alzahrani, Khaled B. Alsulaim, Amr Jam
Journal of Medical Systems.2024;[Epub] CrossRef - Authentic assessment in medical education: exploring AI integration and student-as-partners collaboration
Syeda Sadia Fatima, Nabeel Ashfaque Sheikh, Athar Osama
Postgraduate Medical Journal.2024; 100(1190): 959. CrossRef - Comparative performance analysis of large language models: ChatGPT-3.5, ChatGPT-4 and Google Gemini in glucocorticoid-induced osteoporosis
Linjian Tong, Chaoyang Zhang, Rui Liu, Jia Yang, Zhiming Sun
Journal of Orthopaedic Surgery and Research.2024;[Epub] CrossRef - Can AI-Generated Clinical Vignettes in Japanese Be Used Medically and Linguistically?
Yasutaka Yanagita, Daiki Yokokawa, Shun Uchida, Yu Li, Takanori Uehara, Masatomi Ikusaka
Journal of General Internal Medicine.2024; 39(16): 3282. CrossRef - ChatGPT vs. sleep disorder specialist responses to common sleep queries: Ratings by experts and laypeople
Jiyoung Kim, Seo-Young Lee, Jee Hyun Kim, Dong-Hyeon Shin, Eun Hye Oh, Jin A Kim, Jae Wook Cho
Sleep Health.2024; 10(6): 665. CrossRef - Technology integration into Chinese as a foreign language learning in higher education: An integrated bibliometric analysis and systematic review (2000–2024)
Binze Xu
Language Teaching Research.2024;[Epub] CrossRef - The Transformative Power of Generative Artificial Intelligence for Achieving the Sustainable Development Goal of Quality Education
Prema Nedungadi, Kai-Yu Tang, Raghu Raman
Sustainability.2024; 16(22): 9779. CrossRef - Is AI the new course creator
Sheri Conklin, Tom Dorgan, Daisyane Barreto
Discover Education.2024;[Epub] CrossRef
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Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
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Xiaojun Xu
, Yixiao Chen
, Jing Miao
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J Educ Eval Health Prof. 2024;21:6. Published online March 15, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.6
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6,222
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Abstract
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Supplementary Material
- Background
ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored.
Methods
A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included.
Results
ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students.
Conclusion
ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.
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Citations
Citations to this article as recorded by

- AI-assisted patient education: Challenges and solutions in pediatric kidney transplantation
MZ Ihsan, Dony Apriatama, Pithriani, Riza Amalia
Patient Education and Counseling.2025; 131: 108575. CrossRef - Exploring predictors of AI chatbot usage intensity among students: Within- and between-person relationships based on the technology acceptance model
Anne-Kathrin Kleine, Insa Schaffernak, Eva Lermer
Computers in Human Behavior: Artificial Humans.2025; 3: 100113. CrossRef - Chatbots in neurology and neuroscience: Interactions with students, patients and neurologists
Stefano Sandrone
Brain Disorders.2024; 15: 100145. CrossRef - ChatGPT in education: unveiling frontiers and future directions through systematic literature review and bibliometric analysis
Buddhini Amarathunga
Asian Education and Development Studies.2024; 13(5): 412. CrossRef - Evaluating the performance of ChatGPT-3.5 and ChatGPT-4 on the Taiwan plastic surgery board examination
Ching-Hua Hsieh, Hsiao-Yun Hsieh, Hui-Ping Lin
Heliyon.2024; 10(14): e34851. CrossRef - Preparing for Artificial General Intelligence (AGI) in Health Professions Education: AMEE Guide No. 172
Ken Masters, Anne Herrmann-Werner, Teresa Festl-Wietek, David Taylor
Medical Teacher.2024; 46(10): 1258. CrossRef - A Comparative Analysis of ChatGPT and Medical Faculty Graduates in Medical Specialization Exams: Uncovering the Potential of Artificial Intelligence in Medical Education
Gülcan Gencer, Kerem Gencer
Cureus.2024;[Epub] CrossRef - Research ethics and issues regarding the use of ChatGPT-like artificial intelligence platforms by authors and reviewers: a narrative review
Sang-Jun Kim
Science Editing.2024; 11(2): 96. CrossRef - Innovation Off the Bat: Bridging the ChatGPT Gap in Digital Competence among English as a Foreign Language Teachers
Gulsara Urazbayeva, Raisa Kussainova, Aikumis Aibergen, Assel Kaliyeva, Gulnur Kantayeva
Education Sciences.2024; 14(9): 946. CrossRef - Exploring the perceptions of Chinese pre-service teachers on the integration of generative AI in English language teaching: Benefits, challenges, and educational implications
Ji Young Chung, Seung-Hoon Jeong
Online Journal of Communication and Media Technologies.2024; 14(4): e202457. CrossRef - Unveiling the bright side and dark side of AI-based ChatGPT : a bibliographic and thematic approach
Chandan Kumar Tiwari, Mohd. Abass Bhat, Abel Dula Wedajo, Shagufta Tariq Khan
Journal of Decision Systems.2024; : 1. CrossRef - Artificial Intelligence in Medical Education and Mentoring in Rehabilitation Medicine
Julie K. Silver, Mustafa Reha Dodurgali, Nara Gavini
American Journal of Physical Medicine & Rehabilitation.2024; 103(11): 1039. CrossRef - The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education
Sauliha Rabia Alli, Soaad Qahhār Hossain, Sunit Das, Ross Upshur
JMIR Medical Education.2024; 10: e51446. CrossRef - A Systematic Literature Review of Empirical Research on Applying Generative Artificial Intelligence in Education
Xin Zhang, Peng Zhang, Yuan Shen, Min Liu, Qiong Wang, Dragan Gašević, Yizhou Fan
Frontiers of Digital Education.2024; 1(3): 223. CrossRef - Artificial intelligence in medical problem-based learning: opportunities and challenges
Yaoxing Chen, Hong Qi, Yu Qiu, Juan Li, Liang Zhu, Xiaoling Gao, Hao Wang, Gan Jiang
Global Medical Education.2024;[Epub] CrossRef
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Immersive simulation in nursing and midwifery education: a systematic review
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Lahoucine Ben Yahya
, Aziz Naciri
, Mohamed Radid
, Ghizlane Chemsi
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J Educ Eval Health Prof. 2024;21:19. Published online August 8, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.19
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3,400
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Abstract
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Supplementary Material
- Purpose
Immersive simulation is an innovative training approach in health education that enhances student learning. This study examined its impact on engagement, motivation, and academic performance in nursing and midwifery students.
Methods
A comprehensive systematic search was meticulously conducted in 4 reputable databases—Scopus, PubMed, Web of Science, and Science Direct—following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The research protocol was pre-registered in the PROSPERO registry, ensuring transparency and rigor. The quality of the included studies was assessed using the Medical Education Research Study Quality Instrument.
Results
Out of 90 identified studies, 11 were included in the present review, involving 1,090 participants. Four out of 5 studies observed high post-test engagement scores in the intervention groups. Additionally, 5 out of 6 studies that evaluated motivation found higher post-test motivational scores in the intervention groups than in control groups using traditional approaches. Furthermore, among the 8 out of 11 studies that evaluated academic performance during immersive simulation training, 5 reported significant differences (P<0.001) in favor of the students in the intervention groups.
Conclusion
Immersive simulation, as demonstrated by this study, has a significant potential to enhance student engagement, motivation, and academic performance, surpassing traditional teaching methods. This potential underscores the urgent need for future research in various contexts to better integrate this innovative educational approach into nursing and midwifery education curricula, inspiring hope for improved teaching methods.
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Citations
Citations to this article as recorded by

- Application of Virtual Reality, Artificial Intelligence, and Other Innovative Technologies in Healthcare Education (Nursing and Midwifery Specialties): Challenges and Strategies
Galya Georgieva-Tsaneva, Ivanichka Serbezova, Silvia Beloeva
Education Sciences.2024; 15(1): 11. CrossRef
Educational/Faculty development material
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The 6 degrees of curriculum integration in medical education in the United States
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Julie Youm
, Jennifer Christner
, Kevin Hittle
, Paul Ko
, Cinda Stone
, Angela D. Blood
, Samara Ginzburg
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J Educ Eval Health Prof. 2024;21:15. Published online June 13, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.15
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Supplementary Material
- Despite explicit expectations and accreditation requirements for integrated curriculum, there needs to be more clarity around an accepted common definition, best practices for implementation, and criteria for successful curriculum integration. To address the lack of consensus surrounding integration, we reviewed the literature and herein propose a definition for curriculum integration for the medical education audience. We further believe that medical education is ready to move beyond “horizontal” (1-dimensional) and “vertical” (2-dimensional) integration and propose a model of “6 degrees of curriculum integration” to expand the 2-dimensional concept for future designs of medical education programs and best prepare learners to meet the needs of patients. These 6 degrees include: interdisciplinary, timing and sequencing, instruction and assessment, incorporation of basic and clinical sciences, knowledge and skills-based competency progression, and graduated responsibilities in patient care. We encourage medical educators to look beyond 2-dimensional integration to this holistic and interconnected representation of curriculum integration.
Review
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Can an artificial intelligence chatbot be the author of a scholarly article?
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Ju Yoen Lee
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J Educ Eval Health Prof. 2023;20:6. Published online February 27, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.6
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11,840
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Abstract
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Supplementary Material
- At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) chatbot with amazing writing ability, caused a great sensation in academia. The chatbot turned out to be very capable, but also capable of deception, and the news broke that several researchers had listed the chatbot (including its earlier version) as co-authors of their academic papers. In response, Nature and Science expressed their position that this chatbot cannot be listed as an author in the papers they publish. Since an AI chatbot is not a human being, in the current legal system, the text automatically generated by an AI chatbot cannot be a copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. Current AI chatbots such as ChatGPT are much more advanced than search engines in that they produce original text, but they still remain at the level of a search engine in that they cannot take responsibility for their writing. For this reason, they also cannot be authors from the perspective of research ethics.
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Citations
Citations to this article as recorded by

- Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey
Jeremy Y Ng, Sharleen G Maduranayagam, Nirekah Suthakar, Amy Li, Cynthia Lokker, Alfonso Iorio, R Brian Haynes, David Moher
The Lancet Digital Health.2025; 7(1): e94. CrossRef - Introducing Our Custom GPT: An Example of the Potential Impact of Personalized GPT Builders on Scientific Writing
Aymen Kabir, Suraj Shah, Alexander Haddad, Daniel M.S. Raper
World Neurosurgery.2025; 193: 461. CrossRef - Integrating Artificial Intelligence in Medical Writing: Balancing Technological Innovation and Human Expertise, with Practical Applications in Lower Extremity Wounds Care
Pak Thaichana, Myo Zin Oo, Gabriel Leiden Thorup, Chayatorn Chansakaow, Supapong Arworn, Kittipan Rerkasem
The International Journal of Lower Extremity Wounds.2025;[Epub] CrossRef - Ethical issues and violations in using chatbots in academic writing and publishing: the answers from ChatGPT
Eren Erkılıç, Ibrahim Cifci
Journal of Multidisciplinary Academic Tourism.2025; 10(1): 111. CrossRef - Risks of abuse of large language models, like ChatGPT, in scientific publishing: Authorship, predatory publishing, and paper mills
Graham Kendall, Jaime A. Teixeira da Silva
Learned Publishing.2024; 37(1): 55. CrossRef - Can ChatGPT be an author? A study of artificial intelligence authorship policies in top academic journals
Brady D. Lund, K.T. Naheem
Learned Publishing.2024; 37(1): 13. CrossRef - Artificial Intelligence–Generated Scientific Literature: A Critical Appraisal
Justyna Zybaczynska, Matthew Norris, Sunjay Modi, Jennifer Brennan, Pooja Jhaveri, Timothy J. Craig, Taha Al-Shaikhly
The Journal of Allergy and Clinical Immunology: In Practice.2024; 12(1): 106. CrossRef - Does Google’s Bard Chatbot perform better than ChatGPT on the European hand surgery exam?
Goetsch Thibaut, Armaghan Dabbagh, Philippe Liverneaux
International Orthopaedics.2024; 48(1): 151. CrossRef - ChatGPT in medical writing: A game-changer or a gimmick?
Shital Sarah Ahaley, Ankita Pandey, Simran Kaur Juneja, Tanvi Suhane Gupta, Sujatha Vijayakumar
Perspectives in Clinical Research.2024; 15(4): 165. CrossRef - A Brief Review of the Efficacy in Artificial Intelligence and Chatbot-Generated Personalized Fitness Regimens
Daniel K. Bays, Cole Verble, Kalyn M. Powers Verble
Strength & Conditioning Journal.2024; 46(4): 485. CrossRef - Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis
Mike Perkins, Jasper Roe
F1000Research.2024; 12: 1398. CrossRef - The Use of Artificial Intelligence in Writing Scientific Review Articles
Melissa A. Kacena, Lilian I. Plotkin, Jill C. Fehrenbacher
Current Osteoporosis Reports.2024; 22(1): 115. CrossRef - Using AI to Write a Review Article Examining the Role of the Nervous System on Skeletal Homeostasis and Fracture Healing
Murad K. Nazzal, Ashlyn J. Morris, Reginald S. Parker, Fletcher A. White, Roman M. Natoli, Jill C. Fehrenbacher, Melissa A. Kacena
Current Osteoporosis Reports.2024; 22(1): 217. CrossRef - GenAI et al.: Cocreation, Authorship, Ownership, Academic Ethics and Integrity in a Time of Generative AI
Aras Bozkurt
Open Praxis.2024; 16(1): 1. CrossRef - An integrative decision-making framework to guide policies on regulating ChatGPT usage
Umar Ali Bukar, Md Shohel Sayeed, Siti Fatimah Abdul Razak, Sumendra Yogarayan, Oluwatosin Ahmed Amodu
PeerJ Computer Science.2024; 10: e1845. CrossRef - Artificial Intelligence and Its Role in Medical Research
Anurag Gola, Ambarish Das, Amar B. Gumataj, S. Amirdhavarshini, J. Venkatachalam
Current Medical Issues.2024; 22(2): 97. CrossRef - From advancements to ethics: Assessing ChatGPT’s role in writing research paper
Vasu Gupta, Fnu Anamika, Kinna Parikh, Meet A Patel, Rahul Jain, Rohit Jain
Turkish Journal of Internal Medicine.2024; 6(2): 74. CrossRef - Yapay Zekânın Edebiyatta Kullanım Serüveni
Nesime Ceyhan Akça, Serap Aslan Cobutoğlu, Özlem Yeşim Özbek, Mehmet Furkan Akça
RumeliDE Dil ve Edebiyat Araştırmaları Dergisi.2024; (39): 283. CrossRef - ChatGPT's Gastrointestinal Tumor Board Tango: A limping dance partner?
Ughur Aghamaliyev, Javad Karimbayli, Clemens Giessen-Jung, Matthias Ilmer, Kristian Unger, Dorian Andrade, Felix O. Hofmann, Maximilian Weniger, Martin K. Angele, C. Benedikt Westphalen, Jens Werner, Bernhard W. Renz
European Journal of Cancer.2024; 205: 114100. CrossRef - Gout and Gout-Related Comorbidities: Insight and Limitations from Population-Based Registers in Sweden
Panagiota Drivelegka, Lennart TH Jacobsson, Mats Dehlin
Gout, Urate, and Crystal Deposition Disease.2024; 2(2): 144. CrossRef - Artificial intelligence in academic cardiothoracic surgery
Adham AHMED, Irbaz HAMEED
The Journal of Cardiovascular Surgery.2024;[Epub] CrossRef - The emergence of generative artificial intelligence platforms in 2023, journal metrics, appreciation to reviewers and volunteers, and obituary
Sun Huh
Journal of Educational Evaluation for Health Professions.2024; 21: 9. CrossRef - A survey of safety and trustworthiness of large language models through the lens of verification and validation
Xiaowei Huang, Wenjie Ruan, Wei Huang, Gaojie Jin, Yi Dong, Changshun Wu, Saddek Bensalem, Ronghui Mu, Yi Qi, Xingyu Zhao, Kaiwen Cai, Yanghao Zhang, Sihao Wu, Peipei Xu, Dengyu Wu, Andre Freitas, Mustafa A. Mustafa
Artificial Intelligence Review.2024;[Epub] CrossRef - Identification of ChatGPT-Generated Abstracts Within Shoulder and Elbow Surgery Poses a Challenge for Reviewers
Ryan D. Stadler, Suleiman Y. Sudah, Michael A. Moverman, Patrick J. Denard, Xavier A. Duralde, Grant E. Garrigues, Christopher S. Klifto, Jonathan C. Levy, Surena Namdari, Joaquin Sanchez-Sotelo, Mariano E. Menendez
Arthroscopy: The Journal of Arthroscopic & Related Surgery.2024;[Epub] CrossRef - Decision-Making Framework for the Utilization of Generative Artificial Intelligence in Education: A Case Study of ChatGPT
Umar Ali Bukar, Md. Shohel Sayeed, Siti Fatimah Abdul Razak, Sumendra Yogarayan, Radhwan Sneesl
IEEE Access.2024; 12: 95368. CrossRef - ChatGPT or Gemini: Who Makes the Better Scientific Writing Assistant?
Hatoon S. AlSagri, Faiza Farhat, Shahab Saquib Sohail, Abdul Khader Jilani Saudagar
Journal of Academic Ethics.2024;[Epub] CrossRef - The Syntax of Smart Writing: Artificial Intelligence Unveiled
Balaji Arumugam, Arun Murugan, Kirubakaran S., Saranya Rajamanickam
International Journal of Preventative & Evidence Based Medicine.2024; : 1. CrossRef - Generative artificial intelligence usage by researchers at work: Effects of gender, career stage, type of workplace, and perceived barriers
Pablo Dorta-González, Alexis Jorge López-Puig, María Isabel Dorta-González, Sara M. González-Betancor
Telematics and Informatics.2024; 94: 102187. CrossRef - Leveraging Artificial Intelligence In Project-Based Service Learning To Advance Sustainable Development: A Pedagogical Approach For Marketing Education
C. M. Dubay, Melanie B. Richards
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Research article
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Mentorship and self-efficacy are associated with lower burnout in physical therapists in the United States: a cross-sectional survey study
-
Matthew Pugliese
, Jean-Michel Brismée
, Brad Allen
, Sean Riley
, Justin Tammany
, Paul Mintken
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J Educ Eval Health Prof. 2023;20:27. Published online September 27, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.27
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6,006
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435
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4
Web of Science
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5
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Abstract
PDF
Supplementary Material
- Purpose
This study investigated the prevalence of burnout in physical therapists in the United States and the relationships between burnout and education, mentorship, and self-efficacy.
Methods
This was a cross-sectional survey study. An electronic survey was distributed to practicing physical therapists across the United States over a 6-week period from December 2020 to January 2021. The survey was completed by 2,813 physical therapists from all states. The majority were female (68.72%), White or Caucasian (80.13%), and employed full-time (77.14%). Respondents completed questions on demographics, education, mentorship, self-efficacy, and burnout. The Burnout Clinical Subtypes Questionnaire 12 (BCSQ-12) and self-reports were used to quantify burnout, and the General Self-Efficacy Scale (GSES) was used to measure self-efficacy. Descriptive and inferential analyses were performed.
Results
Respondents from home health (median BCSQ-12=42.00) and skilled nursing facility settings (median BCSQ-12=42.00) displayed the highest burnout scores. Burnout was significantly lower among those who provided formal mentorship (median BCSQ-12=39.00, P=0.0001) compared to no mentorship (median BCSQ-12=41.00). Respondents who received formal mentorship (median BCSQ-12=38.00, P=0.0028) displayed significantly lower burnout than those who received no mentorship (median BCSQ-12=41.00). A moderate negative correlation (rho=-0.49) was observed between the GSES and burnout scores. A strong positive correlation was found between self-reported burnout status and burnout scores (rrb=0.61).
Conclusion
Burnout is prevalent in the physical therapy profession, as almost half of respondents (49.34%) reported burnout. Providing or receiving mentorship and higher self-efficacy were associated with lower burnout. Organizations should consider measuring burnout levels, investing in mentorship programs, and implementing strategies to improve self-efficacy.
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Citations
Citations to this article as recorded by

- Wellness and Stress Management Practices Among Healthcare Professionals and Health Professional Students
Asli C. Yalim, Katherine Daly, Monica Bailey, Denise Kay, Xiang Zhu, Mohammed Patel, Laurie C. Neely, Desiree A. Díaz, Denyi M. Canario Asencio, Karla Rosario, Melissa Cowan, Magdalena Pasarica
American Journal of Health Promotion.2025; 39(2): 204. CrossRef - Final results of the National Oncology Mentorship Program 2023 and its impact on burnout and professional fulfilment
Udit Nindra, Gowri Shivasabesan, Rhiannon Mellor, Weng Ng, Wei Chua, Deme Karikios, Bethan Richards, Jia Liu
Internal Medicine Journal.2025; 55(2): 233. CrossRef - Incidence of Shared Clinical Instruction in Physical Therapy Clinical Education in the United States
Nicki Silberman, Lori Hochman, Jaya Rachwani
Journal of Physical Therapy Education.2025;[Epub] CrossRef - Interprofessional education to support alcohol use screening and future team-based management of stress-related disorders in vulnerable populations
Taylor Fitzpatrick-Schmidt, Scott Edwards
Frontiers in Education.2024;[Epub] CrossRef - Prevalence of Stress and Burnout in Physical Therapist Clinical Instructors
Ryan J. Pontiff, Peggy Gleeson, Katy Mitchell, Rupal M. Patel
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Brief report
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Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study
-
Sun Huh
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J Educ Eval Health Prof. 2023;20:1. Published online January 11, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.1
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15,074
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1,120
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190
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94
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Abstract
PDF
Supplementary Material
- This study aimed to compare the knowledge and interpretation ability of ChatGPT, a language model of artificial general intelligence, with those of medical students in Korea by administering a parasitology examination to both ChatGPT and medical students. The examination consisted of 79 items and was administered to ChatGPT on January 1, 2023. The examination results were analyzed in terms of ChatGPT’s overall performance score, its correct answer rate by the items’ knowledge level, and the acceptability of its explanations of the items. ChatGPT’s performance was lower than that of the medical students, and ChatGPT’s correct answer rate was not related to the items’ knowledge level. However, there was a relationship between acceptable explanations and correct answers. In conclusion, ChatGPT’s knowledge and interpretation ability for this parasitology examination were not yet comparable to those of medical students in Korea.
-
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Review of Education.2023;[Epub] CrossRef - Evaluating the reliability of ChatGPT as a tool for imaging test referral: a comparative study with a clinical decision support system
Shani Rosen, Mor Saban
European Radiology.2023; 34(5): 2826. CrossRef - The Significance of Artificial Intelligence Platforms in Anatomy Education: An Experience With ChatGPT and Google Bard
Hasan B Ilgaz, Zehra Çelik
Cureus.2023;[Epub] CrossRef - Is ChatGPT’s Knowledge and Interpretative Ability Comparable to First Professional MBBS (Bachelor of Medicine, Bachelor of Surgery) Students of India in Taking a Medical Biochemistry Examination?
Abhra Ghosh, Nandita Maini Jindal, Vikram K Gupta, Ekta Bansal, Navjot Kaur Bajwa, Abhishek Sett
Cureus.2023;[Epub] CrossRef - Ethical consideration of the use of generative artificial intelligence, including ChatGPT in writing a nursing article
Sun Huh
Child Health Nursing Research.2023; 29(4): 249. CrossRef - Potential Use of ChatGPT for Patient Information in Periodontology: A Descriptive Pilot Study
Osman Babayiğit, Zeynep Tastan Eroglu, Dilek Ozkan Sen, Fatma Ucan Yarkac
Cureus.2023;[Epub] CrossRef - Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in medical education in Serbia: a descriptive study
Aleksandra Ignjatović, Lazar Stevanović
Journal of Educational Evaluation for Health Professions.2023; 20: 28. CrossRef - Assessing the Performance of ChatGPT in Medical Biochemistry Using Clinical Case Vignettes: Observational Study
Krishna Mohan Surapaneni
JMIR Medical Education.2023; 9: e47191. CrossRef - Performance of ChatGPT, Bard, Claude, and Bing on the Peruvian National Licensing Medical Examination: a cross-sectional study
Betzy Clariza Torres-Zegarra, Wagner Rios-Garcia, Alvaro Micael Ñaña-Cordova, Karen Fatima Arteaga-Cisneros, Xiomara Cristina Benavente Chalco, Marina Atena Bustamante Ordoñez, Carlos Jesus Gutierrez Rios, Carlos Alberto Ramos Godoy, Kristell Luisa Teresa
Journal of Educational Evaluation for Health Professions.2023; 20: 30. CrossRef - ChatGPT’s performance in German OB/GYN exams – paving the way for AI-enhanced medical education and clinical practice
Maximilian Riedel, Katharina Kaefinger, Antonia Stuehrenberg, Viktoria Ritter, Niklas Amann, Anna Graf, Florian Recker, Evelyn Klein, Marion Kiechle, Fabian Riedel, Bastian Meyer
Frontiers in Medicine.2023;[Epub] CrossRef - Medical students’ patterns of using ChatGPT as a feedback tool and perceptions of ChatGPT in a Leadership and Communication course in Korea: a cross-sectional study
Janghee Park
Journal of Educational Evaluation for Health Professions.2023; 20: 29. CrossRef - FROM TEXT TO DIAGNOSE: CHATGPT’S EFFICACY IN MEDICAL DECISION-MAKING
Yaroslav Mykhalko, Pavlo Kish, Yelyzaveta Rubtsova, Oleksandr Kutsyn, Valentyna Koval
Wiadomości Lekarskie.2023; 76(11): 2345. CrossRef - Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students’ and Physicians’ Perceptions
Pasin Tangadulrat, Supinya Sono, Boonsin Tangtrakulwanich
JMIR Medical Education.2023; 9: e50658. CrossRef - Below average ChatGPT performance in medical microbiology exam compared to university students
Malik Sallam, Khaled Al-Salahat
Frontiers in Education.2023;[Epub] CrossRef - ChatGPT: "To be or not to be" ... in academic research. The human mind's analytical rigor and capacity to discriminate between AI bots' truths and hallucinations
Aurelian Anghelescu, Ilinca Ciobanu, Constantin Munteanu, Lucia Ana Maria Anghelescu, Gelu Onose
Balneo and PRM Research Journal.2023; 14(Vol.14, no): 614. CrossRef - ChatGPT Review: A Sophisticated Chatbot Models in Medical & Health-related Teaching and Learning
Nur Izah Ab Razak, Muhammad Fawwaz Muhammad Yusoff, Rahmita Wirza O.K. Rahmat
Malaysian Journal of Medicine and Health Sciences.2023; 19(s12): 98. CrossRef - Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review
Tae Won Kim
Journal of Educational Evaluation for Health Professions.2023; 20: 38. CrossRef - Trends in research on ChatGPT and adoption-related issues discussed in articles: a narrative review
Sang-Jun Kim
Science Editing.2023; 11(1): 3. CrossRef - Information amount, accuracy, and relevance of generative artificial intelligence platforms’ answers regarding learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study
Hyunju Lee, Soobin Park
Journal of Educational Evaluation for Health Professions.2023; 20: 39. CrossRef - What will ChatGPT revolutionize in the financial industry?
Hassnian Ali, Ahmet Faruk Aysan
Modern Finance.2023; 1(1): 116. CrossRef
Research article
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The effect of simulation-based training on problem-solving skills, critical thinking skills, and self-efficacy among nursing students in Vietnam: a before-and-after study
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Tran Thi Hoang Oanh
, Luu Thi Thuy
, Ngo Thi Thu Huyen
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J Educ Eval Health Prof. 2024;21:24. Published online September 23, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.24
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1,463
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Abstract
PDF
Supplementary Material
- Purpose
This study investigated the effect of simulation-based training on nursing students’ problem-solving skills, critical thinking skills, and self-efficacy.
Methods
A single-group pretest and posttest study was conducted among 173 second-year nursing students at a public university in Vietnam from May 2021 to July 2022. Each student participated in the adult nursing preclinical practice course, which utilized a moderate-fidelity simulation teaching approach. Instruments including the Personal Problem-Solving Inventory Scale, Critical Thinking Skills Questionnaire, and General Self-Efficacy Questionnaire were employed to measure participants’ problem-solving skills, critical thinking skills, and self-efficacy. Data were analyzed using descriptive statistics and the paired-sample t-test with the significance level set at P<0.05.
Results
The mean score of the Personal Problem-Solving Inventory posttest (127.24±12.11) was lower than the pretest score (131.42±16.95), suggesting an improvement in the problem-solving skills of the participants (t172=2.55, P=0.011). There was no statistically significant difference in critical thinking skills between the pretest and posttest (P=0.854). Self-efficacy among nursing students showed a substantial increase from the pretest (27.91±5.26) to the posttest (28.71±3.81), with t172=-2.26 and P=0.025.
Conclusion
The results suggest that simulation-based training can improve problem-solving skills and increase self-efficacy among nursing students. Therefore, the integration of simulation-based training in nursing education is recommended.
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Citations
Citations to this article as recorded by

- The Effect of Work-Based Learning on Employability Skills: The Role of Self-Efficacy and Vocational Identity
Suyitno Suyitno, Muhammad Nurtanto, Dwi Jatmoko, Yuli Widiyono, Riawan Yudi Purwoko, Fuad Abdillah, Setuju Setuju, Yudan Hermawan
European Journal of Educational Research.2025; 14(1): 309. CrossRef - Interactive Success: Empowering Young Minds through Games-Based Learning at NADI PPR Intan Baiduri
Mohamad Zaki Mohamad Saad, Shafinah Kamarudin, Zuraini Zukiffly, Siti Soleha Zuaimi
Progress in Computers and Learning .2025; 2(1): 29. CrossRef
Review
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Attraction and achievement as 2 attributes of gamification in healthcare: an evolutionary concept analysis
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Hyun Kyoung Kim
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J Educ Eval Health Prof. 2024;21:10. Published online April 11, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.10
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2,478
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342
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Abstract
PDF
Supplementary Material
- This study conducted a conceptual analysis of gamification in healthcare utilizing Rogers’ evolutionary concept analysis methodology to identify its attributes and provide a method for its applications in the healthcare field. Gamification has recently been used as a health intervention and education method, but the concept is used inconsistently and confusingly. A literature review was conducted to derive definitions, surrogate terms, antecedents, influencing factors, attributes (characteristics with dimensions and features), related concepts, consequences, implications, and hypotheses from various academic fields. A total of 56 journal articles in English and Korean, published between August 2 and August 7, 2023, were extracted from databases such as PubMed Central, the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery Digital Library, the Research Information Sharing Service, and the Korean Studies Information Service System, using the keywords “gamification” and “healthcare.” These articles were then analyzed. Gamification in healthcare is defined as the application of game elements in health-related contexts to improve health outcomes. The attributes of this concept were categorized into 2 main areas: attraction and achievement. These categories encompass various strategies for synchronization, enjoyable engagement, visual rewards, and goal-reinforcing frames. Through a multidisciplinary analysis of the concept’s attributes and influencing factors, this paper provides practical strategies for implementing gamification in health interventions. When developing a gamification strategy, healthcare providers can reference this analysis to ensure the game elements are used both appropriately and effectively.
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Citations
Citations to this article as recorded by

- Short-Term Impact of Digital Mental Health Interventions on Psychological Well-Being and Blood Sugar Control in Type 2 Diabetes Patients in Riyadh
Abdulaziz M. Alodhialah, Ashwaq A. Almutairi, Mohammed Almutairi
Healthcare.2024; 12(22): 2257. CrossRef
Software report
Special article on the 20th anniversary of the journal
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The irtQ R package: a user-friendly tool for item response theory-based test data analysis and calibration
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Hwanggyu Lim
, Kyungseok Kang
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J Educ Eval Health Prof. 2024;21:23. Published online September 12, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.23
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Abstract
PDF
Supplementary Material
- Computerized adaptive testing (CAT) has become a widely adopted test design for high-stakes licensing and certification exams, particularly in the health professions in the United States, due to its ability to tailor test difficulty in real time, reducing testing time while providing precise ability estimates. A key component of CAT is item response theory (IRT), which facilitates the dynamic selection of items based on examinees' ability levels during a test. Accurate estimation of item and ability parameters is essential for successful CAT implementation, necessitating convenient and reliable software to ensure precise parameter estimation. This paper introduces the irtQ R package (http://CRAN.R-project.org/), which simplifies IRT-based analysis and item calibration under unidimensional IRT models. While it does not directly simulate CAT, it provides essential tools to support CAT development, including parameter estimation using marginal maximum likelihood estimation via the expectation-maximization algorithm, pretest item calibration through fixed item parameter calibration and fixed ability parameter calibration methods, and examinee ability estimation. The package also enables users to compute item and test characteristic curves and information functions necessary for evaluating the psychometric properties of a test. This paper illustrates the key features of the irtQ package through examples using simulated datasets, demonstrating its utility in IRT applications such as test data analysis and ability scoring. By providing a user-friendly environment for IRT analysis, irtQ significantly enhances the capacity for efficient adaptive testing research and operations. Finally, the paper highlights additional core functionalities of irtQ, emphasizing its broader applicability to the development and operation of IRT-based assessments.
Technical report
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Item difficulty index, discrimination index, and reliability of the 26 health professions licensing examinations in 2022, Korea: a psychometric study
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Yoon Hee Kim
, Bo Hyun Kim
, Joonki Kim
, Bokyoung Jung
, Sangyoung Bae
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J Educ Eval Health Prof. 2023;20:31. Published online November 22, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.31
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Abstract
PDF
Supplementary Material
- Purpose
This study presents item analysis results of the 26 health personnel licensing examinations managed by the Korea Health Personnel Licensing Examination Institute (KHPLEI) in 2022.
Methods
The item difficulty index, item discrimination index, and reliability were calculated. The item discrimination index was calculated using a discrimination index based on the upper and lower 27% rule and the item-total correlation.
Results
Out of 468,352 total examinees, 418,887 (89.4%) passed. The pass rates ranged from 27.3% for health educators level 1 to 97.1% for oriental medical doctors. Most examinations had a high average difficulty index, albeit to varying degrees, ranging from 61.3% for prosthetists and orthotists to 83.9% for care workers. The average discrimination index based on the upper and lower 27% rule ranged from 0.17 for oriental medical doctors to 0.38 for radiological technologists. The average item-total correlation ranged from 0.20 for oriental medical doctors to 0.38 for radiological technologists. The Cronbach α, as a measure of reliability, ranged from 0.872 for health educators-level 3 to 0.978 for medical technologists. The correlation coefficient between the average difficulty index and average discrimination index was -0.2452 (P=0.1557), that between the average difficulty index and the average item-total correlation was 0.3502 (P=0.0392), and that between the average discrimination index and the average item-total correlation was 0.7944 (P<0.0001).
Conclusion
This technical report presents the item analysis results and reliability of the recent examinations by the KHPLEI, demonstrating an acceptable range of difficulty index and discrimination index values, as well as good reliability.
Educational/Faculty development material
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The performance of ChatGPT-4.0o in medical imaging evaluation: a cross-sectional study
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Elio Stefan Arruzza
, Carla Marie Evangelista
, Minh Chau
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J Educ Eval Health Prof. 2024;21:29. Published online October 31, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.29
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1,147
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239
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2
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Abstract
PDF
Supplementary Material
- This study investigated the performance of ChatGPT-4.0o in evaluating the quality of positioning in radiographic images. Thirty radiographs depicting a variety of knee, elbow, ankle, hand, pelvis, and shoulder projections were produced using anthropomorphic phantoms and uploaded to ChatGPT-4.0o. The model was prompted to provide a solution to identify any positioning errors with justification and offer improvements. A panel of radiographers assessed the solutions for radiographic quality based on established positioning criteria, with a grading scale of 1–5. In only 20% of projections, ChatGPT-4.0o correctly recognized all errors with justifications and offered correct suggestions for improvement. The most commonly occurring score was 3 (9 cases, 30%), wherein the model recognized at least 1 specific error and provided a correct improvement. The mean score was 2.9. Overall, low accuracy was demonstrated, with most projections receiving only partially correct solutions. The findings reinforce the importance of robust radiography education and clinical experience.
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Citations
Citations to this article as recorded by

- Conversational LLM Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis
Raffaele Pellegrino, Alessandro Federico, Antonietta Gerarda Gravina
Diagnostics.2024; 14(22): 2537. CrossRef - Effectiveness of ChatGPT-4o in developing continuing professional development plans for graduate radiographers: a descriptive study
Minh Chau, Elio Stefan Arruzza, Kelly Spuur
Journal of Educational Evaluation for Health Professions.2024; 21: 34. CrossRef
Research articles
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Effect of a transcultural nursing course on improving the cultural competency of nursing graduate students in Korea: a before-and-after study
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Kyung Eui Bae
, Geum Hee Jeong
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J Educ Eval Health Prof. 2023;20:35. Published online December 4, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.35
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Abstract
PDF
Supplementary Material
- Purpose
This study aimed to evaluate the impact of a transcultural nursing course on enhancing the cultural competency of graduate nursing students in Korea. We hypothesized that participants’ cultural competency would significantly improve in areas such as communication, biocultural ecology and family, dietary habits, death rituals, spirituality, equity, and empowerment and intermediation after completing the course. Furthermore, we assessed the participants’ overall satisfaction with the course.
Methods
A before-and-after study was conducted with graduate nursing students at Hallym University, Chuncheon, Korea, from March to June 2023. A transcultural nursing course was developed based on Giger & Haddad’s transcultural nursing model and Purnell’s theoretical model of cultural competence. Data was collected using a cultural competence scale for registered nurses developed by Kim and his colleagues. A total of 18 students participated, and the paired t-test was employed to compare pre-and post-intervention scores.
Results
The study revealed significant improvements in all 7 categories of cultural nursing competence (P<0.01). Specifically, the mean differences in scores (pre–post) ranged from 0.74 to 1.09 across the categories. Additionally, participants expressed high satisfaction with the course, with an average score of 4.72 out of a maximum of 5.0.
Conclusion
The transcultural nursing course effectively enhanced the cultural competency of graduate nursing students. Such courses are imperative to ensure quality care for the increasing multicultural population in Korea.
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Empirical effect of the Dr Lee Jong-wook Fellowship Program to empower sustainable change for the health workforce in Tanzania: a mixed-methods study
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Masoud Dauda
, Swabaha Aidarus Yusuph
, Harouni Yasini
, Issa Mmbaga
, Perpetua Mwambinngu
, Hansol Park
, Gyeongbae Seo
, Kyoung Kyun Oh
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J Educ Eval Health Prof. 2025;22:6. Published online January 20, 2025
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DOI: https://doi.org/10.3352/jeehp.2025.22.6
[Epub ahead of print]
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Abstract
PDF
Supplementary Material
- Purpose
This study evaluated the Dr Lee Jong-wook Fellowship Program’s impact on Tanzania’s health workforce, focusing on relevance, effectiveness, efficiency, impact, and sustainability in addressing healthcare gaps.
Methods
A mixed-methods research design was employed. Data were collected from 97 out of 140 alumni through an online survey, 35 in-depth interviews, and one focus group discussion. The study was conducted from November to December 2023 and included alumni from 2009 to 2022. Measurement instruments included structured questionnaires for quantitative data and semi-structured guides for qualitative data. Quantitative analysis involved descriptive and inferential statistics (Spearman’s rank correlation, non-parametric tests) using Python ver. 3.11.0 and Stata ver. 14.0. Thematic analysis was employed to analyze qualitative data using NVivo ver. 12.0.
Results
Findings indicated high relevance (mean=91.6, standard deviation [SD]=8.6), effectiveness (mean=86.1, SD=11.2), efficiency (mean=82.7, SD=10.2), and impact (mean=87.7, SD=9.9), with improved skills, confidence, and institutional service quality. However, sustainability had a lower score (mean=58.0, SD=11.1), reflecting challenges in follow-up support and resource allocation. Effectiveness strongly correlated with impact (ρ=0.746, P<0.001). The qualitative findings revealed that participants valued tailored training but highlighted barriers, such as language challenges and insufficient practical components. Alumni-led initiatives contributed to knowledge sharing, but limited resources constrained sustainability.
Conclusion
The Fellowship Program enhanced Tanzania’s health workforce capacity, but it requires localized curricula and strengthened alumni networks for sustainability. These findings provide actionable insights for improving similar programs globally, confirming the hypothesis that tailored training positively influences workforce and institutional outcomes.