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Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
Xiaojun Xu, Yixiao Chen, Jing Miao
J Educ Eval Health Prof. 2024;21:6.   Published online March 15, 2024
DOI: https://doi.org/10.3352/jeehp.2024.21.6    [Epub ahead of print]
  • 126 View
  • 29 Download
AbstractAbstract PDF
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.
Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review
Tae Won Kim
J Educ Eval Health Prof. 2023;20:38.   Published online December 27, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.38
  • 964 View
  • 200 Download
AbstractAbstract PDFSupplementary 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.
How to review and assess a systematic review and meta-analysis article: a methodological study (secondary publication)  
Seung-Kwon Myung
J Educ Eval Health Prof. 2023;20:24.   Published online August 27, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.24
  • 1,831 View
  • 222 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary 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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  • 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
Can an artificial intelligence chatbot be the author of a scholarly article?  
Ju Yoen Lee
J Educ Eval Health Prof. 2023;20:6.   Published online February 27, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.6
  • 6,847 View
  • 612 Download
  • 29 Web of Science
  • 34 Crossref
AbstractAbstract PDFSupplementary 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.

Citations

Citations to this article as recorded by  
  • 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
  • The Role of AI in Writing an Article and Whether it Can Be a Co-author: What if it Gets Support From 2 Different AIs Like ChatGPT and Google Bard for the Same Theme?
    İlhan Bahşi, Ayşe Balat
    Journal of Craniofacial Surgery.2024; 35(1): 274.     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
  • 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;[Epub]     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
  • Universal skepticism of ChatGPT: a review of early literature on chat generative pre-trained transformer
    Casey Watters, Michal K. Lemanski
    Frontiers in Big Data.2023;[Epub]     CrossRef
  • The importance of human supervision in the use of ChatGPT as a support tool in scientific writing
    William Castillo-González
    Metaverse Basic and Applied Research.2023;[Epub]     CrossRef
  • ChatGPT for Future Medical and Dental Research
    Bader Fatani
    Cureus.2023;[Epub]     CrossRef
  • Chatbots in Medical Research
    Punit Sharma
    Clinical Nuclear Medicine.2023; 48(9): 838.     CrossRef
  • Potential applications of ChatGPT in dermatology
    Nicolas Kluger
    Journal of the European Academy of Dermatology and Venereology.2023;[Epub]     CrossRef
  • The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research
    Tariq Alqahtani, Hisham A. Badreldin, Mohammed Alrashed, Abdulrahman I. Alshaya, Sahar S. Alghamdi, Khalid bin Saleh, Shuroug A. Alowais, Omar A. Alshaya, Ishrat Rahman, Majed S. Al Yami, Abdulkareem M. Albekairy
    Research in Social and Administrative Pharmacy.2023; 19(8): 1236.     CrossRef
  • ChatGPT Performance on the American Urological Association Self-assessment Study Program and the Potential Influence of Artificial Intelligence in Urologic Training
    Nicholas A. Deebel, Ryan Terlecki
    Urology.2023; 177: 29.     CrossRef
  • Intelligence or artificial intelligence? More hard problems for authors of Biological Psychology, the neurosciences, and everyone else
    Thomas Ritz
    Biological Psychology.2023; 181: 108590.     CrossRef
  • The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts
    Mohammad Hosseini, David B Resnik, Kristi Holmes
    Research Ethics.2023; 19(4): 449.     CrossRef
  • How trustworthy is ChatGPT? The case of bibliometric analyses
    Faiza Farhat, Shahab Saquib Sohail, Dag Øivind Madsen
    Cogent Engineering.2023;[Epub]     CrossRef
  • Disclosing use of Artificial Intelligence: Promoting transparency in publishing
    Parvaiz A. Koul
    Lung India.2023; 40(5): 401.     CrossRef
  • ChatGPT in medical research: challenging time ahead
    Daideepya C Bhargava, Devendra Jadav, Vikas P Meshram, Tanuj Kanchan
    Medico-Legal Journal.2023; 91(4): 223.     CrossRef
  • Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis
    Mike Perkins, Jasper Roe
    F1000Research.2023; 12: 1398.     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
  • 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.2023;[Epub]     CrossRef
  • Artificial Intelligence-Supported Systems in Anesthesiology and Its Standpoint to Date—A Review
    Fiona M. P. Pham
    Open Journal of Anesthesiology.2023; 13(07): 140.     CrossRef
  • ChatGPT as an innovative tool for increasing sales in online stores
    Michał Orzoł, Katarzyna Szopik-Depczyńska
    Procedia Computer Science.2023; 225: 3450.     CrossRef
  • Intelligent Plagiarism as a Misconduct in Academic Integrity
    Jesús Miguel Muñoz-Cantero, Eva Maria Espiñeira-Bellón
    Acta Médica Portuguesa.2023; 37(1): 1.     CrossRef
  • Follow-up of Artificial Intelligence Development and its Controlled Contribution to the Article: Step to the Authorship?
    Ekrem Solmaz
    European Journal of Therapeutics.2023;[Epub]     CrossRef
  • May Artificial Intelligence Be a Co-Author on an Academic Paper?
    Ayşe Balat, İlhan Bahşi
    European Journal of Therapeutics.2023; 29(3): e12.     CrossRef
  • Opportunities and challenges for ChatGPT and large language models in biomedicine and health
    Shubo Tian, Qiao Jin, Lana Yeganova, Po-Ting Lai, Qingqing Zhu, Xiuying Chen, Yifan Yang, Qingyu Chen, Won Kim, Donald C Comeau, Rezarta Islamaj, Aadit Kapoor, Xin Gao, Zhiyong Lu
    Briefings in Bioinformatics.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
  • Editorial policies of Journal of Educational Evaluation for Health Professions on the use of generative artificial intelligence in article writing and peer review
    Sun Huh
    Journal of Educational Evaluation for Health Professions.2023; 20: 40.     CrossRef
Factors associated with medical students’ scores on the National Licensing Exam in Peru: a systematic review  
Javier Alejandro Flores-Cohaila
J Educ Eval Health Prof. 2022;19:38.   Published online December 29, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.38
  • 2,741 View
  • 245 Download
  • 1 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
This study aimed to identify factors that have been studied for their associations with National Licensing Examination (ENAM) scores in Peru.
Methods
A search was conducted of literature databases and registers, including EMBASE, SciELO, Web of Science, MEDLINE, Peru’s National Register of Research Work, and Google Scholar. The following key terms were used: “ENAM” and “associated factors.” Studies in English and Spanish were included. The quality of the included studies was evaluated using the Medical Education Research Study Quality Instrument (MERSQI).
Results
In total, 38,500 participants were enrolled in 12 studies. Most (11/12) studies were cross-sectional, except for one case-control study. Three studies were published in peer-reviewed journals. The mean MERSQI was 10.33. A better performance on the ENAM was associated with a higher-grade point average (GPA) (n=8), internship setting in EsSalud (n=4), and regular academic status (n=3). Other factors showed associations in various studies, such as medical school, internship setting, age, gender, socioeconomic status, simulations test, study resources, preparation time, learning styles, study techniques, test-anxiety, and self-regulated learning strategies.
Conclusion
The ENAM is a multifactorial phenomenon; our model gives students a locus of control on what they can do to improve their score (i.e., implement self-regulated learning strategies) and faculty, health policymakers, and managers a framework to improve the ENAM score (i.e., design remediation programs to improve GPA and integrate anxiety-management courses into the curriculum).

Citations

Citations to this article as recorded by  
  • Performance of ChatGPT on the Peruvian National Licensing Medical Examination: Cross-Sectional Study
    Javier A Flores-Cohaila, Abigaíl García-Vicente, Sonia F Vizcarra-Jiménez, Janith P De la Cruz-Galán, Jesús D Gutiérrez-Arratia, Blanca Geraldine Quiroga Torres, Alvaro Taype-Rondan
    JMIR Medical Education.2023; 9: e48039.     CrossRef
Medical students’ satisfaction level with e-learning during the COVID-19 pandemic and its related factors: a systematic review  
Mahbubeh Tabatabaeichehr, Samane Babaei, Mahdieh Dartomi, Peiman Alesheikh, Amir Tabatabaee, Hamed Mortazavi, Zohreh Khoshgoftar
J Educ Eval Health Prof. 2022;19:37.   Published online December 20, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.37
  • 2,081 View
  • 185 Download
  • 5 Web of Science
  • 4 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
This review investigated medical students’ satisfaction level with e-learning during the coronavirus disease 2019 (COVID-19) pandemic and its related factors.
Methods
A comprehensive systematic search was performed of international literature databases, including Scopus, PubMed, Web of Science, and Persian databases such as Iranmedex and Scientific Information Database using keywords extracted from Medical Subject Headings such as “Distance learning,” “Distance education,” “Online learning,” “Online education,” and “COVID-19” from the earliest date to July 10, 2022. The quality of the studies included in this review was evaluated using the appraisal tool for cross-sectional studies (AXIS tool).
Results
A total of 15,473 medical science students were enrolled in 24 studies. The level of satisfaction with e-learning during the COVID-19 pandemic among medical science students was 51.8%. Factors such as age, gender, clinical year, experience with e-learning before COVID-19, level of study, adaptation content of course materials, interactivity, understanding of the content, active participation of the instructor in the discussion, multimedia use in teaching sessions, adequate time dedicated to the e-learning, stress perception, and convenience had significant relationships with the satisfaction of medical students with e-learning during the COVID-19 pandemic.
Conclusion
Therefore, due to the inevitability of online education and e-learning, it is suggested that educational managers and policymakers choose the best online education method for medical students by examining various studies in this field to increase their satisfaction with e-learning.

Citations

Citations to this article as recorded by  
  • Factors affecting medical students’ satisfaction with online learning: a regression analysis of a survey
    Özlem Serpil Çakmakkaya, Elif Güzel Meydanlı, Ali Metin Kafadar, Mehmet Selman Demirci, Öner Süzer, Muhlis Cem Ar, Muhittin Onur Yaman, Kaan Can Demirbaş, Mustafa Sait Gönen
    BMC Medical Education.2024;[Epub]     CrossRef
  • A comparative study on the effectiveness of online and in-class team-based learning on student performance and perceptions in virtual simulation experiments
    Jing Shen, Hongyan Qi, Ruhuan Mei, Cencen Sun
    BMC Medical Education.2024;[Epub]     CrossRef
  • Physician Assistant Students’ Perception of Online Didactic Education: A Cross-Sectional Study
    Daniel L Anderson, Jeffrey L Alexander
    Cureus.2023;[Epub]     CrossRef
  • Mediating Role of PERMA Wellbeing in the Relationship between Insomnia and Psychological Distress among Nursing College Students
    Qian Sun, Xiangyu Zhao, Yiming Gao, Di Zhao, Meiling Qi
    Behavioral Sciences.2023; 13(9): 764.     CrossRef
Characteristics and 10 key components of interpersonal caring: a narrative review  
Susie Kim
J Educ Eval Health Prof. 2022;19:17.   Published online July 25, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.17
  • 3,682 View
  • 243 Download
  • 1 Crossref
AbstractAbstract PDFSupplementary Material
This paper aims to help people understand better the lives of people who are mentally ill by describing the general concept of the Interpersonal Caring Theory (ICT) and deducing 10 key components of interpersonal caring. The literature review described the definition of interpersonal caring, and its assumptions and characteristics. Furthermore, the authors’ experience with patient care suggested the critical components of interpersonal caring, which is the compassion-based therapeutic actions/behaviors through the collaborative partnership developed between nurse and client. Essential characteristics of interpersonal caring include the following: person-to-person interaction between nurse and patient, genuine love and concern toward the person, conveying trust and hope, transcending space, time, and culture, holistic approach expressed through a comprehensive and dynamic mode of communication, helping the patient focus on their self-worth, and providing culturally relevant and sensitive nursing. Ten key components of interpersonal caring in ICT include noticing, participating, sharing, active listening, companioning, complimenting, comforting, hoping, forgiving, and accepting. Interpersonal caring results from the blended understanding of the empirical, aesthetic, ethical, and intuitive aspects of a given clinical situation, and a nexus of pre-conditions, content, feelings, and sense of self-worth/self-esteem.

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  • Disparidades de salud en el mundo real de los pacientes con esclerosis múltiple
    Roberto Rotta Escalante, Osvaldo Fustinoni, María Elisa Barone, José R. Elli, María del Carmen Martínez Perea
    Neurología Argentina.2023; 15(1): 37.     CrossRef
Prevalence of burnout and related factors in nursing faculty members: a systematic review  
Marziyeh Hosseini, Mitra Soltanian, Camellia Torabizadeh, Zahra Hadian Shirazi
J Educ Eval Health Prof. 2022;19:16.   Published online July 14, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.16
  • 3,951 View
  • 387 Download
  • 5 Web of Science
  • 8 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
The current study aimed to identify the prevalence of burnout and related factors in nursing faculty members through a systematic review of the literature.
Methods
A comprehensive search of electronic databases, including Scopus, PubMed, Web of Science, Iranmedex, and Scientific Information Database was conducted via keywords extracted from Medical Subject Headings, including burnout and nursing faculty, for studies published from database inception to April 1, 2022. The quality of the included studies in this review was assessed using the appraisal tool for cross-sectional studies.
Results
A total of 2,551 nursing faculty members were enrolled in 11 studies. The mean score of burnout in nursing faculty members based on the Maslach Burnout Inventory (MBI) was 59.28 out of 132. The burnout score in this study was presented in 3 MBI subscales: emotional exhaustion, 21.24 (standard deviation [SD]=9.70) out of 54; depersonalization, 5.88 (SD=4.20) out of 30; and personal accomplishment, 32.16 (SD=6.45) out of 48. Several factors had significant relationships with burnout in nursing faculty members, including gender, level of education, hours of work, number of classroom, students taught, full-time work, job pressure, perceived stress, subjective well-being, marital status, job satisfaction, work setting satisfaction, workplace empowerment, collegial support, management style, fulfillment of self-expectation, communication style, humor, and academic position.
Conclusion
Overall, the mean burnout scores in nursing faculty members were moderate. Therefore, health policymakers and managers can reduce the likelihood of burnout in nursing faculty members by using psychosocial interventions and support.

Citations

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  • Civility and resilience practices to address chronic workplace stress in nursing academia
    Teresa M. Stephens, Cynthia M. Clark
    Teaching and Learning in Nursing.2024;[Epub]     CrossRef
  • The state of mental health, burnout, mattering and perceived wellness culture in Doctorally prepared nursing faculty with implications for action
    Bernadette Mazurek Melnyk, Lee Ann Strait, Cindy Beckett, Andreanna Pavan Hsieh, Jeffery Messinger, Randee Masciola
    Worldviews on Evidence-Based Nursing.2023; 20(2): 142.     CrossRef
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    Sheila A. Boamah, Michael Kalu, Rosain Stennett, Emily Belita, Jasmine Travers
    International Journal of Environmental Research and Public Health.2023; 20(5): 4398.     CrossRef
  • Understanding and Fostering Mental Health and Well-Being among University Faculty: A Narrative Review
    Dalal Hammoudi Halat, Abderrezzaq Soltani, Roua Dalli, Lama Alsarraj, Ahmed Malki
    Journal of Clinical Medicine.2023; 12(13): 4425.     CrossRef
  • Strategies to promote nurse educator well-being and prevent burnout: An integrative review
    Allan Lovern, Lindsay Quinlan, Stephanie Brogdon, Cora Rabe, Laura S. Bonanno
    Teaching and Learning in Nursing.2023;[Epub]     CrossRef
  • ALS Health care provider wellness
    Gregory Hansen, Sarah Burton-MacLeod, Kerri Lynn Schellenberg
    Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration.2023; : 1.     CrossRef
  • Cuidando al profesorado: resultados de un programa a distancia de autocuidado para educadores de profesiones de la salud
    Denisse Zúñiga, Guadalupe Echeverría, Pía Nitsche, Nuria Pedrals, Attilio Rigotti, Marisol Sirhan, Klaus Puschel, Marcela Bitran
    Educación Médica.2023; : 100871.     CrossRef
  • A mixed-methods study of the effectiveness and perceptions of a course design institute for health science educators
    Julie Speer, Quincy Conley, Derek Thurber, Brittany Williams, Mitzi Wasden, Brenda Jackson
    BMC Medical Education.2022;[Epub]     CrossRef
Educational applications of metaverse: possibilities and limitations  
Bokyung Kye, Nara Han, Eunji Kim, Yeonjeong Park, Soyoung Jo
J Educ Eval Health Prof. 2021;18:32.   Published online December 13, 2021
DOI: https://doi.org/10.3352/jeehp.2021.18.32
  • 33,251 View
  • 2,764 Download
  • 193 Web of Science
  • 229 Crossref
AbstractAbstract PDFSupplementary Material
This review aims to define the 4 types of the metaverse and to explain the potential and limitations of its educational applications. The metaverse roadmap categorizes the metaverse into 4 types: augmented reality, lifelogging, mirror world, and virtual reality. An example of the application of augmented reality in medical education would be an augmented reality T-shirt that allows students to examine the inside of the human body as an anatomy lab. Furthermore, a research team in a hospital in Seoul developed a spinal surgery platform that applied augmented reality technology. The potential of the metaverse as a new educational environment is suggested to be as follows: a space for new social communication; a higher degree of freedom to create and share; and the provision of new experiences and high immersion through virtualization. Some of its limitations may be weaker social connections and the possibility of privacy impingement; the commission of various crimes due to the virtual space and anonymity of the metaverse; and maladaptation to the real world for students whose identity has not been established. The metaverse is predicted to change our daily life and economy beyond the realm of games and entertainment. The metaverse has infinite potential as a new social communication space. The following future tasks are suggested for the educational use of the metaverse: first, teachers should carefully analyze how students understand the metaverse; second, teachers should design classes for students to solve problems or perform projects cooperatively and creatively; third, educational metaverse platforms should be developed that prevent misuse of student data.

Citations

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    Carmen Carrión
    The Journal of Supercomputing.2024; 80(2): 1598.     CrossRef
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    Youngju Kim, Bumsoo Kim
    Technological Forecasting and Social Change.2024; 198: 122980.     CrossRef
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    Thaísa Pinheiro Silva, Maria Fernanda Andrade-Bortoletto, Deborah Queiroz Freitas, Christiano Oliveira-Santos, Wilton Mitsunari Takeshita
    European Journal of Radiology.2024; 170: 111210.     CrossRef
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    Wai‐Ming To, Billy T. W. Yu, Andy W. L. Chung, David W. K. Chung
    Transactions on Emerging Telecommunications Technologies.2024;[Epub]     CrossRef
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    Emmanuel Fokides, Panagiotis Antonopoulos
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    I Gede Dharma Utamayasa, Riga Mardhika
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The relationship of non-cognitive factors to academic and clinical performance in graduate rehabilitation science students in the United States: a systematic review  
Kelly Reynolds, Caroline Bazemore, Cannon Hanebuth, Steph Hendren, Maggie Horn
J Educ Eval Health Prof. 2021;18:31.   Published online November 23, 2021
DOI: https://doi.org/10.3352/jeehp.2021.18.31
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AbstractAbstract PDFSupplementary Material
Purpose
Rehabilitation science programs utilize cognitive and non-cognitive factors to select students who can complete the didactic and clinical portions of the program and pass the licensure exam. Cognitive factors such a prior grade point average and standardized test scores are known to be predictive of academic performance, but the relationship of non-cognitive factors and performance is less clear. The purpose of this systematic review was to explore the relationship of non-cognitive factors to academic and clinical performance in rehabilitation science programs.
Methods
A search of 7 databases was conducted using the following eligibility criteria: graduate programs in physical therapy (PT), occupational therapy, speech-language pathology, United States-based programs, measurement of at least 1 non-cognitive factor, measurement of academic and/or clinical performance, and quantitative reporting of results. Articles were screened by title, abstract, and full text, and data were extracted.
Results
After the comprehensive screening, 21 articles were included in the review. Seventy-six percent of studies occurred in PT students. Grit, self-efficacy, emotional intelligence, and stress were the most commonly studied factors. Only self-efficacy, emotional intelligence, and personality traits were examined in clinical and academic contexts. The results were mixed for all non-cognitive factors. Higher grit and self-efficacy tended to be associated with better performance, while stress was generally associated with worse outcomes.
Conclusion
No single non-cognitive factor was consistently related to clinical or academic performance in rehabilitation science students. There is insufficient evidence currently to recommend the evaluation of a specific non-cognitive factor for admissions decisions.

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E-learning in health professions education during the COVID-19 pandemic: a systematic review  
Aziz Naciri, Mohamed Radid, Ahmed Kharbach, Ghizlane Chemsi
J Educ Eval Health Prof. 2021;18:27.   Published online October 29, 2021
DOI: https://doi.org/10.3352/jeehp.2021.18.27
  • 9,622 View
  • 545 Download
  • 55 Web of Science
  • 70 Crossref
AbstractAbstract PDFSupplementary Material
As an alternative to traditional teaching, e-learning has enabled continuity of learning for health professions students during the coronavirus disease 2019 (COVID-19) pandemic. This review explored health professions students; perceptions, acceptance, motivation, and engagement with e-learning during the COVID-19 pandemic. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic review was conducted by consulting 5 databases: PubMed, ERIC (Ebsco), Science Direct, Scopus, and Web of Science. The quality of the included studies was assessed using the Medical Education Research Study Quality Instrument. The research protocol was previously registered in the PROSPERO registry (CRD42021237055). From 250 studies identified, 15 were selected with a total of 111,622 students. Mostly positive perceptions were reported in 7 of 12 studies, which mainly focused on technology access, possession of basic computer skills, pedagogical design of online courses, online interactions, and learning flexibility. However, predominantly negative perceptions were identified in 5 of 12 studies, which pointed out constraints related to internet connections, the use of educational platforms, and acquisition of clinical skills. Satisfactory levels of acceptance of distance learning were reported in 3 of 4 studies. For student motivation and engagement, 1 study reported similar or higher motivation than with traditional teaching, and another study indicated that student engagement significantly increased during the COVID-19 pandemic. Health professions students showed a positive response to e-learning regarding perceptions, acceptance, motivation, and engagement. Future research is needed to remediate the lack of studies addressing health professions students’ motivation and engagement during the COVID-19 pandemic.

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Sample size determination and power analysis using the G*Power software  
Hyun Kang
J Educ Eval Health Prof. 2021;18:17.   Published online July 30, 2021
DOI: https://doi.org/10.3352/jeehp.2021.18.17
  • 65,022 View
  • 2,268 Download
  • 422 Web of Science
  • 440 Crossref
AbstractAbstract PDFSupplementary Material
Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of calculating sample size and power require broad statistical knowledge, there is a shortage of personnel with programming skills, and commercial programs are often too expensive to use in practice. The review article aimed to explain the basic concepts of sample size calculation and power analysis; the process of sample estimation; and how to calculate sample size using G*Power software (latest ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) with 5 statistical examples. The null and alternative hypothesis, effect size, power, alpha, type I error, and type II error should be described when calculating the sample size or power. G*Power is recommended for sample size and power calculations for various statistical methods (F, t, χ2, Z, and exact tests), because it is easy to use and free. The process of sample estimation consists of establishing research goals and hypotheses, choosing appropriate statistical tests, choosing one of 5 possible power analysis methods, inputting the required variables for analysis, and selecting the “calculate” button. The G*Power software supports sample size and power calculation for various statistical methods (F, t, χ2, z, and exact tests). This software is helpful for researchers to estimate the sample size and to conduct power analysis.

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Assessment methods and the validity and reliability of measurement tools in online objective structured clinical examinations: a systematic scoping review  
Jonathan Zachary Felthun, Silas Taylor, Boaz Shulruf, Digby Wigram Allen
J Educ Eval Health Prof. 2021;18:11.   Published online June 1, 2021
DOI: https://doi.org/10.3352/jeehp.2021.18.11
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  • 10 Web of Science
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AbstractAbstract PDFSupplementary Material
The coronavirus disease 2019 (COVID-19) pandemic has required educators to adapt the in-person objective structured clinical examination (OSCE) to online settings in order for it to remain a critical component of the multifaceted assessment of a student’s competency. This systematic scoping review aimed to summarize the assessment methods and validity and reliability of the measurement tools used in current online OSCE (hereafter, referred to as teleOSCE) approaches. A comprehensive literature review was undertaken following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. Articles were eligible if they reported any form of performance assessment, in any field of healthcare, delivered in an online format. Two reviewers independently screened the results and analyzed relevant studies. Eleven articles were included in the analysis. Pre-recorded videos were used in 3 studies, while observations by remote examiners through an online platform were used in 7 studies. Acceptability as perceived by students was reported in 2 studies. This systematic scoping review identified several insights garnered from implementing teleOSCEs, the components transferable from telemedicine, and the need for systemic research to establish the ideal teleOSCE framework. TeleOSCEs may be able to improve the accessibility and reproducibility of clinical assessments and equip students with the requisite skills to effectively practice telemedicine in the future.

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The effectiveness of cultural competence education in enhancing knowledge acquisition, performance, attitudes, and student satisfaction among undergraduate health science students: a scoping review  
Elio Arruzza, Minh Chau
J Educ Eval Health Prof. 2021;18:3.   Published online February 24, 2021
DOI: https://doi.org/10.3352/jeehp.2021.18.3
  • 8,909 View
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  • 17 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
Cultural competence in healthcare assists in the delivery of culturally sensitive and high-quality services. This scoping review aims to provide an overview of the available evidence and to examine the effectiveness of classroom-based intervention strategies used to enhance the cultural competence of undergraduate health science students.
Methods
A comprehensive and systematic literature search was undertaken in databases, including Cochrane Library, Medline, and Emcare. Articles were eligible if they employed an experimental study design to assess classroom-based cultural competency education for university students across the health science disciplines. Two reviewers independently screened and extracted relevant data pertaining to study and participant characteristics using a charting table. The outcomes included knowledge, attitudes, skills, and perceived benefits.
Results
Ten studies were analysed. Diverse approaches to cultural education exist in terms of the mode, frequency, and duration of interventions. For the knowledge outcome, students who experienced cultural education interventions yielded higher post-test scores than their baseline cultural knowledge, but without a significant difference from the scores of students who did not receive interventions. Data relating to the skills domain demonstrated positive effects for students after experiencing interventions. Overall, students were satisfied with their experiences and demonstrated improvements in confidence and attitudes towards culturally competent practice.
Conclusion
Across health science disciplines, cultural competency interventions were shown to be effective in enhancing knowledge acquisition, performance of skills, attitudes, and student satisfaction. Future research is necessary to address the significant absence of control arms in the current literature, and to assess long-term effects and patient-related outcomes.

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A proposal for the future of medical education accreditation in Korea  
Ki-Young Lim
J Educ Eval Health Prof. 2020;17:32.   Published online October 21, 2020
DOI: https://doi.org/10.3352/jeehp.2020.17.32
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AbstractAbstract PDFSupplementary Material
For the past 20 years, the medical education accreditation program of the Korean Institute of Medical Education and Evaluation (KIMEE) has contributed significantly to the standardization and improvement of the quality of basic medical education in Korea. It should now contribute to establishing and promoting the future of medical education. The Accreditation Standards of KIMEE 2019 (ASK2019) have been adopted since 2019, with the goal of achieving world-class medical education by applying a learner-centered curriculum using a continuum framework for the 3 phases of formal medical education: basic medical education, postgraduate medical education, and continuing professional development. ASK2019 will also be able to promote medical education that meets community needs and employs systematic assessments throughout the education process. These are important changes that can be used to gauge the future of the medical education accreditation system. Furthermore, globalization, inter-professional education, health systems science, and regular self-assessment systems are emerging as essential topics for the future of medical education. It is time for the medical education accreditation system in Korea to observe and adopt new trends in global medical education.

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JEEHP : Journal of Educational Evaluation for Health Professions