Most-download articles are from the articles published in 2022 during the last three month.
Review
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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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Abstract
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 ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges.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.
Research articles
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Importance, performance frequency, and predicted future importance of dietitians’ jobs by practicing dietitians in Korea: a survey study
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Cheongmin Sohn, Sooyoun Kwon, Won Gyoung Kim, Kyung-Eun Lee, Sun-Young Lee, Seungmin Lee
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J Educ Eval Health Prof. 2024;21:1. Published online January 2, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.1
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Abstract
PDFSupplementary Material
- Purpose
This study aimed to explore the perceptions held by practicing dietitians of the importance of their tasks performed in current work environments, the frequency at which those tasks are performed, and predictions about the importance of those tasks in future work environments.
Methods
This was a cross-sectional survey study. An online survey was administered to 350 practicing dietitians. They were asked to assess the importance, performance frequency, and predicted changes in the importance of 27 tasks using a 5-point scale. Descriptive statistics were calculated, and the means of the variables were compared across categorized work environments using analysis of variance.
Results
The importance scores of all surveyed tasks were higher than 3.0, except for the marketing management task. Self-development, nutrition education/counseling, menu planning, food safety management, and documentation/data management were all rated higher than 4.0. The highest performance frequency score was related to documentation/data management. The importance scores of all duties, except for professional development, differed significantly by workplace. As for predictions about the future importance of the tasks surveyed, dietitians responded that the importance of all 27 tasks would either remain at current levels or increase in the future.
Conclusion
Twenty-seven tasks were confirmed to represent dietitians’ job functions in various workplaces. These tasks can be used to improve the test specifications of the Korean Dietitian Licensing Examination and the curriculum of dietetic education programs.
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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
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Hyunju Lee, Soobin Park
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J Educ Eval Health Prof. 2023;20:39. Published online December 28, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.39
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Abstract
PDFSupplementary Material
- Purpose
This study assessed the performance of 6 generative artificial intelligence (AI) platforms on the learning objectives of medical arthropodology in a parasitology class in Korea. We examined the AI platforms’ performance by querying in Korean and English to determine their information amount, accuracy, and relevance in prompts in both languages.
Methods
From December 15 to 17, 2023, 6 generative AI platforms—Bard, Bing, Claude, Clova X, GPT-4, and Wrtn—were tested on 7 medical arthropodology learning objectives in English and Korean. Clova X and Wrtn are platforms from Korean companies. Responses were evaluated using specific criteria for the English and Korean queries.
Results
Bard had abundant information but was fourth in accuracy and relevance. GPT-4, with high information content, ranked first in accuracy and relevance. Clova X was 4th in amount but 2nd in accuracy and relevance. Bing provided less information, with moderate accuracy and relevance. Wrtn’s answers were short, with average accuracy and relevance. Claude AI had reasonable information, but lower accuracy and relevance. The responses in English were superior in all aspects. Clova X was notably optimized for Korean, leading in relevance.
Conclusion
In a study of 6 generative AI platforms applied to medical arthropodology, GPT-4 excelled overall, while Clova X, a Korea-based AI product, achieved 100% relevance in Korean queries, the highest among its peers. Utilizing these AI platforms in classrooms improved the authors’ self-efficacy and interest in the subject, offering a positive experience of interacting with generative AI platforms to question and receive information.
Educational/Faculty development material
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Common models and approaches for the clinical educator to plan effective feedback encounters
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Cesar Orsini, Veena Rodrigues, Jorge Tricio, Margarita Rosel
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J Educ Eval Health Prof. 2022;19:35. Published online December 19, 2022
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DOI: https://doi.org/10.3352/jeehp.2022.19.35
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Abstract
PDFSupplementary Material
- Giving constructive feedback is crucial for learners to bridge the gap between their current performance and the desired standards of competence. Giving effective feedback is a skill that can be learned, practiced, and improved. Therefore, our aim was to explore models in clinical settings and assess their transferability to different clinical feedback encounters. We identified the 6 most common and accepted feedback models, including the Feedback Sandwich, the Pendleton Rules, the One-Minute Preceptor, the SET-GO model, the R2C2 (Rapport/Reaction/Content/Coach), and the ALOBA (Agenda Led Outcome-based Analysis) model. We present a handy resource describing their structure, strengths and weaknesses, requirements for educators and learners, and suitable feedback encounters for use for each model. These feedback models represent practical frameworks for educators to adopt but also to adapt to their preferred style, combining and modifying them if necessary to suit their needs and context.
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Citations
Citations to this article as recorded by
- Navigating power dynamics between pharmacy preceptors and learners
Shane Tolleson, Mabel Truong, Natalie Rosario
Exploratory Research in Clinical and Social Pharmacy.2024; 13: 100408. CrossRef - Feedback conversations: First things first?
Katharine A. Robb, Marcy E. Rosenbaum, Lauren Peters, Susan Lenoch, Donna Lancianese, Jane L. Miller
Patient Education and Counseling.2023; 115: 107849. CrossRef
Research article
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ChatGPT (GPT-4) passed the Japanese National License Examination for Pharmacists in 2022, answering all items including those with diagrams: a descriptive study
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Hiroyasu Sato, Katsuhiko Ogasawara
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J Educ Eval Health Prof. 2024;21:4. Published online February 28, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.4
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Abstract
PDFSupplementary Material
- Purpose
The objective of this study was to assess the performance of ChatGPT (GPT-4) on all items, including those with diagrams, in the Japanese National License Examination for Pharmacists (JNLEP) and compare it with the previous GPT-3.5 model’s performance.
Methods
The 107th JNLEP, conducted in 2022, with 344 items input into the GPT-4 model, was targeted for this study. Separately, 284 items, excluding those with diagrams, were entered into the GPT-3.5 model. The answers were categorized and analyzed to determine accuracy rates based on categories, subjects, and presence or absence of diagrams. The accuracy rates were compared to the main passing criteria (overall accuracy rate ≥62.9%).
Results
The overall accuracy rate for all items in the 107th JNLEP in GPT-4 was 72.5%, successfully meeting all the passing criteria. For the set of items without diagrams, the accuracy rate was 80.0%, which was significantly higher than that of the GPT-3.5 model (43.5%). The GPT-4 model demonstrated an accuracy rate of 36.1% for items that included diagrams.
Conclusion
Advancements that allow GPT-4 to process images have made it possible for LLMs to answer all items in medical-related license examinations. This study’s findings confirm that ChatGPT (GPT-4) possesses sufficient knowledge to meet the passing criteria.
Editorial
Review
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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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Abstract
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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Citations
Citations to this article as recorded by
- 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
Research articles
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Development and validity evidence for the resident-led large group teaching assessment instrument in the United States: a methodological study
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Ariel Shana Frey-Vogel, Kristina Dzara, Kimberly Anne Gifford, Yoon Soo Park, Justin Berk, Allison Heinly, Darcy Wolcott, Daniel Adam Hall, Shannon Elliott Scott-Vernaglia, Katherine Anne Sparger, Erica Ye-pyng Chung
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J Educ Eval Health Prof. 2024;21:3. Published online February 23, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.3
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Abstract
PDFSupplementary Material
- Purpose
Despite educational mandates to assess resident teaching competence, limited instruments with validity evidence exist for this purpose. Existing instruments do not allow faculty to assess resident-led teaching in a large group format or whether teaching was interactive. This study gathers validity evidence on the use of the Resident-led Large Group Teaching Assessment Instrument (Relate), an instrument used by faculty to assess resident teaching competency. Relate comprises 23 behaviors divided into 6 elements: learning environment, goals and objectives, content of talk, promotion of understanding and retention, session management, and closure.
Methods
Messick’s unified validity framework was used for this study. Investigators used video recordings of resident-led teaching from 3 pediatric residency programs to develop Relate and a rater guidebook. Faculty were trained on instrument use through frame-of-reference training. Resident teaching at all sites was video-recorded during 2018–2019. Two trained faculty raters assessed each video. Descriptive statistics on performance were obtained. Validity evidence sources include: rater training effect (response process), reliability and variability (internal structure), and impact on Milestones assessment (relations to other variables).
Results
Forty-eight videos, from 16 residents, were analyzed. Rater training improved inter-rater reliability from 0.04 to 0.64. The Φ-coefficient reliability was 0.50. There was a significant correlation between overall Relate performance and the pediatric teaching Milestone (r=0.34, P=0.019).
Conclusion
Relate provides validity evidence with sufficient reliability to measure resident-led large-group teaching competence.
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Discovering social learning ecosystems during clinical clerkship from United States medical students’ feedback encounters: a content analysis
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Anna Therese Cianciolo, Heeyoung Han, Lydia Anne Howes, Debra Lee Klamen, Sophia Matos
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J Educ Eval Health Prof. 2024;21:5. Published online February 28, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.5
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Abstract
PDFSupplementary Material
- Purpose
We examined United States medical students’ self-reported feedback encounters during clerkship training to better understand in situ feedback practices. Specifically, we asked: Who do students receive feedback from, about what, when, where, and how do they use it? We explored whether curricular expectations for preceptors’ written commentary aligned with feedback as it occurs naturalistically in the workplace.
Methods
This study occurred from July 2021 to February 2022 at Southern Illinois University School of Medicine. We used qualitative survey-based experience sampling to gather students’ accounts of their feedback encounters in 8 core specialties. We analyzed the who, what, when, where, and why of 267 feedback encounters reported by 11 clerkship students over 30 weeks. Code frequencies were mapped qualitatively to explore patterns in feedback encounters.
Results
Clerkship feedback occurs in patterns apparently related to the nature of clinical work in each specialty. These patterns may be attributable to each specialty’s “social learning ecosystem”—the distinctive learning environment shaped by the social and material aspects of a given specialty’s work, which determine who preceptors are, what students do with preceptors, and what skills or attributes matter enough to preceptors to comment on.
Conclusion
Comprehensive, standardized expectations for written feedback across specialties conflict with the reality of workplace-based learning. Preceptors may be better able—and more motivated—to document student performance that occurs as a natural part of everyday work. Nurturing social learning ecosystems could facilitate workplace-based learning such that, across specialties, students acquire a comprehensive clinical skillset appropriate for graduation.
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Use of learner-driven, formative, ad-hoc, prospective assessment of competence in physical therapist clinical education in the United States: a prospective cohort study
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Carey Holleran, Jeffrey Konrad, Barbara Norton, Tamara Burlis, Steven Ambler
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J Educ Eval Health Prof. 2023;20:36. Published online December 8, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.36
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Abstract
PDFSupplementary Material
- Purpose
The purpose of this project was to implement a process for learner-driven, formative, prospective, ad-hoc, entrustment assessment in Doctor of Physical Therapy clinical education. Our goals were to develop an innovative entrustment assessment tool, and then explore whether the tool detected (1) differences between learners at different stages of development and (2) differences within learners across the course of a clinical education experience. We also investigated whether there was a relationship between the number of assessments and change in performance.
Methods
A prospective, observational, cohort of clinical instructors (CIs) was recruited to perform learner-driven, formative, ad-hoc, prospective, entrustment assessments. Two entrustable professional activities (EPAs) were used: (1) gather a history and perform an examination and (2) implement and modify the plan of care, as needed. CIs provided a rating on the entrustment scale and provided narrative support for their rating.
Results
Forty-nine learners participated across 4 clinical experiences (CEs), resulting in 453 EPA learner-driven assessments. For both EPAs, statistically significant changes were detected both between learners at different stages of development and within learners across the course of a CE. Improvement within each CE was significantly related to the number of feedback opportunities.
Conclusion
The results of this pilot study provide preliminary support for the use of learner-driven, formative, ad-hoc assessments of competence based on EPAs with a novel entrustment scale. The number of formative assessments requested correlated with change on the EPA scale, suggesting that formative feedback may augment performance improvement.
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
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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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10,495
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Abstract
PDFSupplementary 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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Citations
Citations to this article as recorded by
- Large Language Models and Artificial Intelligence: A Primer for Plastic Surgeons on the Demonstrated and Potential Applications, Promises, and Limitations of ChatGPT
Jad Abi-Rafeh, Hong Hao Xu, Roy Kazan, Ruth Tevlin, Heather Furnas
Aesthetic Surgery Journal.2024; 44(3): 329. CrossRef - Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers
Ana Suárez, Víctor Díaz‐Flores García, Juan Algar, Margarita Gómez Sánchez, María Llorente de Pedro, Yolanda Freire
International Endodontic Journal.2024; 57(1): 108. CrossRef - Bob or Bot: Exploring ChatGPT's Answers to University Computer Science Assessment
Mike Richards, Kevin Waugh, Mark Slaymaker, Marian Petre, John Woodthorpe, Daniel Gooch
ACM Transactions on Computing Education.2024; 24(1): 1. CrossRef - Examining the use of ChatGPT in public universities in Hong Kong: a case study of restricted access areas
Michelle W. T. Cheng, Iris H. Y. YIM
Discover Education.2024;[Epub] CrossRef - Performance of ChatGPT on Ophthalmology-Related Questions Across Various Examination Levels: Observational Study
Firas Haddad, Joanna S Saade
JMIR Medical Education.2024; 10: e50842. CrossRef - A comparative vignette study: Evaluating the potential role of a generative AI model in enhancing clinical decision‐making in nursing
Mor Saban, Ilana Dubovi
Journal of Advanced Nursing.2024;[Epub] CrossRef - Comparison of the Performance of GPT-3.5 and GPT-4 With That of Medical Students on the Written German Medical Licensing Examination: Observational Study
Annika Meyer, Janik Riese, Thomas Streichert
JMIR Medical Education.2024; 10: e50965. CrossRef - From hype to insight: Exploring ChatGPT's early footprint in education via altmetrics and bibliometrics
Lung‐Hsiang Wong, Hyejin Park, Chee‐Kit Looi
Journal of Computer Assisted Learning.2024;[Epub] CrossRef - A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Morris Gordon, Michelle Daniel, Aderonke Ajiboye, Hussein Uraiby, Nicole Y. Xu, Rangana Bartlett, Janice Hanson, Mary Haas, Maxwell Spadafore, Ciaran Grafton-Clarke, Rayhan Yousef Gasiea, Colin Michie, Janet Corral, Brian Kwan, Diana Dolmans, Satid Thamma
Medical Teacher.2024; : 1. CrossRef - Üniversite Öğrencilerinin ChatGPT 3,5 Deneyimleri: Yapay Zekâyla Yazılmış Masal Varyantları
Bilge GÖK, Fahri TEMİZYÜREK, Özlem BAŞ
Korkut Ata Türkiyat Araştırmaları Dergisi.2024; (14): 1040. CrossRef - Tracking ChatGPT Research: Insights From the Literature and the Web
Omar Mubin, Fady Alnajjar, Zouheir Trabelsi, Luqman Ali, Medha Mohan Ambali Parambil, Zhao Zou
IEEE Access.2024; 12: 30518. CrossRef - Applicability of ChatGPT in Assisting to Solve Higher Order Problems in Pathology
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Cureus.2023;[Epub] CrossRef - Issues in the 3rd year of the COVID-19 pandemic, including computer-based testing, study design, ChatGPT, journal metrics, and appreciation to reviewers
Sun Huh
Journal of Educational Evaluation for Health Professions.2023; 20: 5. CrossRef - Emergence of the metaverse and ChatGPT in journal publishing after the COVID-19 pandemic
Sun Huh
Science Editing.2023; 10(1): 1. CrossRef - Assessing the Capability of ChatGPT in Answering First- and Second-Order Knowledge Questions on Microbiology as per Competency-Based Medical Education Curriculum
Dipmala Das, Nikhil Kumar, Langamba Angom Longjam, Ranwir Sinha, Asitava Deb Roy, Himel Mondal, Pratima Gupta
Cureus.2023;[Epub] CrossRef - Evaluating ChatGPT's Ability to Solve Higher-Order Questions on the Competency-Based Medical Education Curriculum in Medical Biochemistry
Arindam Ghosh, Aritri Bir
Cureus.2023;[Epub] CrossRef - Overview of Early ChatGPT’s Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts
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Cureus.2023;[Epub] CrossRef - ChatGPT for Future Medical and Dental Research
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Cureus.2023;[Epub] CrossRef - ChatGPT in Dentistry: A Comprehensive Review
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Cureus.2023;[Epub] CrossRef - Can we trust AI chatbots’ answers about disease diagnosis and patient care?
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Journal of the Korean Medical Association.2023; 66(4): 218. CrossRef - Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions
Alaa Abd-alrazaq, Rawan AlSaad, Dari Alhuwail, Arfan Ahmed, Padraig Mark Healy, Syed Latifi, Sarah Aziz, Rafat Damseh, Sadam Alabed Alrazak, Javaid Sheikh
JMIR Medical Education.2023; 9: e48291. CrossRef - Early applications of ChatGPT in medical practice, education and research
Sam Sedaghat
Clinical Medicine.2023; 23(3): 278. CrossRef - A Review of Research on Teaching and Learning Transformation under the Influence of ChatGPT Technology
璇 师
Advances in Education.2023; 13(05): 2617. CrossRef - Performance of GPT-3.5 and GPT-4 on the Japanese Medical Licensing Examination: Comparison Study
Soshi Takagi, Takashi Watari, Ayano Erabi, Kota Sakaguchi
JMIR Medical Education.2023; 9: e48002. CrossRef - ChatGPT’s quiz skills in different otolaryngology subspecialties: an analysis of 2576 single-choice and multiple-choice board certification preparation questions
Cosima C. Hoch, Barbara Wollenberg, Jan-Christoffer Lüers, Samuel Knoedler, Leonard Knoedler, Konstantin Frank, Sebastian Cotofana, Michael Alfertshofer
European Archives of Oto-Rhino-Laryngology.2023; 280(9): 4271. CrossRef - Analysing the Applicability of ChatGPT, Bard, and Bing to Generate Reasoning-Based Multiple-Choice Questions in Medical Physiology
Mayank Agarwal, Priyanka Sharma, Ayan Goswami
Cureus.2023;[Epub] CrossRef - The Intersection of ChatGPT, Clinical Medicine, and Medical Education
Rebecca Shin-Yee Wong, Long Chiau Ming, Raja Affendi Raja Ali
JMIR Medical Education.2023; 9: e47274. CrossRef - The Role of Artificial Intelligence in Higher Education: ChatGPT Assessment for Anatomy Course
Tarık TALAN, Yusuf KALINKARA
Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi.2023; 7(1): 33. CrossRef - Comparing ChatGPT’s ability to rate the degree of stereotypes and the consistency of stereotype attribution with those of medical students in New Zealand in developing a similarity rating test: a methodological study
Chao-Cheng Lin, Zaine Akuhata-Huntington, Che-Wei Hsu
Journal of Educational Evaluation for Health Professions.2023; 20: 17. CrossRef - Examining Real-World Medication Consultations and Drug-Herb Interactions: ChatGPT Performance Evaluation
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JMIR Medical Education.2023; 9: e48433. CrossRef - Assessing the Efficacy of ChatGPT in Solving Questions Based on the Core Concepts in Physiology
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Cureus.2023;[Epub] CrossRef - ChatGPT Performs on the Chinese National Medical Licensing Examination
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Journal of Medical Systems.2023;[Epub] CrossRef - Artificial intelligence and its impact on job opportunities among university students in North Lima, 2023
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ICST Transactions on Scalable Information Systems.2023;[Epub] CrossRef - Revolutionizing Dental Care: A Comprehensive Review of Artificial Intelligence Applications Among Various Dental Specialties
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Cureus.2023;[Epub] CrossRef - Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review
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JMIR Medical Education.2023; 9: e48785. CrossRef - Exploring the impact of language models, such as ChatGPT, on student learning and assessment
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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;[Epub] CrossRef - Redesigning Tertiary Educational Evaluation with AI: A Task-Based Analysis of LIS Students’ Assessment on Written Tests and Utilizing ChatGPT at NSTU
Shamima Yesmin
Science & Technology Libraries.2023; : 1. CrossRef - ChatGPT and the AI revolution: a comprehensive investigation of its multidimensional impact and potential
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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?
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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
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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 - A systematic review of ChatGPT use in K‐12 education
Peng Zhang, Gemma Tur
European Journal of Education.2023;[Epub] 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 - Evaluating ChatGPT as a self‐learning tool in medical biochemistry: A performance assessment in undergraduate medical university examination
Krishna Mohan Surapaneni, Anusha Rajajagadeesan, Lakshmi Goudhaman, Shalini Lakshmanan, Saranya Sundaramoorthi, Dineshkumar Ravi, Kalaiselvi Rajendiran, Porchelvan Swaminathan
Biochemistry and Molecular Biology Education.2023;[Epub] 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 intelligences’ answers to learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study
Hyunju Lee, Soo Bin Park
Journal of Educational Evaluation for Health Professions.2023; 20: 39. CrossRef
Research article
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Performance of ChatGPT, Bard, Claude, and Bing on the Peruvian National Licensing Medical Examination: a cross-sectional study
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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 Panta Quezada, Jesus Daniel Gutierrez-Arratia, Javier Alejandro Flores-Cohaila
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J Educ Eval Health Prof. 2023;20:30. Published online November 20, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.30
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893
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Abstract
PDFSupplementary Material
- Purpose
We aimed to describe the performance and evaluate the educational value of justifications provided by artificial intelligence chatbots, including GPT-3.5, GPT-4, Bard, Claude, and Bing, on the Peruvian National Medical Licensing Examination (P-NLME).
Methods
This was a cross-sectional analytical study. On July 25, 2023, each multiple-choice question (MCQ) from the P-NLME was entered into each chatbot (GPT-3, GPT-4, Bing, Bard, and Claude) 3 times. Then, 4 medical educators categorized the MCQs in terms of medical area, item type, and whether the MCQ required Peru-specific knowledge. They assessed the educational value of the justifications from the 2 top performers (GPT-4 and Bing).
Results
GPT-4 scored 86.7% and Bing scored 82.2%, followed by Bard and Claude, and the historical performance of Peruvian examinees was 55%. Among the factors associated with correct answers, only MCQs that required Peru-specific knowledge had lower odds (odds ratio, 0.23; 95% confidence interval, 0.09–0.61), whereas the remaining factors showed no associations. In assessing the educational value of justifications provided by GPT-4 and Bing, neither showed any significant differences in certainty, usefulness, or potential use in the classroom.
Conclusion
Among chatbots, GPT-4 and Bing were the top performers, with Bing performing better at Peru-specific MCQs. Moreover, the educational value of justifications provided by the GPT-4 and Bing could be deemed appropriate. However, it is essential to start addressing the educational value of these chatbots, rather than merely their performance on examinations.
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Citations
Citations to this article as recorded by
- Performance of GPT-4V in answering the Japanese otolaryngology board certification examination questions: An evaluation study (Preprint)
Masao Noda, Takayoshi Ueno, Ryota Koshu, Yuji Takaso, Mari Dias Shimada, Chizu Saito, Hisashi Sugimoto, Hiroaki Fushiki, Makoto Ito, Akihiro Nomura, Tomokazu Yoshizaki
JMIR Medical Education.2024;[Epub] 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
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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6,845
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612
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29
Web of Science
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34
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Abstract
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.
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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
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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
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Journal of Educational Evaluation for Health Professions.2023; 20: 40. CrossRef
Research articles
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Negative effects on medical students’ scores for clinical performance during the COVID-19 pandemic in Taiwan: a comparative study
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Eunice Jia-Shiow Yuan, Shiau-Shian Huang, Chia-An Hsu, Jiing-Feng Lirng, Tzu-Hao Li, Chia-Chang Huang, Ying-Ying Yang, Chung-Pin Li, Chen-Huan Chen
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J Educ Eval Health Prof. 2023;20:37. Published online December 26, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.37
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Abstract
PDFSupplementary Material
- Purpose
Coronavirus disease 2019 (COVID-19) has heavily impacted medical clinical education in Taiwan. Medical curricula have been altered to minimize exposure and limit transmission. This study investigated the effect of COVID-19 on Taiwanese medical students’ clinical performance using online standardized evaluation systems and explored the factors influencing medical education during the pandemic.
Methods
Medical students were scored from 0 to 100 based on their clinical performance from 1/1/2018 to 6/31/2021. The students were placed into pre-COVID-19 (before 2/1/2020) and midst-COVID-19 (on and after 2/1/2020) groups. Each group was further categorized into COVID-19-affected specialties (pulmonary, infectious, and emergency medicine) and other specialties. Generalized estimating equations (GEEs) were used to compare and examine the effects of relevant variables on student performance.
Results
In total, 16,944 clinical scores were obtained for COVID-19-affected specialties and other specialties. For the COVID-19-affected specialties, the midst-COVID-19 score (88.513.52) was significantly lower than the pre-COVID-19 score (90.143.55) (P<0.0001). For the other specialties, the midst-COVID-19 score (88.323.68) was also significantly lower than the pre-COVID-19 score (90.063.58) (P<0.0001). There were 1,322 students (837 males and 485 females). Male students had significantly lower scores than female students (89.333.68 vs. 89.993.66, P=0.0017). GEE analysis revealed that the COVID-19 pandemic (unstandardized beta coefficient=-1.99, standard error [SE]=0.13, P<0.0001), COVID-19-affected specialties (B=0.26, SE=0.11, P=0.0184), female students (B=1.10, SE=0.20, P<0.0001), and female attending physicians (B=-0.19, SE=0.08, P=0.0145) were independently associated with students’ scores.
Conclusion
COVID-19 negatively impacted medical students' clinical performance, regardless of their specialty. Female students outperformed male students, irrespective of the pandemic.
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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
PDFSupplementary 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.