Most-read articles are from the articles published in 2022 during the last three month.
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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10,493
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994
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99
Web of Science
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58
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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
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Discover Education.2024;[Epub] CrossRef - Performance of ChatGPT on Ophthalmology-Related Questions Across Various Examination Levels: Observational Study
Firas Haddad, Joanna S Saade
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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
Ranwir K Sinha, Asitava Deb Roy, Nikhil Kumar, Himel Mondal
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
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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
Omar Temsah, Samina A Khan, Yazan Chaiah, Abdulrahman Senjab, Khalid Alhasan, Amr Jamal, Fadi Aljamaan, Khalid H Malki, Rabih Halwani, Jaffar A Al-Tawfiq, Mohamad-Hani Temsah, Ayman Al-Eyadhy
Cureus.2023;[Epub] CrossRef - ChatGPT for Future Medical and Dental Research
Bader Fatani
Cureus.2023;[Epub] CrossRef - ChatGPT in Dentistry: A Comprehensive Review
Hind M Alhaidry, Bader Fatani, Jenan O Alrayes, Aljowhara M Almana, Nawaf K Alfhaed
Cureus.2023;[Epub] CrossRef - Can we trust AI chatbots’ answers about disease diagnosis and patient care?
Sun Huh
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
Hsing-Yu Hsu, Kai-Cheng Hsu, Shih-Yen Hou, Ching-Lung Wu, Yow-Wen Hsieh, Yih-Dih Cheng
JMIR Medical Education.2023; 9: e48433. CrossRef - Assessing the Efficacy of ChatGPT in Solving Questions Based on the Core Concepts in Physiology
Arijita Banerjee, Aquil Ahmad, Payal Bhalla, Kavita Goyal
Cureus.2023;[Epub] CrossRef - ChatGPT Performs on the Chinese National Medical Licensing Examination
Xinyi Wang, Zhenye Gong, Guoxin Wang, Jingdan Jia, Ying Xu, Jialu Zhao, Qingye Fan, Shaun Wu, Weiguo Hu, Xiaoyang Li
Journal of Medical Systems.2023;[Epub] CrossRef - Artificial intelligence and its impact on job opportunities among university students in North Lima, 2023
Doris Ruiz-Talavera, Jaime Enrique De la Cruz-Aguero, Nereo García-Palomino, Renzo Calderón-Espinoza, William Joel Marín-Rodriguez
ICST Transactions on Scalable Information Systems.2023;[Epub] CrossRef - Revolutionizing Dental Care: A Comprehensive Review of Artificial Intelligence Applications Among Various Dental Specialties
Najd Alzaid, Omar Ghulam, Modhi Albani, Rafa Alharbi, Mayan Othman, Hasan Taher, Saleem Albaradie, Suhael Ahmed
Cureus.2023;[Epub] CrossRef - Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review
Carl Preiksaitis, Christian Rose
JMIR Medical Education.2023; 9: e48785. CrossRef - Exploring the impact of language models, such as ChatGPT, on student learning and assessment
Araz Zirar
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
Mohd Afjal
Library Hi Tech.2023;[Epub] CrossRef - The Significance of Artificial Intelligence Platforms in Anatomy Education: An Experience With ChatGPT and Google Bard
Hasan B Ilgaz, Zehra Çelik
Cureus.2023;[Epub] CrossRef - Is ChatGPT’s Knowledge and Interpretative Ability Comparable to First Professional MBBS (Bachelor of Medicine, Bachelor of Surgery) Students of India in Taking a Medical Biochemistry Examination?
Abhra Ghosh, Nandita Maini Jindal, Vikram K Gupta, Ekta Bansal, Navjot Kaur Bajwa, Abhishek Sett
Cureus.2023;[Epub] CrossRef - Ethical consideration of the use of generative artificial intelligence, including ChatGPT in writing a nursing article
Sun Huh
Child Health Nursing Research.2023; 29(4): 249. CrossRef - Potential Use of ChatGPT for Patient Information in Periodontology: A Descriptive Pilot Study
Osman Babayiğit, Zeynep Tastan Eroglu, Dilek Ozkan Sen, Fatma Ucan Yarkac
Cureus.2023;[Epub] CrossRef - Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in medical education in Serbia: a descriptive study
Aleksandra Ignjatović, Lazar Stevanović
Journal of Educational Evaluation for Health Professions.2023; 20: 28. CrossRef - Assessing the Performance of ChatGPT in Medical Biochemistry Using Clinical Case Vignettes: Observational Study
Krishna Mohan Surapaneni
JMIR Medical Education.2023; 9: e47191. CrossRef - 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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No difference in factual or conceptual recall comprehension for tablet, laptop, and handwritten note-taking by medical students in the United States: a survey-based observational study
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Warren Wiechmann, Robert Edwards, Cheyenne Low, Alisa Wray, Megan Boysen-Osborn, Shannon Toohey
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J Educ Eval Health Prof. 2022;19:8. Published online April 26, 2022
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DOI: https://doi.org/10.3352/jeehp.2022.19.8
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Abstract
PDFSupplementary Material
- Purpose
Technological advances are changing how students approach learning. The traditional note-taking methods of longhand writing have been supplemented and replaced by tablets, smartphones, and laptop note-taking. It has been theorized that writing notes by hand requires more complex cognitive processes and may lead to better retention. However, few studies have investigated the use of tablet-based note-taking, which allows the incorporation of typing, drawing, highlights, and media. We therefore sought to confirm the hypothesis that tablet-based note-taking would lead to equivalent or better recall as compared to written note-taking.
Methods
We allocated 68 students into longhand, laptop, or tablet note-taking groups, and they watched and took notes on a presentation on which they were assessed for factual and conceptual recall. A second short distractor video was shown, followed by a 30-minute assessment at the University of California, Irvine campus, over a single day period in August 2018. Notes were analyzed for content, supplemental drawings, and other media sources.
Results
No significant difference was found in the factual or conceptual recall scores for tablet, laptop, and handwritten note-taking (P=0.61). The median word count was 131.5 for tablets, 121.0 for handwriting, and 297.0 for laptops (P=0.01). The tablet group had the highest presence of drawing, highlighting, and other media/tools.
Conclusion
In light of conflicting research regarding the best note-taking method, our study showed that longhand note-taking is not superior to tablet or laptop note-taking. This suggests students should be encouraged to pick the note-taking method that appeals most to them. In the future, traditional note-taking may be replaced or supplemented with digital technologies that provide similar efficacy with more convenience.
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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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
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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
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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
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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
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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
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F1000Research.2023; 12: 1398. CrossRef - Ethical consideration of the use of generative artificial intelligence, including ChatGPT in writing a nursing article
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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
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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
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European Journal of Therapeutics.2023;[Epub] CrossRef - May Artificial Intelligence Be a Co-Author on an Academic Paper?
Ayşe Balat, İlhan Bahşi
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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
Brief report
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Training and implementation of handheld ultrasound technology at Georgetown Public Hospital Corporation in Guyana: a virtual learning cohort study
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Michelle Bui, Adrian Fernandez, Budheshwar Ramsukh, Onika Noel, Chris Prashad, David Bayne
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J Educ Eval Health Prof. 2023;20:11. Published online April 4, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.11
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2,026
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Abstract
PDFSupplementary Material
- A virtual point-of-care ultrasound (POCUS) education program was initiated to introduce handheld ultrasound technology to Georgetown Public Hospital Corporation in Guyana, a low-resource setting. We studied ultrasound competency and participant satisfaction in a cohort of 20 physicians-in-training through the urology clinic. The program consisted of a training phase, where they learned how to use the Butterfly iQ ultrasound, and a mentored implementation phase, where they applied their skills in the clinic. The assessment was through written exams and an objective structured clinical exam (OSCE). Fourteen students completed the program. The written exam scores were 3.36/5 in the training phase and 3.57/5 in the mentored implementation phase, and all students earned 100% on the OSCE. Students expressed satisfaction with the program. Our POCUS education program demonstrates the potential to teach clinical skills in low-resource settings and the value of virtual global health partnerships in advancing POCUS and minimally invasive diagnostics.
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Citations
Citations to this article as recorded by
- Efficacy of Handheld Ultrasound in Medical Education: A Comprehensive Systematic Review and Narrative Analysis
Mariam Haji-Hassan, Roxana-Denisa Capraș, Sorana D. Bolboacă
Diagnostics.2023; 13(24): 3665. CrossRef
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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- 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
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 article
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Medical students’ self-assessed efficacy and satisfaction with training on endotracheal intubation and central venous catheterization with smart glasses in Taiwan: a non-equivalent control-group pre- and post-test study
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Yu-Fan Lin, Chien-Ying Wang, Yen-Hsun Huang, Sheng-Min Lin, Ying-Ying Yang
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J Educ Eval Health Prof. 2022;19:25. Published online September 2, 2022
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DOI: https://doi.org/10.3352/jeehp.2022.19.25
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Abstract
PDFSupplementary Material
- Purpose
Endotracheal intubation and central venous catheterization are essential procedures in clinical practice. Simulation-based technology such as smart glasses has been used to facilitate medical students’ training on these procedures. We investigated medical students’ self-assessed efficacy and satisfaction regarding the practice and training of these procedures with smart glasses in Taiwan.
Methods
This observational study enrolled 145 medical students in the 5th and 6th years participating in clerkships at Taipei Veterans General Hospital between October 2020 and December 2021. Students were divided into the smart glasses or the control group and received training at a workshop. The primary outcomes included students’ pre- and post-intervention scores for self-assessed efficacy and satisfaction with the training tool, instructor’s teaching, and the workshop.
Results
The pre-intervention scores for self-assessed efficacy of 5th- and 6th-year medical students in endotracheal intubation and central venous catheterization procedures showed no significant difference. The post-intervention score of self-assessed efficacy in the smart glasses group was better than that of the control group. Moreover, 6th-year medical students in the smart glasses group showed higher satisfaction with the training tool, instructor’s teaching, and workshop than those in the control group.
Conclusion
Smart glasses served as a suitable simulation tool for endotracheal intubation and central venous catheterization procedures training in medical students. Medical students practicing with smart glasses showed improved self-assessed efficacy and higher satisfaction with training, especially for procedural steps in a space-limited field. Simulation training on procedural skills with smart glasses in 5th-year medical students may be adjusted to improve their satisfaction.
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- The use of smart glasses in nursing education: A scoping review
Charlotte Romare, Lisa Skär
Nurse Education in Practice.2023; 73: 103824. CrossRef
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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1,825
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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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- 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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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.
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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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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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- 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
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Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in medical education in Serbia: a descriptive study
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Aleksandra Ignjatović, Lazar Stevanović
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J Educ Eval Health Prof. 2023;20:28. Published online October 16, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.28
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1,480
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Abstract
PDFSupplementary Material
- Purpose
This study aimed to assess the performance of ChatGPT (GPT-3.5 and GPT-4) as a study tool in solving biostatistical problems and to identify any potential drawbacks that might arise from using ChatGPT in medical education, particularly in solving practical biostatistical problems.
Methods
ChatGPT was tested to evaluate its ability to solve biostatistical problems from the Handbook of Medical Statistics by Peacock and Peacock in this descriptive study. Tables from the problems were transformed into textual questions. Ten biostatistical problems were randomly chosen and used as text-based input for conversation with ChatGPT (versions 3.5 and 4).
Results
GPT-3.5 solved 5 practical problems in the first attempt, related to categorical data, cross-sectional study, measuring reliability, probability properties, and the t-test. GPT-3.5 failed to provide correct answers regarding analysis of variance, the chi-square test, and sample size within 3 attempts. GPT-4 also solved a task related to the confidence interval in the first attempt and solved all questions within 3 attempts, with precise guidance and monitoring.
Conclusion
The assessment of both versions of ChatGPT performance in 10 biostatistical problems revealed that GPT-3.5 and 4’s performance was below average, with correct response rates of 5 and 6 out of 10 on the first attempt. GPT-4 succeeded in providing all correct answers within 3 attempts. These findings indicate that students must be aware that this tool, even when providing and calculating different statistical analyses, can be wrong, and they should be aware of ChatGPT’s limitations and be careful when incorporating this model into medical education.
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Citations
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- Can Generative AI and ChatGPT Outperform Humans on Cognitive-Demanding Problem-Solving Tasks in Science?
Xiaoming Zhai, Matthew Nyaaba, Wenchao Ma
Science & Education.2024;[Epub] CrossRef
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Mentorship and self-efficacy are associated with lower burnout in physical therapists in the United States: a cross-sectional survey study
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Matthew Pugliese, Jean-Michel Brismée, Brad Allen, Sean Riley, Justin Tammany, Paul Mintken
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J Educ Eval Health Prof. 2023;20:27. Published online September 27, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.27
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Abstract
PDFSupplementary Material
- Purpose
This study investigated the prevalence of burnout in physical therapists in the United States and the relationships between burnout and education, mentorship, and self-efficacy.
Methods
This was a cross-sectional survey study. An electronic survey was distributed to practicing physical therapists across the United States over a 6-week period from December 2020 to January 2021. The survey was completed by 2,813 physical therapists from all states. The majority were female (68.72%), White or Caucasian (80.13%), and employed full-time (77.14%). Respondents completed questions on demographics, education, mentorship, self-efficacy, and burnout. The Burnout Clinical Subtypes Questionnaire 12 (BCSQ-12) and self-reports were used to quantify burnout, and the General Self-Efficacy Scale (GSES) was used to measure self-efficacy. Descriptive and inferential analyses were performed.
Results
Respondents from home health (median BCSQ-12=42.00) and skilled nursing facility settings (median BCSQ-12=42.00) displayed the highest burnout scores. Burnout was significantly lower among those who provided formal mentorship (median BCSQ-12=39.00, P=0.0001) compared to no mentorship (median BCSQ-12=41.00). Respondents who received formal mentorship (median BCSQ-12=38.00, P=0.0028) displayed significantly lower burnout than those who received no mentorship (median BCSQ-12=41.00). A moderate negative correlation (rho=-0.49) was observed between the GSES and burnout scores. A strong positive correlation was found between self-reported burnout status and burnout scores (rrb=0.61).
Conclusion
Burnout is prevalent in the physical therapy profession, as almost half of respondents (49.34%) reported burnout. Providing or receiving mentorship and higher self-efficacy were associated with lower burnout. Organizations should consider measuring burnout levels, investing in mentorship programs, and implementing strategies to improve self-efficacy.
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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
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Janghee Park
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J Educ Eval Health Prof. 2023;20:29. Published online November 10, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.29
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1,015
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Abstract
PDFSupplementary Material
- Purpose
This study aimed to analyze patterns of using ChatGPT before and after group activities and to explore medical students’ perceptions of ChatGPT as a feedback tool in the classroom.
Methods
The study included 99 2nd-year pre-medical students who participated in a “Leadership and Communication” course from March to June 2023. Students engaged in both individual and group activities related to negotiation strategies. ChatGPT was used to provide feedback on their solutions. A survey was administered to assess students’ perceptions of ChatGPT’s feedback, its use in the classroom, and the strengths and challenges of ChatGPT from May 17 to 19, 2023.
Results
The students responded by indicating that ChatGPT’s feedback was helpful, and revised and resubmitted their group answers in various ways after receiving feedback. The majority of respondents expressed agreement with the use of ChatGPT during class. The most common response concerning the appropriate context of using ChatGPT’s feedback was “after the first round of discussion, for revisions.” There was a significant difference in satisfaction with ChatGPT’s feedback, including correctness, usefulness, and ethics, depending on whether or not ChatGPT was used during class, but there was no significant difference according to gender or whether students had previous experience with ChatGPT. The strongest advantages were “providing answers to questions” and “summarizing information,” and the worst disadvantage was “producing information without supporting evidence.”
Conclusion
The students were aware of the advantages and disadvantages of ChatGPT, and they had a positive attitude toward using ChatGPT in the classroom.
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Citations
Citations to this article as recorded by
- ChatGPT and Clinical Training: Perception, Concerns, and Practice of Pharm-D Students
Mohammed Zawiah, Fahmi Al-Ashwal, Lobna Gharaibeh, Rana Abu Farha, Karem Alzoubi, Khawla Abu Hammour, Qutaiba A Qasim, Fahd Abrah
Journal of Multidisciplinary Healthcare.2023; Volume 16: 4099. 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
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Effect of motion-graphic video-based training on the performance of operating room nurse students in cataract surgery in Iran: a randomized controlled study
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Behnaz Fatahi, Samira Fatahi, Sohrab Nosrati, Masood Bagheri
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J Educ Eval Health Prof. 2023;20:34. Published online November 28, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.34
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Abstract
PDFSupplementary Material
- Purpose
The present study was conducted to determine the effect of motion-graphic video-based training on the performance of operating room nurse students in cataract surgery using phacoemulsification at Kermanshah University of Medical Sciences in Iran.
Methods
This was a randomized controlled study conducted among 36 students training to become operating room nurses. The control group only received routine training, and the intervention group received motion-graphic video-based training on the scrub nurse’s performance in cataract surgery in addition to the educator’s training. The performance of the students in both groups as scrub nurses was measured through a researcher-made checklist in a pre-test and a post-test.
Results
The mean scores for performance in the pre-test and post-test were 17.83 and 26.44 in the control group and 18.33 and 50.94 in the intervention group, respectively, and a significant difference was identified between the mean scores of the pre- and post-test in both groups (P=0.001). The intervention also led to a significant increase in the mean performance score in the intervention group compared to the control group (P=0.001).
Conclusion
Considering the significant difference in the performance score of the intervention group compared to the control group, motion-graphic video-based training had a positive effect on the performance of operating room nurse students, and such training can be used to improve clinical training.
Brief report
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Initial steps for integrating academic electronic health records into clinical curricula of physical and occupational therapy in the United States: a survey-based observational study
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Stephen Burrows, Lola Halperin, Eric Nemec, Wendy Romney
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J Educ Eval Health Prof. 2022;19:24. Published online September 2, 2022
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DOI: https://doi.org/10.3352/jeehp.2022.19.24
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Abstract
PDFSupplementary Material
- Training programs must be designed to prepare physical and occupational therapy students to use electronic health records (EHR) and interprofessional collaboration. This report aims to describe physical and occupational therapy students’ perceptions of integrating an academic EHR (AEHR) in their problem-based learning (PBL) curricula in the College of Health Professions, Sacred Heart University, Fairfield, Connecticut, the United States. A paper-based case approach to PBL was adapted by creating patient cases in an AEHR. Students were asked to complete chart reviews and review provider notes to enhance their learning. An online survey was conducted to determine their perceptions of using AEHR from May 2014 to August 2015. Eighty-five students completed the survey, and 88.1% felt that using an AEHR was needed, and 82.4% felt that the additional notes enhanced their understanding of the interdisciplinary team. However, 83.5% reported the AEHR system increased the time needed to extract meaningful information. Incorporating an AEHR into curricula is essential to ensure students are adequately prepared for future patient interactions.