Skip Navigation
Skip to contents

JEEHP : Journal of Educational Evaluation for Health Professions

OPEN ACCESS
SEARCH
Search

Article category

Page Path
HOME > Article category > Article category
499 Article category
Filter
Filter
Article category
Keywords
Publication year
Authors
Funded articles
Brief report
Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study  
Sun Huh
J Educ Eval Health Prof. 2023;20:1.   Published online January 11, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.1
  • 11,216 View
  • 1,016 Download
  • 122 Web of Science
  • 66 Crossref
AbstractAbstract 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.

Citations

Citations to this article as recorded by  
  • Performance of ChatGPT on the India Undergraduate Community Medicine Examination: Cross-Sectional Study
    Aravind P Gandhi, Felista Karen Joesph, Vineeth Rajagopal, P Aparnavi, Sushma Katkuri, Sonal Dayama, Prakasini Satapathy, Mahalaqua Nazli Khatib, Shilpa Gaidhane, Quazi Syed Zahiruddin, Ashish Behera
    JMIR Formative Research.2024; 8: e49964.     CrossRef
  • 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
  • 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.2024; 52(2): 237.     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
  • Potential applications of ChatGPT in obstetrics and gynecology in Korea: a review article
    YooKyung Lee, So Yun Kim
    Obstetrics & Gynecology Science.2024; 67(2): 153.     CrossRef
  • Application of generative language models to orthopaedic practice
    Jessica Caterson, Olivia Ambler, Nicholas Cereceda-Monteoliva, Matthew Horner, Andrew Jones, Arwel Tomos Poacher
    BMJ Open.2024; 14(3): e076484.     CrossRef
  • Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
    Xiaojun Xu, Yixiao Chen, Jing Miao
    Journal of Educational Evaluation for Health Professions.2024; 21: 6.     CrossRef
  • The advent of ChatGPT: Job Made Easy or Job Loss to Data Analysts
    Abiola Timothy Owolabi, Oluwaseyi Oluwadamilare Okunlola, Emmanuel Taiwo Adewuyi, Janet Iyabo Idowu, Olasunkanmi James Oladapo
    WSEAS TRANSACTIONS ON COMPUTERS.2024; 23: 24.     CrossRef
  • ChatGPT in dentomaxillofacial radiology education
    Hilal Peker Öztürk, Hakan Avsever, Buğra Şenel, Şükran Ayran, Mustafa Çağrı Peker, Hatice Seda Özgedik, Nurten Baysal
    Journal of Health Sciences and Medicine.2024; 7(2): 224.     CrossRef
  • Performance of ChatGPT on the Korean National Examination for Dental Hygienists
    Soo-Myoung Bae, Hye-Rim Jeon, Gyoung-Nam Kim, Seon-Hui Kwak, Hyo-Jin Lee
    Journal of Dental Hygiene Science.2024; 24(1): 62.     CrossRef
  • Medical knowledge of ChatGPT in public health, infectious diseases, COVID-19 pandemic, and vaccines: multiple choice questions examination based performance
    Sultan Ayoub Meo, Metib Alotaibi, Muhammad Zain Sultan Meo, Muhammad Omair Sultan Meo, Mashhood Hamid
    Frontiers in Public Health.2024;[Epub]     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
    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
    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
  • FROM TEXT TO DIAGNOSE: CHATGPT’S EFFICACY IN MEDICAL DECISION-MAKING
    Yaroslav Mykhalko, Pavlo Kish, Yelyzaveta Rubtsova, Oleksandr Kutsyn, Valentyna Koval
    Wiadomości Lekarskie.2023; 76(11): 2345.     CrossRef
  • Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students’ and Physicians’ Perceptions
    Pasin Tangadulrat, Supinya Sono, Boonsin Tangtrakulwanich
    JMIR Medical Education.2023; 9: e50658.     CrossRef
  • Below average ChatGPT performance in medical microbiology exam compared to university students
    Malik Sallam, Khaled Al-Salahat
    Frontiers in Education.2023;[Epub]     CrossRef
  • ChatGPT: "To be or not to be" ... in academic research. The human mind's analytical rigor and capacity to discriminate between AI bots' truths and hallucinations
    Aurelian Anghelescu, Ilinca Ciobanu, Constantin Munteanu, Lucia Ana Maria Anghelescu, Gelu Onose
    Balneo and PRM Research Journal.2023; 14(Vol.14, no): 614.     CrossRef
  • ChatGPT Review: A Sophisticated Chatbot Models in Medical & Health-related Teaching and Learning
    Nur Izah Ab Razak, Muhammad Fawwaz Muhammad Yusoff, Rahmita Wirza O.K. Rahmat
    Malaysian Journal of Medicine and Health Sciences.2023; 19(s12): 98.     CrossRef
  • Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review
    Tae Won Kim
    Journal of Educational Evaluation for Health Professions.2023; 20: 38.     CrossRef
  • Trends in research on ChatGPT and adoption-related issues discussed in articles: a narrative review
    Sang-Jun Kim
    Science Editing.2023; 11(1): 3.     CrossRef
  • Information amount, accuracy, and relevance of generative artificial intelligence platforms’ answers regarding learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study
    Hyunju Lee, Soobin Park
    Journal of Educational Evaluation for Health Professions.2023; 20: 39.     CrossRef
Editorial
Presidential address: reflection on work from April 2019 to 2022 and appreciation to the staff and volunteers
Yoon-Seong Lee
J Educ Eval Health Prof. 2022;19:39.   Published online December 30, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.39
  • 1,045 View
  • 87 Download
  • 1 Web of Science
  • 1 Crossref
PDF

Citations

Citations to this article as recorded by  
  • 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
Review
Factors associated with medical students’ scores on the National Licensing Exam in Peru: a systematic review  
Javier Alejandro Flores-Cohaila
J Educ Eval Health Prof. 2022;19:38.   Published online December 29, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.38
  • 2,984 View
  • 266 Download
  • 1 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
This study aimed to identify factors that have been studied for their associations with National Licensing Examination (ENAM) scores in Peru.
Methods
A search was conducted of literature databases and registers, including EMBASE, SciELO, Web of Science, MEDLINE, Peru’s National Register of Research Work, and Google Scholar. The following key terms were used: “ENAM” and “associated factors.” Studies in English and Spanish were included. The quality of the included studies was evaluated using the Medical Education Research Study Quality Instrument (MERSQI).
Results
In total, 38,500 participants were enrolled in 12 studies. Most (11/12) studies were cross-sectional, except for one case-control study. Three studies were published in peer-reviewed journals. The mean MERSQI was 10.33. A better performance on the ENAM was associated with a higher-grade point average (GPA) (n=8), internship setting in EsSalud (n=4), and regular academic status (n=3). Other factors showed associations in various studies, such as medical school, internship setting, age, gender, socioeconomic status, simulations test, study resources, preparation time, learning styles, study techniques, test-anxiety, and self-regulated learning strategies.
Conclusion
The ENAM is a multifactorial phenomenon; our model gives students a locus of control on what they can do to improve their score (i.e., implement self-regulated learning strategies) and faculty, health policymakers, and managers a framework to improve the ENAM score (i.e., design remediation programs to improve GPA and integrate anxiety-management courses into the curriculum).

Citations

Citations to this article as recorded by  
  • Performance of ChatGPT on the Peruvian National Licensing Medical Examination: Cross-Sectional Study
    Javier A Flores-Cohaila, Abigaíl García-Vicente, Sonia F Vizcarra-Jiménez, Janith P De la Cruz-Galán, Jesús D Gutiérrez-Arratia, Blanca Geraldine Quiroga Torres, Alvaro Taype-Rondan
    JMIR Medical Education.2023; 9: e48039.     CrossRef
Research article
Suggestion of more suitable study designs and the corresponding reporting guidelines in articles published in the Journal of Educational Evaluation for Health Professions from 2021 to September 2022: a descriptive study  
Soo Young Kim
J Educ Eval Health Prof. 2022;19:36.   Published online December 26, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.36
  • 1,299 View
  • 109 Download
  • 3 Web of Science
  • 3 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
This study aimed to suggest a more suitable study design and the corresponding reporting guidelines in the papers published in the Journal of Educational Evaluation for Health Professionals from January 2021 to September 2022.
Methods
Among 59 papers published in the Journal of Educational Evaluation for Health Professionals from January 2021 to September 2022, research articles, review articles, and brief reports were selected. The followings were analyzed: first, the percentage of articles describing the study design in the title, abstracts, or methods; second, the portion of articles describing reporting guidelines; third, the types of study design and corresponding reporting guidelines; and fourth, the suggestion of a more suitable study design based on the study design algorithm for medical literature on interventions, systematic reviews & other review types, and epidemiological studies overview.
Results
Out of 45 articles, 44 described study designs (97.8%). Out of 44, 19 articles were suggested to be described with more suitable study designs, which mainly occurred in before-and-after studies, diagnostic research, and non-randomized trials. Of the 18 reporting guidelines mentioned, 8 (44.4%) were considered perfect. STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) was used for descriptive studies, before-and-after studies, and randomized controlled trials; however, its use should be reconsidered.
Conclusion
Some declarations of study design and reporting guidelines were suggested to be described with more suitable ones. Education and training on study design and reporting guidelines for researchers are needed, and reporting guideline policies for descriptive studies should also be implemented.

Citations

Citations to this article as recorded by  
  • 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
  • A comprehensive perspective on the interaction between gut microbiota and COVID-19 vaccines
    Ming Hong, Tin Lan, Qiuxia Li, Binfei Li, Yong Yuan, Feng Xu, Weijia Wang
    Gut Microbes.2023;[Epub]     CrossRef
  • Why do editors of local nursing society journals strive to have their journals included in MEDLINE? A case study of the Korean Journal of Women Health Nursing
    Sun Huh
    Korean Journal of Women Health Nursing.2023; 29(3): 147.     CrossRef
Review
Medical students’ satisfaction level with e-learning during the COVID-19 pandemic and its related factors: a systematic review  
Mahbubeh Tabatabaeichehr, Samane Babaei, Mahdieh Dartomi, Peiman Alesheikh, Amir Tabatabaee, Hamed Mortazavi, Zohreh Khoshgoftar
J Educ Eval Health Prof. 2022;19:37.   Published online December 20, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.37
  • 2,273 View
  • 208 Download
  • 7 Web of Science
  • 6 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
This review investigated medical students’ satisfaction level with e-learning during the coronavirus disease 2019 (COVID-19) pandemic and its related factors.
Methods
A comprehensive systematic search was performed of international literature databases, including Scopus, PubMed, Web of Science, and Persian databases such as Iranmedex and Scientific Information Database using keywords extracted from Medical Subject Headings such as “Distance learning,” “Distance education,” “Online learning,” “Online education,” and “COVID-19” from the earliest date to July 10, 2022. The quality of the studies included in this review was evaluated using the appraisal tool for cross-sectional studies (AXIS tool).
Results
A total of 15,473 medical science students were enrolled in 24 studies. The level of satisfaction with e-learning during the COVID-19 pandemic among medical science students was 51.8%. Factors such as age, gender, clinical year, experience with e-learning before COVID-19, level of study, adaptation content of course materials, interactivity, understanding of the content, active participation of the instructor in the discussion, multimedia use in teaching sessions, adequate time dedicated to the e-learning, stress perception, and convenience had significant relationships with the satisfaction of medical students with e-learning during the COVID-19 pandemic.
Conclusion
Therefore, due to the inevitability of online education and e-learning, it is suggested that educational managers and policymakers choose the best online education method for medical students by examining various studies in this field to increase their satisfaction with e-learning.

Citations

Citations to this article as recorded by  
  • Factors affecting medical students’ satisfaction with online learning: a regression analysis of a survey
    Özlem Serpil Çakmakkaya, Elif Güzel Meydanlı, Ali Metin Kafadar, Mehmet Selman Demirci, Öner Süzer, Muhlis Cem Ar, Muhittin Onur Yaman, Kaan Can Demirbaş, Mustafa Sait Gönen
    BMC Medical Education.2024;[Epub]     CrossRef
  • A comparative study on the effectiveness of online and in-class team-based learning on student performance and perceptions in virtual simulation experiments
    Jing Shen, Hongyan Qi, Ruhuan Mei, Cencen Sun
    BMC Medical Education.2024;[Epub]     CrossRef
  • Pharmacy Students’ Attitudes Toward Distance Learning After the COVID-19 Pandemic: Cross-Sectional Study From Saudi Arabia
    Saud Alsahali, Salman Almutairi, Salem Almutairi, Saleh Almofadhi, Mohammed Anaam, Mohammed Alshammari, Suhaj Abdulsalim, Yasser Almogbel
    JMIR Formative Research.2024; 8: e54500.     CrossRef
  • Effects of the First Wave of the COVID-19 Pandemic on the Work Readiness of Undergraduate Nursing Students in China: A Mixed-Methods Study
    Lifang He, Jean Rizza Dela Cruz
    Risk Management and Healthcare Policy.2024; Volume 17: 559.     CrossRef
  • Physician Assistant Students’ Perception of Online Didactic Education: A Cross-Sectional Study
    Daniel L Anderson, Jeffrey L Alexander
    Cureus.2023;[Epub]     CrossRef
  • Mediating Role of PERMA Wellbeing in the Relationship between Insomnia and Psychological Distress among Nursing College Students
    Qian Sun, Xiangyu Zhao, Yiming Gao, Di Zhao, Meiling Qi
    Behavioral Sciences.2023; 13(9): 764.     CrossRef
Educational/Faculty development material
Common models and approaches for the clinical educator to plan effective feedback encounters  
Cesar Orsini, Veena Rodrigues, Jorge Tricio, Margarita Rosel
J Educ Eval Health Prof. 2022;19:35.   Published online December 19, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.35
  • 4,587 View
  • 636 Download
  • 2 Web of Science
  • 3 Crossref
AbstractAbstract 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.

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 in Medical Education—Its Importance and How to Do It
    Tarik Babar, Omer A. Awan
    Academic Radiology.2024;[Epub]     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 articles
Physical therapy students’ perception of their ability of clinical and clinical decision-making skills enhanced after simulation-based learning courses in the United States: a repeated measures design  
Fabian Bizama, Mansoor Alameri, Kristy Jean Demers, Derrick Ferguson Campbell
J Educ Eval Health Prof. 2022;19:34.   Published online December 19, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.34
  • 2,345 View
  • 185 Download
  • 1 Web of Science
  • 3 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
It aimed to investigate physical therapy students’ perception of their ability of clinical and clinical decision-making skills after a simulation-based learning course in the United States.
Methods
Survey questionnaires were administered to voluntary participants, including 44 second and third-year physical therapy students of the University of St. Augustine for Health Sciences during 2021–2022. Thirty-six questionnaire items consisted of 4 demographic items, 1 general evaluation, 21 test items for clinical decision-making skills, and 4 clinical skill items. Descriptive and inferential statistics evaluated differences in students’ perception of their ability in clinical decision-making and clinical skills, pre- and post-simulation, and post-first clinical experience during 2021–2022.
Results
Friedman test revealed a significant increase from pre- to post-simulation in perception of the ability of clinical and clinical decision-making skills total tool score (P<0.001), clinical decision-making 21-item score (P<0.001), and clinical skills score (P<0.001). No significant differences were found between post-simulation and post-first clinical experience. Post-hoc tests indicated a significant difference between pre-simulation and post-simulation (P<0.001) and between pre-simulation and post-first clinical experience (P<0.001). Forty-three students (97.6%) either strongly agreed (59.1%) or agreed (38.5%) that simulation was a valuable learning experience.
Conclusion
The above findings suggest that simulation-based learning helped students begin their first clinical experience with enhanced clinical and clinical decision-making skills.

Citations

Citations to this article as recorded by  
  • Physiotherapists' training in oncology rehabilitation from entry‐level to advanced education: A qualitative study
    Gianluca Bertoni, Valentina Conti, Marco Testa, Ilaria Coppola, Stefania Costi, Simone Battista
    Physiotherapy Research International.2024;[Epub]     CrossRef
  • Simulación clínica mediada por tecnología: un escenario didáctico a partir de recursos para la formación de los profesionales en rehabilitación
    Cyndi Yacira Meneses Castaño, Isabel Jimenez Becerra, Paola Teresa Penagos Gomez
    Educación Médica.2023; 24(4): 100810.     CrossRef
  • Self-Efficacy with Telehealth Examination: the Doctor of Physical Therapy Student Perspective
    Derrick F. Campbell, Jean-Michel Brismee, Brad Allen, Troy Hooper, Manuel A. Domenech, Kathleen J. Manella
    Philippine Journal of Physical Therapy.2023; 2(2): 12.     CrossRef
Possibility of independent use of the yes/no Angoff and Hofstee methods for the standard setting of the Korean Medical Licensing Examination written test: a descriptive study  
Do-Hwan Kim, Ye Ji Kang, Hoon-Ki Park
J Educ Eval Health Prof. 2022;19:33.   Published online December 12, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.33
  • 1,589 View
  • 113 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
This study aims to apply the yes/no Angoff and Hofstee methods to actual Korean Medical Licensing Examination (KMLE) 2022 written examination data to estimate cut scores for the written KMLE.
Methods
Fourteen panelists gathered to derive the cut score of the 86th KMLE written examination data using the yes/no Angoff method. The panel reviewed the items individually before the meeting and shared their respective understanding of the minimum-competency physician. The standard setting process was conducted in 5 rounds over a total of 800 minutes. In addition, 2 rounds of the Hofstee method were conducted before starting the standard setting process and after the second round of yes/no Angoff.
Results
For yes/no Angoff, as each round progressed, the panel’s opinion gradually converged to a cut score of 198 points, and the final passing rate was 95.1%. The Hofstee cut score was 208 points out of a maximum 320 with a passing rate of 92.1% at the first round. It scored 204 points with a passing rate of 93.3% in the second round.
Conclusion
The difference between the cut scores obtained through yes/no Angoff and Hofstee methods did not exceed 2% points, and they were within the range of cut scores from previous studies. In both methods, the difference between the panelists decreased as rounds were repeated. Overall, our findings suggest the acceptability of cut scores and the possibility of independent use of both methods.

Citations

Citations to this article as recorded by  
  • 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
  • Presidential address: improving item validity and adopting computer-based testing, clinical skills assessments, artificial intelligence, and virtual reality in health professions licensing examinations in Korea
    Hyunjoo Pai
    Journal of Educational Evaluation for Health Professions.2023; 20: 8.     CrossRef
Brief report
Self-directed learning quotient and common learning types of pre-medical students in Korea by the Multi-Dimensional Learning Strategy Test 2nd edition: a descriptive study
Sun Kim, A Ra Cho, Chul Woon Chung
J Educ Eval Health Prof. 2022;19:32.   Published online November 28, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.32
  • 1,337 View
  • 129 Download
AbstractAbstract PDFSupplementary Material
This study aimed to find the self-directed learning quotient and common learning types of pre-medical students through the confirmation of 4 characteristics of learning strategies, including personality, motivation, emotion, and behavior. The response data were collected from 277 out of 294 target first-year pre-medical students from 2019 to 2021, using the Multi-Dimensional Learning Strategy Test 2nd edition. The most common learning type was a self-directed type (44.0%), stagnant type (33.9%), latent type (14.4%), and conscientiousness type (7.6%). The self-directed learning index was high (29.2%), moderate (24.6%), somewhat high (21.7%), somewhat low (14.4%), and low (10.1%). This study confirmed that many students lacked self-directed learning capabilities for learning strategies. In addition, it was found that the difficulties experienced by each student were different, and the variables resulting in difficulties were also diverse. It may provide insights into how to develop programs that can help students increase their self-directed learning capability.
Research articles
Medical student selection process enhanced by improving selection algorithms and changing the focus of interviews in Australia: a descriptive study
Boaz Shulruf, Gary Mayer Velan, Sean Edward Kennedy
J Educ Eval Health Prof. 2022;19:31.   Published online November 28, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.31
  • 1,701 View
  • 121 Download
AbstractAbstract PDFSupplementary Material
Purpose
The study investigates the efficacy of new features introduced to the selection process for medical school at the University of New South Wales, Australia: (1) considering the relative ranks rather than scores of the Undergraduate Medicine and Health Sciences Admission Test and Australian Tertiary Admission Rank; (2) structured interview focusing on interpersonal interaction and concerns should the applicants become students; and (3) embracing interviewers’ diverse perspectives.
Methods
Data from 5 cohorts of students were analyzed, comparing outcomes of the second year in the medicine program of 4 cohorts of the old selection process and 1 of the new process. The main analysis comprised multiple linear regression models for predicting academic, clinical, and professional outcomes, by section tools and demographic variables.
Results
Selection interview marks from the new interview (512 applicants, 2 interviewers each) were analyzed for inter-rater reliability, which identified a high level of agreement (kappa=0.639). No such analysis was possible for the old interview since it required interviewers to reach a consensus. Multivariate linear regression models utilizing outcomes for 5 cohorts (N=905) revealed that the new selection process was much more effective in predicting academic and clinical achievement in the program (R2=9.4%–17.8% vs. R2=1.5%–8.4%).
Conclusion
The results suggest that the medical student selection process can be significantly enhanced by employing a non-compensatory selection algorithm; and using a structured interview focusing on interpersonal interaction and concerns should the applicants become students; as well as embracing interviewers’ diverse perspectives.
Is online objective structured clinical examination teaching an acceptable replacement in post-COVID-19 medical education in the United Kingdom?: a descriptive study  
Vashist Motkur, Aniket Bharadwaj, Nimalesh Yogarajah
J Educ Eval Health Prof. 2022;19:30.   Published online November 7, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.30
  • 1,613 View
  • 129 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
Coronavirus disease 2019 (COVID-19) restrictions resulted in an increased emphasis on virtual communication in medical education. This study assessed the acceptability of virtual teaching in an online objective structured clinical examination (OSCE) series and its role in future education.
Methods
Six surgical OSCE stations were designed, covering common surgical topics, with specific tasks testing data interpretation, clinical knowledge, and communication skills. These were delivered via Zoom to students who participated in student/patient/examiner role-play. Feedback was collected by asking students to compare online teaching with previous experiences of in-person teaching. Descriptive statistics were used for Likert response data, and thematic analysis for free-text items.
Results
Sixty-two students provided feedback, with 81% of respondents finding online instructions preferable to paper equivalents. Furthermore, 65% and 68% found online teaching more efficient and accessible, respectively, than in-person teaching. Only 34% found communication with each other easier online; Forty percent preferred online OSCE teaching to in-person teaching. Students also expressed feedback in positive and negative free-text comments.
Conclusion
The data suggested that generally students were unwilling for online teaching to completely replace in-person teaching. The success of online teaching was dependent on the clinical skill being addressed; some were less amenable to a virtual setting. However, online OSCE teaching could play a role alongside in-person teaching.

Citations

Citations to this article as recorded by  
  • Feasibility and reliability of the pandemic-adapted online-onsite hybrid graduation OSCE in Japan
    Satoshi Hara, Kunio Ohta, Daisuke Aono, Toshikatsu Tamai, Makoto Kurachi, Kimikazu Sugimori, Hiroshi Mihara, Hiroshi Ichimura, Yasuhiko Yamamoto, Hideki Nomura
    Advances in Health Sciences Education.2023;[Epub]     CrossRef
  • Should Virtual Objective Structured Clinical Examination (OSCE) Teaching Replace or Complement Face-to-Face Teaching in the Post-COVID-19 Educational Environment: An Evaluation of an Innovative National COVID-19 Teaching Programme
    Charles Gamble, Alice Oatham, Raj Parikh
    Cureus.2023;[Epub]     CrossRef
Enhanced numeracy skills following team-based learning in United States pharmacy students: a longitudinal cohort study  
Rob Edwin Carpenter, Leanne Coyne, Dave Silberman, Jody Kyoto Takemoto
J Educ Eval Health Prof. 2022;19:29.   Published online October 27, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.29
  • 1,565 View
  • 150 Download
AbstractAbstract PDFSupplementary Material
Purpose
The literature suggests that the ability to numerate cannot be fully understood without accounting for the social context in which mathematical activity is represented. Team-based learning (TBL) is an andragogical approach with theoretical links to sociocultural and community-of-practice learning. This study aimed to quantitatively explore the impact of TBL instruction on numeracy development in 2 cohorts of pharmacy students and identify the impact of TBL instruction on numeracy development from a social perspective for healthcare education.
Methods
Two cohorts of students were administered the Health Science Reasoning Test-Numeracy (HSRT-N) before beginning pharmacy school. Two years after using TBL as the primary method of instruction, both comprehensive and domain data from the HSRT-N were analyzed.
Results
In total, 163 pharmacy student scores met the inclusion criteria. The students’ numeracy skills measured by HSRT-N improved after 2 years of TBL instruction.
Conclusion
Numeracy was the most significantly improved HSRT-N domain in pharmacy students following two years of TBL instruction. Although a closer examination of numeracy development in TBL is warranted, initial data suggest that TBL instruction may be an adequate proxy for advancing numeracy in a cohort of pharmacy students. TBL may encourage a social practice of mathematics to improve pharmacy students’ ability to numerate critically.
Factors affecting nursing and health technician students' satisfaction with distance learning during the COVID-19 pandemic in Morocco: a descriptive study  
Aziz Naciri, Mohamed Radid, Abderrahmane Achbani, Mohamed Amine Baba, Ahmed Kharbach, Ghizlane Chemsi
J Educ Eval Health Prof. 2022;19:28.   Published online October 17, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.28
  • 2,489 View
  • 241 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
Distance learning describes any learning based on the use of new multimedia technologies and the internet to allow students to acquire new knowledge and skills at a distance. This study aimed to determine satisfaction levels with distance learning and associated factors among nursing and health technician students during the coronavirus disease 2019 pandemic in Morocco.
Methods
An descriptive study was conducted between April and June 2022 among nursing and health technician students using a self-administered instrument. The student satisfaction questionnaire consists of 24 questions categorized into 6 subscales: instructor, technology, course setup, interaction, outcomes, and overall satisfaction. It was based on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Univariate and multivariate logistic regression analyses were conducted to identify factors associated with student satisfaction during distance learning.
Results
A total of 330 students participated in this study, and 176 students (53.3%) were satisfied with the distance learning activities. A mean score higher than 2.8 out of 5 was obtained for all subscales. Multiple regression analysis showed that students’ year of study (adjusted odds ratio [aOR], 2.34; 95% confidence interval [CI], 1.28–4.27) and internet quality (aOR, 0.47; 95% CI, 0.29–0.77) were the significant factors associated with students’ satisfaction during distance learning.
Conclusion
This study highlights the satisfaction level of students and factors that influenced it during distance learning. A thorough understanding of student satisfaction with digital environments will contribute to the successful implementation of distance learning devices in nursing.

Citations

Citations to this article as recorded by  
  • Satisfaction with online education among students, faculty, and parents before and after the COVID-19 outbreak: Evidence from a meta-analysis
    Tianyuan Xu, Ling Xue
    Frontiers in Psychology.2023;[Epub]     CrossRef
Equal Z standard-setting method to estimate the minimum number of panelists for a medical school’s objective structured clinical examination in Taiwan: a simulation study  
Ying-Ying Yang, Pin-Hsiang Huang, Ling-Yu Yang, Chia-Chang Huang, Chih-Wei Liu, Shiau-Shian Huang, Chen-Huan Chen, Fa-Yauh Lee, Shou-Yen Kao, Boaz Shulruf
J Educ Eval Health Prof. 2022;19:27.   Published online October 17, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.27
  • 1,657 View
  • 116 Download
AbstractAbstract PDFSupplementary Material
Purpose
Undertaking a standard-setting exercise is a common method for setting pass/fail cut scores for high-stakes examinations. The recently introduced equal Z standard-setting method (EZ method) has been found to be a valid and effective alternative for the commonly used Angoff and Hofstee methods and their variants. The current study aims to estimate the minimum number of panelists required for obtaining acceptable and reliable cut scores using the EZ method.
Methods
The primary data were extracted from 31 panelists who used the EZ method for setting cut scores for a 12-station of medical school’s final objective structured clinical examination (OSCE) in Taiwan. For this study, a new data set composed of 1,000 random samples of different panel sizes, ranging from 5 to 25 panelists, was established and analyzed. Analysis of variance was performed to measure the differences in the cut scores set by the sampled groups, across all sizes within each station.
Results
On average, a panel of 10 experts or more yielded cut scores with confidence more than or equal to 90% and 15 experts yielded cut scores with confidence more than or equal to 95%. No significant differences in cut scores associated with panel size were identified for panels of 5 or more experts.
Conclusion
The EZ method was found to be valid and feasible. Less than an hour was required for 12 panelists to assess 12 OSCE stations. Calculating the cut scores required only basic statistical skills.
Acceptability of the 8-case objective structured clinical examination of medical students in Korea using generalizability theory: a reliability study  
Song Yi Park, Sang-Hwa Lee, Min-Jeong Kim, Ki-Hwan Ji, Ji Ho Ryu
J Educ Eval Health Prof. 2022;19:26.   Published online September 8, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.26
  • 2,166 View
  • 209 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
This study investigated whether the reliability was acceptable when the number of cases in the objective structured clinical examination (OSCE) decreased from 12 to 8 using generalizability theory (GT).
Methods
This psychometric study analyzed the OSCE data of 439 fourth-year medical students conducted in the Busan and Gyeongnam areas of South Korea from July 12 to 15, 2021. The generalizability study (G-study) considered 3 facets—students (p), cases (c), and items (i)—and designed the analysis as p×(i:c) due to items being nested in a case. The acceptable generalizability (G) coefficient was set to 0.70. The G-study and decision study (D-study) were performed using G String IV ver. 6.3.8 (Papawork, Hamilton, ON, Canada).
Results
All G coefficients except for July 14 (0.69) were above 0.70. The major sources of variance components (VCs) were items nested in cases (i:c), from 51.34% to 57.70%, and residual error (pi:c), from 39.55% to 43.26%. The proportion of VCs in cases was negligible, ranging from 0% to 2.03%.
Conclusion
The case numbers decreased in the 2021 Busan and Gyeongnam OSCE. However, the reliability was acceptable. In the D-study, reliability was maintained at 0.70 or higher if there were more than 21 items/case in 8 cases and more than 18 items/case in 9 cases. However, according to the G-study, increasing the number of items nested in cases rather than the number of cases could further improve reliability. The consortium needs to maintain a case bank with various items to implement a reliable blueprinting combination for the OSCE.

Citations

Citations to this article as recorded by  
  • Applying the Generalizability Theory to Identify the Sources of Validity Evidence for the Quality of Communication Questionnaire
    Flávia Del Castanhel, Fernanda R. Fonseca, Luciana Bonnassis Burg, Leonardo Maia Nogueira, Getúlio Rodrigues de Oliveira Filho, Suely Grosseman
    American Journal of Hospice and Palliative Medicine®.2023;[Epub]     CrossRef

JEEHP : Journal of Educational Evaluation for Health Professions