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From articles published in Journal of Educational Evaluation for Health Professions during the past two years (2022 ~ ).

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
  • 13,704 View
  • 1,070 Download
  • 161 Web of Science
  • 80 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

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Review
Can an artificial intelligence chatbot be the author of a scholarly article?  
Ju Yoen Lee
J Educ Eval Health Prof. 2023;20:6.   Published online February 27, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.6
  • 10,022 View
  • 730 Download
  • 54 Web of Science
  • 49 Crossref
AbstractAbstract PDFSupplementary Material
At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) chatbot with amazing writing ability, caused a great sensation in academia. The chatbot turned out to be very capable, but also capable of deception, and the news broke that several researchers had listed the chatbot (including its earlier version) as co-authors of their academic papers. In response, Nature and Science expressed their position that this chatbot cannot be listed as an author in the papers they publish. Since an AI chatbot is not a human being, in the current legal system, the text automatically generated by an AI chatbot cannot be a copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. Current AI chatbots such as ChatGPT are much more advanced than search engines in that they produce original text, but they still remain at the level of a search engine in that they cannot take responsibility for their writing. For this reason, they also cannot be authors from the perspective of research ethics.

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Editorials
Application of computer-based testing in the Korean Medical Licensing Examination, the emergence of the metaverse in medical education, journal metrics and statistics, and appreciation to reviewers and volunteers
Sun Huh
J Educ Eval Health Prof. 2022;19:2.   Published online January 13, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.2
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PDFSupplementary Material

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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
J Educ Eval Health Prof. 2023;20:5.   Published online January 31, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.5
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Reviews
Prevalence of burnout and related factors in nursing faculty members: a systematic review  
Marziyeh Hosseini, Mitra Soltanian, Camellia Torabizadeh, Zahra Hadian Shirazi
J Educ Eval Health Prof. 2022;19:16.   Published online July 14, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.16
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AbstractAbstract PDFSupplementary Material
Purpose
The current study aimed to identify the prevalence of burnout and related factors in nursing faculty members through a systematic review of the literature.
Methods
A comprehensive search of electronic databases, including Scopus, PubMed, Web of Science, Iranmedex, and Scientific Information Database was conducted via keywords extracted from Medical Subject Headings, including burnout and nursing faculty, for studies published from database inception to April 1, 2022. The quality of the included studies in this review was assessed using the appraisal tool for cross-sectional studies.
Results
A total of 2,551 nursing faculty members were enrolled in 11 studies. The mean score of burnout in nursing faculty members based on the Maslach Burnout Inventory (MBI) was 59.28 out of 132. The burnout score in this study was presented in 3 MBI subscales: emotional exhaustion, 21.24 (standard deviation [SD]=9.70) out of 54; depersonalization, 5.88 (SD=4.20) out of 30; and personal accomplishment, 32.16 (SD=6.45) out of 48. Several factors had significant relationships with burnout in nursing faculty members, including gender, level of education, hours of work, number of classroom, students taught, full-time work, job pressure, perceived stress, subjective well-being, marital status, job satisfaction, work setting satisfaction, workplace empowerment, collegial support, management style, fulfillment of self-expectation, communication style, humor, and academic position.
Conclusion
Overall, the mean burnout scores in nursing faculty members were moderate. Therefore, health policymakers and managers can reduce the likelihood of burnout in nursing faculty members by using psychosocial interventions and support.

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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
  • 3,195 View
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  • 9 Web of Science
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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.

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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
Tae Won Kim
J Educ Eval Health Prof. 2023;20:38.   Published online December 27, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.38
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AbstractAbstract PDFSupplementary Material
This study aims to explore ChatGPT’s (GPT-3.5 version) functionalities, including reinforcement learning, diverse applications, and limitations. ChatGPT is an artificial intelligence (AI) chatbot powered by OpenAI’s Generative Pre-trained Transformer (GPT) model. The chatbot’s applications span education, programming, content generation, and more, demonstrating its versatility. ChatGPT can improve education by creating assignments and offering personalized feedback, as shown by its notable performance in medical exams and the United States Medical Licensing Exam. However, concerns include plagiarism, reliability, and educational disparities. It aids in various research tasks, from design to writing, and has shown proficiency in summarizing and suggesting titles. Its use in scientific writing and language translation is promising, but professional oversight is needed for accuracy and originality. It assists in programming tasks like writing code, debugging, and guiding installation and updates. It offers diverse applications, from cheering up individuals to generating creative content like essays, news articles, and business plans. Unlike search engines, ChatGPT provides interactive, generative responses and understands context, making it more akin to human conversation, in contrast to conventional search engines’ keyword-based, non-interactive nature. ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges. Nonetheless, ChatGPT is considered to be a transformative AI tool poised to redefine the future of generative technology. In conclusion, advancements in AI, such as ChatGPT, are altering how knowledge is acquired and applied, marking a shift from search engines to creativity engines. This transformation highlights the increasing importance of AI literacy and the ability to effectively utilize AI in various domains of life.

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Research article
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 Panta Quezada, Jesus Daniel Gutierrez-Arratia, Javier Alejandro Flores-Cohaila
J Educ Eval Health Prof. 2023;20:30.   Published online November 20, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.30
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AbstractAbstract 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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Educational/Faculty development material
Using a virtual flipped classroom model to promote critical thinking in online graduate courses in the United States: a case presentation  
Jennifer Tomesko, Deborah Cohen, Jennifer Bridenbaugh
J Educ Eval Health Prof. 2022;19:5.   Published online February 28, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.5
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AbstractAbstract PDFSupplementary Material
Flipped classroom models encourage student autonomy and reverse the order of traditional classroom content such as lectures and assignments. Virtual learning environments are ideal for executing flipped classroom models to improve critical thinking skills. This paper provides health professions faculty with guidance on developing a virtual flipped classroom in online graduate nutrition courses between September 2021 and January 2022 at the School of Health Professions, Rutgers The State University of New Jersey. Examples of pre-class, live virtual face-to-face, and post-class activities are provided. Active learning, immediate feedback, and enhanced student engagement in a flipped classroom may result in a more thorough synthesis of information, resulting in increased critical thinking skills. This article describes how a flipped classroom model design in graduate online courses that incorporate virtual face-to-face class sessions in a virtual learning environment can be utilized to promote critical thinking skills. Health professions faculty who teach online can apply the examples discussed to their online courses.

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Review
Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review  
Xiaojun Xu, Yixiao Chen, Jing Miao
J Educ Eval Health Prof. 2024;21:6.   Published online March 15, 2024
DOI: https://doi.org/10.3352/jeehp.2024.21.6
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AbstractAbstract PDFSupplementary Material
Background
ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored.
Methods
A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included.
Results
ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students.
Conclusion
ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.

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  • Chatbots in neurology and neuroscience: Interactions with students, patients and neurologists
    Stefano Sandrone
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    Buddhini Amarathunga
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Research articles
Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in medical education in Serbia: a descriptive study  
Aleksandra Ignjatović, Lazar Stevanović
J Educ Eval Health Prof. 2023;20:28.   Published online October 16, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.28
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AbstractAbstract 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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  • 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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    Ambadasu Bharatha, Nkemcho Ojeh, Ahbab Mohammad Fazle Rabbi, Michael Campbell, Kandamaran Krishnamurthy, Rhaheem Layne-Yarde, Alok Kumar, Dale Springer, Kenneth Connell, Md Anwarul Majumder
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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  
Janghee Park
J Educ Eval Health Prof. 2023;20:29.   Published online November 10, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.29
  • 2,507 View
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AbstractAbstract 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.

Citations

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  • Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
    Xiaojun Xu, Yixiao Chen, Jing Miao
    Journal of Educational Evaluation for Health Professions.2024; 21: 6.     CrossRef
  • Embracing ChatGPT for Medical Education: Exploring Its Impact on Doctors and Medical Students
    Yijun Wu, Yue Zheng, Baijie Feng, Yuqi Yang, Kai Kang, Ailin Zhao
    JMIR Medical Education.2024; 10: e52483.     CrossRef
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    Anita V Thomae, Claudia M Witt, Jürgen Barth
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    Guixia Pan, Jing Ni
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    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
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    Journal of Educational Evaluation for Health Professions.2023; 20: 39.     CrossRef
Priorities in updating training paradigms in orthopedic manual therapy: an international Delphi study  
Damian Keter, David Griswold, Kenneth Learman, Chad Cook
J Educ Eval Health Prof. 2023;20:4.   Published online January 27, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.4
  • 3,752 View
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AbstractAbstract PDFSupplementary Material
Purpose
Orthopedic manual therapy (OMT) education demonstrates significant variability between philosophies and while literature has offered a more comprehensive understanding of the contextual, patient specific, and technique factors which interact to influence outcome, most OMT training paradigms continue to emphasize the mechanical basis for OMT application. The purpose of this study was to establish consensus on modifications & adaptions to training paradigms which need to occur within OMT education to align with current evidence.
Methods
A 3-round Delphi survey instrument designed to identify foundational knowledge to include and omit from OMT education was completed by 28 educators working within high level manual therapy education programs internationally. Round 1 consisted of open-ended questions to identify content in each area. Round 2 and Round 3 allowed participants to rank the themes identified in Round 1.
Results
Consensus was reached on 25 content areas to include within OMT education, 1 content area to omit from OMT education, and 34 knowledge components which should be present in those providing OMT. Support was seen for education promoting understanding the complex psychological, neurophysiological, and biomechanical systems as they relate to both evaluation and treatment effect. While some concepts were more consistently supported there was significant variability in responses which is largely expected to be related to previous training.
Conclusion
The results of this study indicate manual therapy educators understanding of evidence-based practice as support for all 3 tiers of evidence were represented. The results of this study should guide OMT training program development and modification.

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    Jean-Pascal Grenier, Maria Rothmund
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Improvement of the clinical skills of nurse anesthesia students using mini-clinical evaluation exercises in Iran: a randomized controlled study  
Ali Khalafi, Yasamin Sharbatdar, Nasrin Khajeali, Mohammad Hosein Haghighizadeh, Mahshid Vaziri
J Educ Eval Health Prof. 2023;20:12.   Published online April 6, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.12
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AbstractAbstract PDFSupplementary Material
Purpose
The present study aimed to investigate the effect of a mini-clinical evaluation exercise (CEX) assessment on improving the clinical skills of nurse anesthesia students at Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Methods
This study started on November 1, 2022, and ended on December 1, 2022. It was conducted among 50 nurse anesthesia students divided into intervention and control groups. The intervention group’s clinical skills were evaluated 4 times using the mini-CEX method. In contrast, the same skills were evaluated in the control group based on the conventional method—that is, general supervision by the instructor during the internship and a summative evaluation based on a checklist at the end of the course. The intervention group students also filled out a questionnaire to measure their satisfaction with the mini-CEX method.
Results
The mean score of the students in both the control and intervention groups increased significantly on the post-test (P<0.0001), but the improvement in the scores of the intervention group was significantly greater compared with the control group (P<0.0001). The overall mean score for satisfaction in the intervention group was 76.3 out of a maximum of 95.
Conclusion
The findings of this study showed that using mini-CEX as a formative evaluation method to evaluate clinical skills had a significant effect on the improvement of nurse anesthesia students’ clinical skills, and they had a very favorable opinion about this evaluation method.

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    Perioperative Care and Operating Room Management.2024; 34: 100368.     CrossRef
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    Juan Zhang, Hong Chen, Xie Wang, Xiaofeng Huang, Daojun Xie
    BMC Medical Education.2024;[Epub]     CrossRef
  • Impactos do Mini-Cex no ensino-aprendizagem da saúde: uma revisão integrativa
    João Henrique Anizio de Farias, Draenne Micarla dos Santos Silva, Clédson Calixto de Oliveira, Elzenir Pereira de Oliveira Almeida
    Revista de Gestão e Secretariado.2024; 15(9): e4150.     CrossRef
  • Comparing Satisfaction of Undergraduate Nursing Students`: Mini-CEX vs CIM in Assessing Clinical Competence
    Somia Saghir, Anny Ashiq Ali, Kashif Khan, Uzma Bibi, Shafaat Ullah, Rafi Ullah, Zaifullah Khan, Tahir Khan
    Pakistan Journal of Health Sciences.2023; : 134.     CrossRef
  • Enhancement of the technical and non-technical skills of nurse anesthesia students using the Anesthetic List Management Assessment Tool in Iran: a quasi-experimental study
    Ali Khalafi, Maedeh Kordnejad, Vahid Saidkhani
    Journal of Educational Evaluation for Health Professions.2023; 20: 19.     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
  • 8,003 View
  • 870 Download
  • 3 Web of Science
  • 4 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.

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    Azam Hosseinpour, Morteza Nasiri, Fatemeh Keshmiri, Tayebeh Arabzadeh, Hossein Sharafi
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    Katharine A. Robb, Marcy E. Rosenbaum, Lauren Peters, Susan Lenoch, Donna Lancianese, Jane L. Miller
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JEEHP : Journal of Educational Evaluation for Health Professions
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