Medical Teacher最新文献

Purpose: To explore ChatGPT's utility in evaluating medical students' implicit attitudes toward the doctor-patient relationship (DPR).

Materials and methods: This study analyzed interview transcripts from 10 medical students, categorizing implicit DPR attitudes into Care and Share dimensions, each with 5 levels. We first assessed ChatGPT's ability to identify DPR-related textual content, then compared grading results from experts, ChatGPT, and participants' self-evaluations. Finally, experts evaluated ChatGPT's performance acceptability.

Results: ChatGPT annotated fewer DPR-related segments than human experts. In grading, pre-course scores from experts and ChatGPT were comparable but lower than self-assessments. Post-course, expert scores were lower than ChatGPT's and further below self-assessments. ChatGPT achieved an accuracy of 0.84-0.85, precision of 0.81-0.85, recall of 0.84-0.85, and F1 score of 0.82-0.84 for attitude classification, with an average acceptability score of 3.9/5.

Conclusions: Large language models (LLMs) demonstrated high consistency with human experts in judging implicit attitudes. Future research should optimize LLMs and replicate this framework across diverse contexts with larger samples.

Purpose: Students face unique challenges to academic success. Downwards trends in licensure pass rates suggest institutions must better understand effective teaching and student supports. This scoping review assessed recent literature on the characteristics and effectiveness of strategies to improve student success in graduate health professions programs.

Methods: The search was conducted through Medline (OVID), CINAHL (EBSCO), Web of Science, and ERIC (Proquest) in April 2024. Interventional studies published after 2017 reporting on programmatic strategies instituted after admission were included to target contemporary strategies investigated in graduate health professions students.

Results: Analysis of included studies (n=24) revealed six programmatic approaches: bridging programs, mentorship, tutoring, remediation, curriculum design, and wellness support. All strategies reported varying levels of positive impact on student success. Across these interventions, three themes emerged: the importance of early identification of students at risk for academic difficulty, professional identity formation, and contextual learning.

Conclusions: While many programs have implemented interventional strategies, reporting their impact on academic outcomes is limited. Successful strategies targeting early identification, professional identity formation, and contextual learning demonstrate potential, but require careful implementation with adequate resources at all institutional levels. Future research should determine which strategies are most impactful longitudinally while measuring universal objective outcomes.

Generative Artificial Intelligence (GenAI) tools are increasingly integrated into research and academic writing, offering opportunities to streamline workflows and increase productivity. However, these tools also introduce risks when used uncritically, unethically, or without transparency. In particular, the undisclosed use of GenAI, now widely documented, may compromise research integrity. The aim of this AMEE Guide is to provide researchers with practical guidance on when and how to disclose the use of GenAI in scholarly writing. Specifically, we propose a clear framework to promote ethical GenAI use and reporting practices in health professions education research. We start with an exploration of key aspects of responsible use of GenAI in publishing (e.g. authorship, verification and responsibility, plagiarism and bias, data privacy and confidentiality, journal requirements). We then address the importance of transparency about GenAI use in research production, both within research teams (internal disclosure) and to journals and readers (external disclosure). With respect to the latter, we highlight the need to be aware of journal-specific guidance and offer guiding principles for effective disclosure. Central to these principles is the call for scholars to provide a candid description of how GenAI was used, allowing readers to understand how the model shaped the research and writing processes. We also briefly consider the use and disclosure of GenAI in peer review. Given that, at the time of writing this Guide (November 2025), many questions remain regarding AI use and disclosure for publishing, we conclude with reflections on future developments and directions for research.

Background: Current research on generative AI in medical education focuses on AI's performance or risks, such as unreliability. We argue these issues are not isolated flaws but are symptoms of systemic contradictions that emerge when a technology is introduced into a learning environment. To move beyond descriptive reports, a theoretical framework is necessary to analyze the systemic tensions that arise during generative AI integration.

Methods: A total of 141 first-year clerkship medical students used ChatGPT and provided qualitative data, including conversations with ChatGPT, evaluations of the generative AI's responses, and free-text feedback after watching concept videos of 'Acute Liver Failure'. We employed inductive thematic analysis to identify initial patterns, followed by a deductive analysis using Cultural-Historical Activity Theory to identify and interpret systemic contradictions.

Results: The analysis revealed four contradictions within the activity system: 1) a conflict between the Tool's (ChatGPT's) unreliability and the Object of achieving accurate knowledge; 2) a skills gap between the Subject's (students') initial questioning abilities and the Tool's operational demand; 3) an unstable Division of Labor (student-AI) that conflicted with professional Rules, creating a demand for the need for expert validation; and 4) ambiguous Rules that created confusion and conflicted with professional norms.

Conclusions: Challenges like AI unreliability and skill gaps are contradictions that function as catalysts for expansive learning. Resolving these tensions requires systemic transformation, including formalizing prompt engineering training and redefining the educator's role from an information provider to an essential expert validator within a new collaborative practice.

Purpose: This study aims to compare several free large language models (LLMs), identify which provides the most effective feedback, and investigate whether LLM-generated feedback can improve the accuracy and standardization of imaging reports produced by students.

Methods: A randomly selected class (test group, N= 30) was asked to write an imaging report based on each typical teaching case before and after receiving feedback generated by LLM. Another randomly selected class (control group, N= 30) was asked to write an imaging report of the same case without receiving the LLM-generated feedback. The quality of the feedback generated by the 4 main free LLMs was evaluated. The residency training examination marking scale was used to evaluate the quality of the reports. A questionnaire was used to investigate whether the students were satisfied with the feedback given by LLM.

Results: The feedback generated by ChatGPT 3.5, ERNIE Bot v3.5, and Tongyi v2.5 all demonstrated better structure and logic than that of Claude 3 OPUS (Mann-Whitney U Test, p < 0.05), but all exhibited some degree of hallucination. The scores of the reports in the test group were increased after receiving the feedback, and were higher than the control group (t-test, p < 0.05).

Conclusion: The feedback given by LLMs can help the students critically evaluate their reports and improve their reporting skills, but should be supervised by teachers.

Introduction: Increasing the diversity of medical students is a challenge and priority in many countries. In France, systems-level changes have been introduced to attract candidates from diverse backgrounds, specifically the traditional pathway to medical studies, the PASS (Parcours Accès Spécifique Santé/Specific Access to Health Training, biomedical sciences-focused) has been supplemented with a second pathway, the LAS (Licence Accès Santé/Bachelor's Degree with Access to Health Studies) combining a broader major with a health-access module. This study is the first to assess the effectiveness of the LAS in increasing the social, geographic, and sex diversity of candidates admitted to Medical or Dental Schools in France.

Methods: This prospective cohort included candidates to health studies. Socioeconomic origin was determined according to parents' profession. Primary outcome was admission to Medical or Dental School. Mediation analysis assessed the role of prior academic performance (assessed by the French Baccalaureate grade) between socioeconomic origin and admission.

Results: Among 2,059 candidates (women: 70%), 230/1,534 PASS (15% of admission, women: 55%, upper socioeconomic origin: 68%) and 43/525 LAS (8% of admission, women: 74%, upper socioeconomic origin: 49%) were admitted to Medical or Dental School. In multivariable logistic regression, sex (OR = 0.37 for women, 95%CI [0.26-0.53], p<.001), upper socioeconomic origin (OR = 1.78, 95%CI [1.20-2.64], p<.01), and prior academic performance predicted admission in PASS (OR = 5.57, 95%CI [2.90-10.7], p<.001). In LAS, only prior academic performance was independently associated with admission (OR = 8.93, 95%CI [3.99-20.0], p<.001). Prior academic performance partially mediated the effect of socioeconomic origin on admission in PASS, and fully mediated the effect in LAS.

Discussion: Introducing the LAS pathway measurably improved diversity among admitted students and reduced socioeconomic and sex-related disparities. In contrast, the historical PASS system continues to reinforce these inequities. By widening the academic lens used for selection, LAS shows that reforms can meaningfully counteract social reproduction while maintaining academic rigor.

What is the educational challenge?: Healthcare emergencies are common and heterogenous, but conceptually poorly defined in health professions education. This gap was highlighted while developing educational materials for medical students and newly qualified doctors learning to manage healthcare emergencies. We found no existing comprehensive framework to describe the nature of emergencies for educational and other purposes.

What are the proposed solutions?: We propose the Predictability-Urgency-Scale-Harm (PUSH) model, a multidimensional taxonomy that characterises healthcare emergencies by predictability (fully to unpredictable), urgency (pressing to immediate), scale (individual to population) and harm (none to severe). This adapts the WHO definition of emergencies to clinical practice and goes beyond existing one-dimensional acuity or triage scales.

What are the potential benefits to a wider global audience?: The PUSH model can be used by educators and clinicians to design and debrief simulation scenarios, map learners' real-life emergency exposure, and support shared mental models of emergencies in healthcare teams. It can enhance research design and comparability of studies. Other benefits include being low-cost, requiring no technology and applicability in both high- and low-resource settings.

What are the next steps?: Future work will refine the PUSH model through expert consensus and evaluate reliability, usability and educational impact when applied to clinical incidents and simulation-based education.

Introduction: Medical schools are often characterized by rigid hierarchical structures that may suppress student voices. Student governments provide an avenue for students to represent their peers, influence institutional policies, and foster personal and professional development. This study aims to reveal how participation in self-governance shapes students' attitudes, skills, and orientations toward change within their educational environments.

Methods: This qualitative study recruited current executive members and recent graduates who had served in the Japan Association for Medical Student Societies (Igakuren), the only nationally elected medical student body in Japan. Participants were identified through email invitations, personal networks, and official meetings. The first author conducted in-depth online interviews, which were audio-recorded, transcribed verbatim, and analyzed via a thematic analysis using a framework approach.

Results: A total of 21 medical students and doctors participated in the study. A thematic analysis identified four main themes: learning through everyday negotiations; advocacy and empowerment; cooperation and interaction; and contribution to professional development.

Discussion: Student self-governance cultivates medical students' self-efficacy, leadership, and professional development. Participants acquired key competencies by recognizing systemic issues, collaborating with diverse stakeholders, and leading initiatives for change-demonstrating the principles of Freire's problem-posing education. For health professions education to be genuinely student-centered, faculty and institutional leaders must support and engage in equitable, respectful dialogue with students.

Background: Following the COVID-19 pandemic, there has been renewed global attention on One Health (OH) as a framework to address the numerous global health challenges. Despite its growing recognition, the integration of OH into medical education has been limited. Many institutions are still unclear on the best approach to introduce and deliver OH within their academic programs.

Aim: To map the pedagogical strategies, implementation experiences, and challenges in integrating OH into medical curricula.

Methods: A scoping review was conducted in accordance with PRISMA-ScR guidelines. PubMed and Scopus databases were searched for peer-reviewed studies published between January 2015 and December 2024. Data were charted using a standardized extraction form and synthesized descriptively through thematic content analysis.

Results: A total of 14 articles were found from institutions across North America, Africa, and Europe, representing initiatives ranging from integrated modules and stand-alone courses to extracurricular activities. Many utilized interactive, interdisciplinary pedagogies such as problem-based learning, simulations, capstone projects, and community outreach programs. The expected competencies ranged from interdisciplinary collaboration to recognizing human-animal-environment interconnectedness to applying OH principles in identifying and managing health conditions. Content areas extended beyond zoonotic diseases and environmental health to include broader aspects of health systems and health policy development. All the initiatives emphasized on fostering collaborative competencies and broadening students' perspectives on health. However, implementation was challenged by institutional constraints such as curriculum overload, limited faculty expertise, and logistical barriers to interdisciplinary teaching. Many institutions encountered epistemological resistance and reluctance to move beyond reductionist, human-centric paradigms, which was a likely factor in students finding it difficult to relate OH concepts to their medical practice.

Conclusion: The review highlights the importance of faculty capacity building, early introduction of systems thinking, and alignment of clinical training with OH principles to ensure a more sustainable integration of OH in medical education.