Medical Teacher最新文献

Emerging evidence indicates that burnout manifests early, even among medical students. The pre-clerkship demographic remains inadequately understood with a notable scarcity of studies examining burnout. To address this gap, this project developed a conceptual model to better understand burnout among pre-clerkship = students. Fifteen second-year medical students participated in interviews exploring their experiences with educational stress and perceptions of burnout. Constructivist Grounded Theory informed data collection and analysis due to the undertheorized nature of burnout in the pre-clerkship setting. Recurring themes encompassed information overload, perceived limited faculty support, anticipatory stress of entering clerkship and social isolation during hybrid learning, highlighting multifaceted stressors that ranged from academic to relational. Participants underscored various personal factors, such as time management, adaptation strategies and focusing on personal strengths, as mitigating burnout. Participants recognized the systemic nature of burnout, suggesting that early clinical exposure, peer mentorship and social connections, and dedicated personal time could serve as protective measures. This study addresses a gap in the literature by presenting a model of how personal characteristics interact with institutional and environmental pressures among pre-clerkship students. The findings prompt reflection on why pre-clerkship students express burnout before entering the clinical setting, advocating for institutional strategies to support junior learners.

What is the educational challenge? Across many healthcare disciplines, clinicians report feeling unprepared to treat and communicate effectively with patients presenting with chronic pain. In-person role-playing activities are resource-intensive, relying on multiple faculty or classmates who cannot provide feedback at the same depth as faculty. What are the proposed solutions? ChatGPT's Advanced Voice Mode (AVM) offers a novel solution. Using specific prompts, AVM enables learners to engage in real-time voice conversations with simulated patients, helping to build confidence in evidence-based communication tools like motivational interviewing. When piloted in a physical therapy class, learner feedback was generally positive, but some shortcomings and suggestions for improvement were also noted. What are the potential benefits to a wider global audience? By offering personalized feedback, AVM allows learners to practice skills outside of class time. This approach offers a scalable way to deliver individualized communication feedback in health education setting with large cohorts, and limited faculty member or simulation lab resources. What are the next steps? Further research is needed to validate the fidelity and effectiveness of AVM feedback and its more widespread feasibility as an assessment tool. Addressing learner privacy concerns and refining prompts to ensure realistic patient interactions will be crucial for broader implementation.

Introduction: Medical students are exposed to sensitive topics throughout their university education. Topics can be sensitive based on an individual's life experience, and common examples include domestic violence and mental health. Teaching sensitive topics risks re-traumatisation, and educators typically receive little training in balancing harm minimisation and maximising preparedness to handle distressing patient encounters. A trauma-informed medical education (TIME) approach has been proposed, aiming to optimise the learning environment, improve resilience and prepare students for practice. However, perceptions of TIME and which approaches students find effective remain incompletely understood.

Methods: We conducted a descriptive, qualitative study design using semi-structured interviews with sixteen medical students from Monash University, Australia. We recruited students in their final years who had completed their general practice rotation, and interviews were recorded and transcribed verbatim. We undertook reflexive thematic analysis of transcripts using NVivo software.

Results: The overarching theme was the generation of a 'Safe Space' to learn sensitive topics. Contributing elements included sub-themes of (1) Preparedness for Teaching, (2) Teaching Techniques, and (3) Debriefing.

Discussion: Students reported that many TIME strategies were already in place in their teaching. The findings highlighted that trauma-informed teaching strategies were effective in promoting medical student psychological safety and well-being.

Background: Suicide gatekeeper training is a preventive approach that equips nonprofessionals with the knowledge and skills to recognize and minimize suicide risks. AI-based virtual human technology offers an innovative way to deliver the training via immersive and interactive learning experiences.

Objective: The study evaluated the acceptability and practicality of using Virti^® virtual human simulation technique to design a Question-Persuade-Referral communication training and explored university students' learning experiences and feedback of the design.

Design: Both qualitative and quantitative findings reflected Virti^® learning outcomes. User satisfaction and confidence of Virti and suicide gatekeeper knowledge were measured along with semistructured inquiries collecting the learning experiences. Virti^® was implemented in a 3-hour gatekeeper training session, including classroom teaching (1.5h) and Virti^® practice (1.5h) via mobile or tablet.

Results: Fifty-three undergraduates participated in the study (average age 19.2). The post-training score of the item, "I become better understood that gatekeeper care is essential." was rated the highest (Mean=8.23, SD=1.68), while "confidence in acting as a suicide gatekeeper" rated the lowest (Mean=5.82, SD=2.41). Three themes emerged through student feedback: Enhancing communication skills and confidence; perceived value and educational potential of Virti^®; and technical limitations and improvement of Virti^®.

Conclusion: The study validated that AI-based Virti^® scenarios were feasible for suicide gatekeeper training and communication skills promotion. Addressing technical issues may further strengthen user confidence.

Social accountability (SA) has become a key principle for health professions education, urging institutions to align their actions with the health priorities of the populations they serve. Despite its importance, many schools struggle to implement SA effectively, often due to fragmented leadership development and a lack of institutional frameworks. This AMEE Guide positions leadership as a strategic, system-wide driver of social accountability. Drawing on global case studies, established leadership models, and institutional evidence, it explains how leaders can translate SA from mission statements into measurable outcomes. The Guide introduces the SA values and links them to leadership practices. It offers practical recommendations across three levels: individual competencies, institutional mechanisms, and systemic enablers. The Guide highlights leadership models and demonstrates their application through detailed examples from institutions worldwide. By integrating leadership theory with practical strategies, the Guide provides a pathway for academic leaders to embed SA into their institutions' core practices through inclusive governance, responsive curricula, community partnerships, impact-driven research, and ongoing cultural change. It equips educators, deans, faculty developers, and policymakers with the tools needed to foster leaders capable of transforming medical and health professions education into a force for equity and social good.

Background: Assessing skills in simulated settings is resource-intensive and lacks validated metrics. Advances in AI offer the potential for automated competence assessment, addressing these limitations. This study aimed to develop and validate a machine learning AI model for automated evaluation during simulation-based thyroid ultrasound (US) training.

Methods: Videos from eight experts and 21 novices performing thyroid US on a simulator were analyzed. Frames were processed into sequences of 1, 10, and 50 seconds. A convolutional neural network with a pre-trained ResNet-50 base and a long short-term memory layer analyzed these sequences. The model was trained to distinguish competence levels (competent=1, not competent=0) using fourfold cross-validation, with performance metrics including precision, recall, F1 score, and accuracy. Bayesian updating and adaptive thresholding assessed performance over time.

Results: The AI model effectively differentiated expert and novice US performance. The 50-second sequences achieved the highest accuracy (70%) and F1 score (0.76). Experts showed significantly longer durations above the threshold (15.71s) compared to novices (9.31s, p= .030).

Conclusions: A long short-term memory-based AI model provides near real-time, automated assessments of competence in US training. Utilizing temporal video data enables detailed micro-assessments of complex procedures, which may enhance interpretability and be applied across various procedural domains.

Introduction: Near-peer teaching (NPT) in medical education supports active learning and peer engagement while fostering professional development. However, the academic benefits for NPT tutors, including effects on performance and graduation timelines, remain underexplored.

Methods: We evaluated the impact of NPT participation on academic outcomes, including grade point averages (GPAs), final examination performance, and curriculum adherence. A five-year dataset comprising 1544 medical students was analyzed. Academic performance metrics included GPAs from basic sciences and clinical rotations, diploma GPA, final board exam scores, and progression through semesters. Multiple regression and Generalized Additive Models (GAMs), adjusting for baseline performance and confounders, were used to assess associations.

Results: NPT tutors were found to have significantly higher diploma GPAs (+0.24, p < .001) and State exam scores (+0.15, p < .001) than non-tutors. They also had fewer passive semesters (-0.25, p < .001) and shorter total study duration (-0.79 semesters, p < .001). Non-linear interactions were observed between early academic performance and later outcomes.

Discussion: Participation as an NPT tutor was associated with higher academic performance and more efficient curricular progression. Academic benefits were most pronounced among students with lower preclinical GPAs, suggesting that NPT may represent a high-impact opportunity to support students who face early academic challenges.

What was the educational challenge?: Health professions educators grapple with profound emotional burdens-technology-related distress, gnawing self-doubt, and a chilling fear of obsolescence-when urged to integrate generative AI (GenAI) into their teaching. Prevailing faculty development often overlooks this critical affective dimension, focusing on technical skills while leaving anxieties unaddressed.

What was the solution?: A six-week course, 'AI in Health Professions Education,' was developed, grounded in empathetic course design and trauma-informed pedagogy, to create a supportive and safe learning environment.

How was the solution implemented?: Core principles of safety, transparency, collaboration, and empowerment were woven into the course. This involved validating initial emotional responses, fostering psychological safety for experimentation, instructor transparency about AI use and their own anxieties, and offering learners a choice to restore agency.

What lessons were learned that are relevant to a wider global audience?: Addressing affective readiness is paramount; technical competence in AI cannot flourish in the absence of acknowledged fear. Centering on human emotion and autonomy, using customizable and psychologically safe strategies, effectively dismantles globally shared anxieties, such as the fear of inadequacy, making AI adoption feasible and empowering individuals across diverse contexts.

What are the next steps?: Future efforts will include structured research into affective learning outcomes, such as the emotional trajectory of tech confidence, and the development of a toolkit for empathetically designed AI training globally, particularly in low-resource settings.