Medical Education Online最新文献

Background: Medical students globally face elevated mental health risks, yet those in conflict-affected regions experience compounded stressors. Lebanon's overlapping crises offer a unique context for examining resilience under conditions of extreme stress. This study aims to 1) evaluate depression, anxiety, resilience, and stress levels among medical students, 2) identify anxiety‒depression clusters, and 3) assess predictors such as demographics or resilience associated with each cluster.

Methods: We conducted a cross-sectional survey of medical students at a Lebanese medical school between December 2024 and March 2025. Validated instruments assessed anxiety (GAD-7), depression (PHQ-9), perceived stress (PSS-10), and resilience (BRS). Clusters of anxiety and depression were identified, then bivariate and multivariable analyses were performed with the clusters being the dependent variable. An alpha of 0.05 was considered statistically significant.

Results: A total of 187 students participated in this survey. At least mild to moderate symptoms of anxiety and depression were found at 77% and 80% of the participants, with 33% reporting high perceived stress and 26% low resilience. Three distinct clusters were identified, the 'high-risk group', the 'medium-risk group', and the 'low-risk group'. Common factors significantly associated with the 'high-risk group' or 'medium-risk group' included smoking, experiencing social isolation and high perceived stress. Being engaged in regular extracurricular activities (adjusted OR (95% CI) = 0.04 (0.004; 0.41), p = 0.006) was associated with lower odds of belonging to the 'high-risk group'; while having a high resilience level (0.06 (0.006; 0.61); p = 0.02) was associated with lower odds, and exercising irregularly (3.96 (1.24; 12.7); p = 0.02) with higher odds of belonging to the 'medium-risk group'.

Conclusion: Medical students in this conflict-affected region experience alarming levels of anxiety, depression, and stress, yet resilience provides a degree of protection. Tailored, risk-based interventions combined with stress-management, resilience training, and healthy lifestyle habits are essential. Further longitudinal research is needed to assess long-term impacts.

Virtual patients have been increasingly utilized in medical education to develop empathy in a structured and scalable manner. Compared with traditional methods such as clinical practice and standardized patients, virtual patients can offer reproducible, resource-efficient learning experiences. This scoping review maps the research on virtual patient-based interventions designed to foster empathy in medical students. It seeks to identify existing research gaps, including conceptual definitions of empathy, scenario design, instructional strategies, assessment methods, and outcome measures. The Joanna Briggs Institute's scoping review methodology was followed, and the PRISMA-ScR guidelines for reporting were used. A comprehensive search was conducted in PubMed, CINAHL, ERIC, Web of Science, Scopus, and the Cochrane Library. Two independent reviewers screened all titles, abstracts, and full texts; a third reviewer resolved any discrepancies. Findings were presented narratively and in tabular form to highlight key insights and research gaps. Eighteen studies involving 1,920 medical students were included. The most common study design was single-arm pre-post pilot studies (n = 4, 21.1%), followed by randomized controlled trials (n = 2, 10.5%) and mixed-methods designs (n = 2, 10.5%). Five research gaps were identified: 1) lack of explicit definitions of empathy, 2) limited diversity in clinical scenarios, 3) absence of repeated virtual patient interventions, 4) limitations in assessment methods, and 5) insufficient evidence on the sustained outcomes of empathy. These findings offer important insights into the current state of medical education, where standardized curricula for empathy training remain underdeveloped. Future efforts should address these challenges by integrating virtual patients into instructional designs that effectively foster empathy in medical students. This review provides a foundation for developing and implementing educational programs that meet the needs of students seeking to enhance their empathic abilities and contribute to improved patient outcomes and prevent clinician burnout.

The study is aimed at exploring the potential of using ChatGPT-4 to develop multiple-choice questions that adhere to item development principles through the application of reverse engineering techniques in nursing education. To determine whether ChatGPT-4 could generate multiple-choice questions regarding medical-surgical nursing, the researchers first evaluated ChatGPT-4's level of nursing knowledge. The researchers then developed 20 prompts using reverse engineering techniques; these were subsequently entered into ChatGPT-4, resulting in the generation of 60 multiple-choice questions. All 60 questions were reviewed by five nursing education experts to evaluate compliance with item development principles. ChatGPT-4 demonstrated a high accuracy rate (98.00%) when answering multiple-choice questions about medical-surgical nursing education. AI-generated questions primarily adhered to the principles of item development, with an average compliance score of 7.90/9.00. The least adhered to among the nine-question development principles was 'The question stem should use only essential words or phrases.' Given that the AI-generated questions may serve as preliminary drafts for use in nursing education following expert review, ChatGPT-4 can function as a valuable tool in the design of educational materials. When utilizing ChatGPT-4, it is beneficial for nursing educators to apply reverse engineering techniques that enhance question quality through repeated comparison, as well as to analyze the prompts and corresponding AI-generated questions. Furthermore, including structured outlines and representative sample multiple-choice questions using Markdown syntax in the prompt can enhance the quality of AI-generated questions.

Recent shifts in medical education have moved curricula away from siloed, discipline-based courses toward competency-focused, integrated models. Leading within a longitudinal, integrated curriculum requires a distinct set of leadership competencies. To address this need, medical education leaders across five institutions collaborated to develop a generalist leadership framework. Using a modified Delphi process, the team conducted literature reviews reviewing both published and gray literature and held monthly meetings over six months. This process resulted in the identification of core competencies, organized into three domains: Humility and Life-long Learning, Supporting Diverse Collaborations, and Holistic Stewardship of the Curriculum. A competency based self-assessment tool, Generalist Leadership Assessment for Skills and Strengths (GLASS) Tool, was then developed around this framework. The GLASS tool was piloted and validated in workshops at three national medical education conferences. Feedback from these sessions demonstrated strong support for the tool, with an average of 94% agreement on the relevance and clarity of the competencies. Participants used the tool to self-assess their leadership competencies, with higher self-ratings observed in the domain of humility and life-long learning over holistic stewardship of the curriculum. The tool was refined based on expert input clarifying the milestones and consolidating the competencies. The GLASS tool is designed to support reflective leadership development across four levels of educational engagement: individual, departmental, institutional, and national. It offers a transparent approach for faculty development, leadership readiness, and strategic planning in evolving medical education environments.

Feedback and feedforward are highly relevant in promoting students' learning. With advances in artificial intelligence (AI), new opportunities to support feedback and feedforward are emerging. However, few studies have explored how medical students perceive and accept AI-based feedback and feedforward in medical communication training. In this study, we explored medical students' perceptions of AI-based and avatar-mediated feedback and feedforward in a simulation applying a doctor-patient consultation video. The participants comprised 82 medical students (56.1% female), 66 of whom were in their second semester and 16 in their fourth semester. Before participants saw a video of a medical student in a standardized pre-recorded consultation, they were asked to put themselves in the position of the peer shown. A human-like avatar subsequently provided AI-based feedback and feedforward for the medical student in the video. The participants-still taking on the shown medical student's role-were then asked to rate the perceived trustworthiness and their potential learning acceptance of the AI-based feedback and feedforward. The participants' ratings of trustworthiness and potential learning acceptance were higher for the AI-based, avatar-mediated feedforward than the feedback. Additionally, they reported a generally positive attitude toward AI. This attitude was positively correlated with a higher potential learning acceptance of feedback. The tendency to favor feedforward over feedback in interpersonal contexts-as described in the literature-was evident for the perception of the AI-based, avatar-mediated evaluations of a simulated doctor-patient consultation video. Future research could apply these insights to enhance AI-based learning in medical education, e.g. by providing students with AI-based feedforward on their own consultation videos and assessing their perceptions of the same.

Academic coaching is an increasingly vital strategy in higher education, fostering skill development, academic resilience, and personal growth. While research suggests that coaching enhances performance and key competencies such as time management, stress reduction, and self-advocacy, systematic evaluations are needed to assess its effectiveness across graduate medical and healthcare education. This systematic scoping review investigates the effects of academic coaching interventions on academic and professional outcomes among graduate-level health and medical students. We conducted a comprehensive literature search and screening process following the PRISMA methodology and registered in the International Database of Education Systematic Reviews (IDESR #IDESR000171). Eleven studies met the inclusion criteria and were analyzed using the Population, Intervention, Comparison, and Outcomes (PICO) framework to contextualize findings. The review revealed notable variation in target populations, program structures, focus areas, and data collection methods. Medical student studies addressed both early and advanced learners, with interventions ranging from self-regulated learning and executive/life coaching to academic remediation and longitudinal coaching relationships. Tailored academic coaching interventions show promise in supporting learner development across a range of outcomes. However, the effectiveness of these programs depends on their alignment with learners' specific needs, educational stages, and institutional contexts. Future coaching initiatives should be designed with attention to the Master Adaptive Learner framework to ensure responsiveness to the evolving demands of health professions education.

Global healthcare challenges highlight the need to cultivate social responsibility in medical education. However, traditional teaching methods inadequately develop this competency, overlooking peer-driven mechanisms. This study proposes a moderated mediation model to explore how peer interaction impacts medical students' social responsibility, examining extracurricular activities as a mediator and achievement motivation as a moderator. Using cluster sampling, data from five waves (2014-2023) of the Student Survey of Learning and Development (SSLD) at a Chinese research university were analyzed, involving 6,546 medical students. Validated scales measured peer interaction, extracurricular activities, achievement motivation, and social responsibility. SPSS PROCESS with 5,000 bootstrap samples tested mediation and moderation effects. Peer interaction significantly and positively predicted medical students' social responsibility (β = 0.187, p < 0.001), with extracurricular activities serving as a partial mediator (indirect effect proportion: 66.51%). Crucially, achievement motivation moderated both the direct and the first half of the mediation path: Under low achievement motivation (M-1SD), the direct (simple slope = 0. 078, t = 3.723, p < 0. 001) and indirect (simple slope = 0. 387, t = 20.635, p < 0. 001) effects of peer interaction on social responsibility were statistically significant. Conversely, under high achievement motivation (M + 1SD), the direct effect became insignificant (simple slope = 0. 009, t = 0.546, p > 0. 05), and the indirect effect weakened (simple slope = 0. 295, t = 20.077, p < 0. 001). Peer interaction promotes social responsibility through extracurricular engagement, but high achievement motivation diminishes this effect. Students with strong extrinsic goals may misuse peer interaction and activities, undermining social responsibility. Structured peer support programs with reflective components are needed, especially for achievement-oriented students, to foster genuine social responsibility in medical education.

Transnational medical educational programs are now commonplace. Given the importance of individualised feedback, this study explored how cultural factors influence feedback experiences across three transnational campuses of one medical school. A total of 57 final-year medical students were interviewed from a sampling frame of 514 (269 male, 245 female). One-to-one semi-structured interviews were conducted and analysed using template analysis. Codes were iteratively refined into four themes, which were critically reviewed through the lenses of Hofstede's cultural dimensions and Figured Worlds. Four themes were identified: (1) early socialisation into feedback shaped by family and schooling; (2) hierarchical learning environments positioning students as passive recipients; (3) strategies to cope with negative feedback; and (4) gendered differences in the interpretation of feedback. National identity played only a minor role, while prior experiences and hierarchical structures were more influential. Female students more often described humiliation and emotional burden, whereas some male students framed negative feedback as a 'rite of passage.' Feedback experiences in transnational medical programs appear less determined by national culture than by early life experiences and hierarchical clinical environments. Hofstede's framework offered limited explanatory value, while Figured Worlds illuminated how identities are negotiated in feedback encounters. To enhance feedback cultures in transnational settings, faculty development should prioritise dialogic approaches, sensitivity to learners' prior experiences, and awareness of gendered impacts. Institutional change is needed to move beyond transmissive practices and foster learner-centred, inclusive feedback.

Large language models (LLMs), such as ChatGPT, have demonstrated impressive performance on general medical examinations; however, their effectiveness significantly declines in specialized board examinations due to limited domain-specific training data and computational constraints inherent to their self-attention mechanisms. This study investigates a novel token-splitting strategy informed by Cognitive Load Theory (CLT), aimed at overcoming these limitations by optimizing cognitive processing and enhancing knowledge retention in specialized educational contexts. We implemented a token-splitting approach by segmenting Taiwan plastic surgery board examination materials and associated textbook content into cognitively manageable segments ranging from 4,000 to 20,000 tokens. These segmented inputs were provided to GPT-4.1 via its standard ChatGPT web interface. Model performance was rigorously evaluated, comparing accuracy and efficiency across various token lengths and question complexities classified according to Bloom's taxonomy.The GPT-4.1 model utilizing the token-splitting strategy significantly outperformed the baseline (unmodified) model, achieving notably higher accuracy. The optimal segmentation length was determined to be 6,000 tokens, effectively balancing cognitive coherence with information retention and model attention. Errors observed at this optimal length primarily resulted from content absent from textual materials or requiring multimodal interpretation (e.g., image-based reasoning). Provided relevant textual content was adequately segmented, GPT-4.1 consistently demonstrated high accuracy (From 75.88% to 92.93%). The findings highlight that a token-splitting approach, grounded in Cognitive Load Theory, significantly enhances LLM performance on specialized medical board examinations. This accessible, user-friendly strategy provides educators and clinicians with a practical means to improve AI-assisted education outcomes without requiring complex technical skills or infrastructure. Future research and development integrating multimodal capabilities and adaptive segmentation strategies promise to further optimize educational applications and clinical decision-making support.

This study aimed to evaluate the impact of a teaching model combining massive open online course (MOOC) with virtual simulation experiments on clinical skills training. A quasi-experimental design was employed in the Second Clinical College of Wuhan University. The control group received traditional face-to-face teaching, while the experimental group received a teaching model combining MOOC instruction with virtual simulation experiments. After completing the course, both groups participated in a theoretical test and a Structured Clinical Skills Assessment to assess their mastery of clinical skills. Additionally, a questionnaire survey was conducted to assess the effectiveness of the experimental teaching model and to gauge student satisfaction. The experimental group's theoretical test scores were significantly higher than those of the control group, and the experimental group's Structured Clinical Skills Assessment scores were significantly higher than those of the control group, with statistically significant differences (p < 0.05). The post-course questionnaire survey of the experimental group showed that most students were satisfied with the teaching model. In the general surgery experiment course, the teaching model combining MOOC instruction with virtual simulation experiments is an effective teaching method that improves surgical skill performance and increases student satisfaction.