Medical Teacher最新文献

Artificial intelligence (AI) is reshaping healthcare, necessitating a transformation in health professions education. To prepare future professionals for an AI-integrated landscape, curricula must evolve beyond traditional biomedical training to incorporate interdisciplinary knowledge and AI-related competencies. However, current education often falls short in equipping students with the necessary skills. The 'Brain + X' course exemplifies the effectiveness of interdisciplinary learning in enhancing both theoretical understanding and practical AI applications. By integrating neuroscience fundamentals with AI techniques and hands-on training, the course fosters critical thinking and cross-disciplinary problem-solving skills. Participants reported significant improvements in data analysis, scientific conceptualization, and theoretical knowledge expansion. Survey data indicate that 96% of students found the course directly applicable to their research, while 91.9% demonstrated an enhanced capacity to address cross-disciplinary challenges. Pre- and post-course evaluations further revealed increased mastery of neuroscience methodologies and recognition of AI's indispensable role in healthcare. Additionally, the course strengthened students' ability to synthesize knowledge across disciplines, promoting long-term intellectual and professional growth. These findings underscore the necessity of interdisciplinary AI education in health professions. The 'Brain + X' model provides a foundation for integrating AI into healthcare training, fostering a new generation of professionals equipped to navigate and contribute to an increasingly AI-driven medical ecosystem.

In medical education, the number of knowledge syntheses has increased dramatically, reflecting their growth and influence on education practice, research, and policy. However, despite the availability of instruction on many of the steps of conducting knowledge syntheses, practical guidance for the critical step of data extraction is limited. Data extraction is the process of systematically identifying and collecting information from the studies included in a knowledge synthesis. Without clear guidance, data extraction can become flawed and overly time-consuming, ultimately jeopardizing the quality of the knowledge synthesis. This article addresses this gap by offering 12 practical tips for data extraction. The tips are grounded in the literature and informed by the authors' collective experience conducting and mentoring knowledge synthesis projects. Organized into two sections, creating a data extraction tool and operationalizing it, the tips provide actionable guidance on aligning extraction with research objectives, supporting a team-based approach, resolving discrepancies, and how to pilot a data extraction tool. Taken together, these tips aim to improve the rigor, efficiency, and reliability of knowledge synthesis in medical education.

In this short communication, we reconceptualize autonomy in medical education, particularly within the context of professional identity formation (PIF). Traditionally, autonomy has been framed as independence and achievement based on 'fixed norms'. However, identity is also shaped through 'relational responsiveness', which emphasizes mutual engagement and co-construction. Drawing on Asian cultural understandings of selfhood, we propose viewing autonomy as integrity enacted within a network of mutual responsibility. We argue that both fixed norms and relational responsiveness are essential, and we introduce a preliminary framework that highlights their complementary roles. While responsiveness fosters adaptability and connectedness, it can lead to identity diffusion if not grounded in internal standards. A blended model of both modes offers a more inclusive, culturally sensitive, and resilient approach to PIF in today's complex medical landscape.

This article describes a framework to assist individuals and teams who prepare for (or are engaged in) health professions education innovations. The four essential elements are Needs Assessment, Planning, Implementation and Programme Evaluation. Each element is described in detail with a series of questions to contemplate, and to ensure that all key steps are sufficiently covered. While it is helpful to use this process at the beginning of a project, it will be necessary to develop a continuing cycle of reflection. Needs change and a rapid quality improvement cycle will help maintain relevance and thus effectiveness. It will promote dissemination to inspire educators at the same or other institutions. The four-element framework was developed by the ASPIRE-to-Excellence Award Panel and Academy Section for Innovative and Inspirational Approaches to Health Professions Education (I&I). Examples from 11 awardees from four continents are provided. They illustrate innovative and inspirational work that can stimulate more progress in the field.

Introduction: Reducing examiner variability in Objective Structured Clinical Exams (OSCEs) is a priority within clinical performance assessment. In contrast to typical OSCE examiner training, video-based benchmarking (VBB) involves examiners scoring videos a/from their specific station b/shortly before the OSCE and then reflecting on and discussing scores/justifications agreed by an expert panel. Whilst realist evaluation has described mechanisms and contexts by which VBB may operate, VBB's overall efficacy is unknown.

Methods: We performed a multi-centre (12 UK medical schools) stratified randomised controlled trial of VBB versus control to determine the influence of VBB on examiners' score variability and other score characteristics. Secondarily, we compared the average scores allocated by examiners from different schools.

Results: 171 medically qualified, trained OSCE examiners participated in the study. VBB showed no significant effect on overall examiner variability. In pre-specified analyses, VBB reduced variability from group mean of initially 'outlying' examiners on the borderline performance (VBB mean variability 3.02 out of 27 (IQR1.98-4.98), control 4.70 (3.91-5.70), p < 0.016) and made examiners more likely to correctly fail a minimally failing performance (p < 0.03, OR = 2.133[95% CI 1.081-4.208]). VBB caused a small increase in confidence. There were no significant differences in average scores by school.

Conclusions: VBB may enhance trust in OSCEs through more accurate classification of borderline performances and aligning outlying examiners scoring.

Introduction: Competency-based medical education (CBME) is a global paradigm designed to align training with the needs of the healthcare system. However, its implementation relies on a shared understanding of key concepts, especially competence and its assessment, which may be interpreted differently across settings. This study explores how faculty members understand competence assessment and examines its implications for CBME implementation.

Methods: This qualitative, exploratory study was conducted at a public medical school in Brazil. Twenty self-selected faculty members participated in semi-structured, online interviews. Data were analyzed using a deductive framework method informed by four theoretical models from the CBME literature, which structured the analytic dimensions guiding coding and interpretation.

Results: Most participants articulated an analytic mental model of competence, describing it as composed of discrete components-knowledge, skills, and attitudes-assessed independently. No participant expressed a synthetic approach centered on real-world professional tasks. Assessment was predominantly positioned as certification of learning and described as enacted through multiple, non-integrated methods. Outcomes were primarily framed as serving internal educational stakeholders, with limited reference to patients or the healthcare system.

Discussion: The findings highlight how locally situated cultural understandings shape competence assessment within a formally structured CBME context. The gap between assessment practices and real-world readiness raises concerns regarding the legitimacy of certification, especially in settings lacking a structured educational continuum. Advancing CBME requires interpretive engagement and contextual responsiveness. Exploring how faculty members understand competence assessment offers a key entry point for examining culture and encouraging actions that support CBME in fulfilling its social mandate and reaffirming medical education as a public good.

What is the educational challenge? Discharge summary (DS) writing is a core competency for junior physicians, yet persistent deficiencies in the quality, accuracy, and timeliness of these clinical documents are well-documented, with downstream repercussions in patient safety and continuity of care. Existing educational interventions rely heavily on faculty-intensive, small-group teaching models, which limits scalability and long-term sustainability. There is therefore a need to develop novel, more resource-efficient approaches to provide high-quality training in DS writing with individualised feedback.

What are the proposed solutions? We propose a new educational model that integrates artificial intelligence (AI)-generated feedback into a structured DS training programme. As a proof-of-concept, we conducted a small-scale evaluation comparing feedback quality from multiple AI platforms and a human trainer using a standardised rubric. Based on these findings, we designed an asynchronous Coursemology-based e-learning module incorporating customised generative-AI (cGen-AI) to generate draft feedback, with human moderation retained as a safety and quality assurance step. This model is currently in the pre-implementation phase.

What are the potential benefits to a wider global audience? This conceptual human-in-the-loop AI model has the potential to deliver scalable, consistent, and individualised feedback while substantially reducing faculty and logistical workload. By enabling asynchronous practice and standardised assessment, it directly addresses sustainability challenges faced by DS training programmes internationally.

What are the next steps? Full implementation and evaluation including reliability, learner acceptance, and educational impact of this model is being planned for an entire medical student cohort to replace the existing small-group, faculty-facilitated sessions. The success of such cGen-AI approach for DS training can also be extended to other similar domains of medical training in the future.

Introduction: Affirmative action policies have been implemented to promote equitable access to higher education for socially vulnerable students, although their influence on student outcomes remains debated. This study compared the academic performance of students admitted through the regular path (RP) and affirmative action (AA) system, at a Brazilian public medical school.

Methods: This prospective cohort study included 236 fifth-year medical students from the School of Health Sciences (ESCS), between 2022 and 2024. Sociodemographic and economic data were obtained from institutional and national databases. Academic performance was assessed through cognitive, practical, and professionalism evaluations, as well as the final annual grade.

Results: Of 236 students, 134 (56.8%) entered via RP and 102 (43.2%) via AA. AA students were older, less often self-declared white, had lower parental education, and worse socioeconomic indicators (all p < 0.001). In univariate analyses, RP students scored higher in three of four evaluations (p < 0.001). After adjustment, admission type was not associated with performance. Cognitive scores correlated with younger age at admission (β: -0.039; 95% CI: -0.065 to -0.014; p < 0.001). Mini-CEX showed no significant associations. Professionalism scores were higher among female (β: 0.165; 95% CI: 0.013 to 0.316; p = 0.033), married (β: 0.589; 95% CI: 0.151 to 1.027; p = 0.009), those with a parent holding higher education (β: 0.242; 95% CI: 0.036 to 0.448; p = 0.021), and younger entrants (β: -0.033; 95% CI: -0.055 to -0.011; p = 0.003). Final grades were higher among female (β: 0.146; 95% CI: 0.032 to 0.261; p = 0.013), married (β: 0.376; 95% CI: 0.044 to 0.708; p = 0.027), and younger students (β: -0.032; 95% CI: -0.048 to -0.015; p < 0.001).

Conclusion: After adjusting for sociodemographic and economic factors, RP and AA students performed similarly, supporting AA as an effective inclusion policy.