Culturally-adapted resilience-building curriculum for medical students: a comprehensive approach at Showa University School of Medicine, Japan
Bibliographic record
Abstract
The growing prevalence of psychological morbidity, depersonalization, and low personal accomplishment among medical students underscores the need for resilience-enhancing programs tailored to their specific needs. Incorporating cultural perspectives and societal context into these interventions is crucial to ensure their effectiveness and relevance. In response, Showa University School of Medicine in Japan has pioneered a culturally-adapted, resilience-based curriculum for medical students from their first through sixth years since 2020. This presentation will outline the comprehensive framework of the resilience-focused curriculum, including course objectives, content, learning resources, timetables, and pedagogical approaches. Key components of the curriculum encompass self-assessment and reflection, stress management strategies, effective communication and conflict resolution skills, and fostering a growth mindset. Additionally, interdisciplinary collaborations with psychology and social work departments provide individual supports and resources for students. Emphasizing the distinct challenges faced by medical students, such as academic stressors, relationships with senior clinical educators, patient and family interactions, and managing errors and burnout, the presentation will highlight the classes on the curriculum, support systems and mentorship programs in promoting mental well-being and resilience. The experiences and outcomes of the 2020-2022 cohorts will be shared, offering valuable insights into the effectiveness of the resilience-building curriculum in the Japanese context. Lessons learned from the implementation process, including challenges faced and strategies employed, will provide practical examples for other medical schools seeking to develop similar programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".