How can we design a curriculum for a resilient medical student? - A blueprint for resiliency programs for med students in Japan
Bibliographic record
Abstract
Recent research around the world has consistently reported that medical students experience a high rate of psychological morbidity, depersonalization, and low personal accomplishment. Resilience-enhancing programs have been proposed and implemented even in Japan. However, most of them remain extracurricular programs that are not specifically tailored to medical students. Additionally, they mostly mimic resiliency programs in North America, although studies have indicated that cultural perspective to the self, others, and context contribute to the capacity to respond to a stressful situation.In this context, the presenters investigated what factors might affect the similarities or differences in the perceptions of resilience among experienced palliative care physicians in Canada and Japan in 2017-2018 in order to propose a theory for a resiliency curriculum from a different cultural perspective. This study showed that Japanese physicians are more likely to rely on “Relationships” with other persons such as mentors, family, friends, or colleagues; in contrast, Canadian physicians tended to be more focused on individual factors such as “Autonomy” and “Confidence”.As a result, Showa University School of Medicine in Japan has developed a progressively advancing resiliency program for first through fourth year medical students as part of a new curriculum, implementation of which will begin in the spring of 2020. This represents one of the largest revisions in the school’s history. In this presentation, a blueprint for resiliency programs in a new curriculum will be presented, including course description, course content, educational objectives, learning resources, timetables, and instructional strategies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".