Survey on the current status of undergraduate education on self-care in university medical schools and medical colleges in Japan
Bibliographic record
Abstract
A survey on the current status of undergraduate education on self-care was conducted in university medical schools and medical colleges in Japan. This survey was planned and conducted by the Professionalism Subcommittee of the Japan Society for Medical Education. Prior approval was given by the Ethics Committee of Showa University. Self-care education was defined as education to enhance the well-being (physical and mental health) of medical students. Of the 82 universities invited to participate, 65 universities responded to the survey, giving a response rate of 79.3%. Of these 65 universities, 32 universities (49.2%) indicated that they were implementing self-care education programs. Stress management, mindfulness, self-awareness, resilience, and improvement of self-affirmation were the most common topics, and many of the faculty in charge of the topics were psychiatrists, psychologists, and medical education faculty members. Although about half of the universities implemented self-care education programs, the educational content has not yet been standardized, suggesting the need for standardization of self-care education in the future. The survey was conducted in all medical year levels, and the results showed that self-care education is given to first- and second-year medical students. It was suggested that it is necessary to provide more education to upper-year medical students who undergo clinical practice and experience increased stress. The need for self-care education for medical students has become even more important since 2020 and onwards, partly because of the impact of the COVID-19 pandemic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".