Mindfulness-based self-care education for healthcare professional students in Japan
Bibliographic record
Abstract
[Background] Recent studies have consistently shown that medical students experience a high rate of psychological symptoms. In this situation, teaching mindfulness in medical school has the potential to prevent student burnout. However, there are few consistent educational programs in medical schools throughout Japan.[Method] Since 2015, Showa University (Tokyo) has practiced an intensive self-care program based on mindfulness for 600 first-year healthcare professional students in the schools of medicine, dentistry, pharmacy, nursing, and rehabilitation. The target objectives of this program were as follows: understand the needs of self-care, enhance self-awareness, evaluate evidence of mindfulness for mental diseases, and practice formal/informal mindfulness-based activities. This program consisted of a 90-minute lecture, followed by consecutive reflective activities, including completing personal journals and portfolios. The students were required to plan how to make use of what they learned in this course. The students were asked to complete a questionnaire upon completion of the course.[Results] The questionnaire indicated that more than 90% of the students were satisfied with the program, and about 25% started regular mindfulness-based practices such as meditation and breathing methods aimed to reduce test anxiety. Descriptions from the e-portfolio showed that the participants understood evitable stressors and the importance of the body-mind relationship.[Conclusion] Mindfulness-based self-care education can encourage healthcare students to understand the necessity of self-care during the early stages of their professional training. This program for the first year students will be followed by a course on Professionalism for healthcare professional students during their subsequent years of university education.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".