The self-care education for healthcare professional students using mindfulness
Bibliographic record
Abstract
Background Recent studies has consistently shown medical students experience high rate psychological symptoms. Under this circumstance teaching mindfulness is a possible option. However, there are few consistent educational courses in medical schools in Japan.Method Showa University (Tokyo, Japan) launched an intensive self-care program based on mindfulness for 600 first-year healthcare professional students in 2015 (120 medicine, 110 dentistry, 210 pharmacist, 100 nursing, 60 PT and OT). The target achievements of this program were as follows:Understand the needs of self-care, Enhance self-awareness, Evaluate evidence of mindfulness for mental diseases, Practice formal/informal mindfulness-based activities. This program consisted of a 90-minutes lecture, and consecutive reflective activities completing personal journals and portfolio follow the lectures. The students are required to plan how to make use of what they learned in this course. The students were asked to complete a questionnaire after the course.Results The questionnaire indicated that 98% of the students were satisfied with the course materials. In particular, some participants stated that regular mindfulness-based practices such as meditation, breathing method, and even informal mindfulness activities in daily life reduced test anxiety, mood- dependent behavior, and absence of flexibility. Descriptions from the e-portfolio showed that the participants understood the importance of body-mind relationship and evitable stressors around them.ConclusionTeaching mindfulness could encourage healthcare students to understand the necessity of self-care at early stages of their professional training. The results would help to develop our next stage of this self-care program based on mindfulness, 12 weekly 1.5-hours session.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".