Exploring effective learning sessions to enhance self-awareness and promote interest in self-care among medical professionals
Bibliographic record
Abstract
Background Self-awareness among medical professionals is becoming more important. However, it is difficult to practice self-awareness unless consciously. We held in-hospital learning sessions to enhance self-awareness and to support self-care of medical professionals. The session introduced mindfulness, meditation, and self-care from the perspectives of "psychological safety" and "end-of-life care." Objective To investigate the effects of sessions based on participants' reactions. Methods A self-administered questionnaire was distributed to 128 medical professionals who participated in the sessions, and the free description was analyzed according to the qualitative coding procedure. Results As a result of analyzing the contents of 97 entries described in the free description of the questionnaire. Six categories were generated in the end: Stressful experience, Active practice of meditation, The need for self-care, Knowledge of mindfulness, Healing through narrative, Self-awareness through the learning session. Discussion Stressful experience about "psychological safety" and "end-of-life care" were narrated. We found that they are receptive to mindfulness, meditation, and self-care, and want to actively incorporate it. The experience of self-awareness was enhanced through the learning sessions. Introducing meditation, mindfulness, and self-care as methods of coping with specific stresses in clinical settings was effective in enhancing self-awareness among medical professionals.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".