Teaching mindfulness in medical school: where are we now and where are we going?
Bibliographic record
Abstract
OBJECTIVES: Mindfulness has the potential to prevent compassion fatigue and burnout in that the doctor who is self-aware is more likely to engage in self-care activities and to manage stress better. Moreover, well doctors are better equipped to foster wellness in their patients. Teaching mindfulness in medical school is gaining momentum; we examined the literature and related websites to determine the extent to which this work is carried out with medical students and residents. METHODS: A literature search revealed that 14 medical schools teach mindfulness to medical and dental students and residents. RESULTS: A wide range of formats are used in teaching mindfulness. These include simple lectures, 1-day workshops and 8-10-week programmes in mindfulness-based stress reduction. Two medical schools stand out because they have integrated mindfulness into their curricula: the University of Rochester School of Medicine and Dentistry (USA) and Monash Medical School (Australia). Studies show that students who follow these programmes experience decreased psychological distress and an improved quality of life. CONCLUSIONS: Although the evidence points to the usefulness of teaching mindful practices, various issues remain to be considered. When is it best to teach mindfulness in the trajectory of a doctor's career? What format works best, when and for whom? How can what is learned be maintained over time? Should mindfulness training be integrated into the medical school core curriculum?
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".