Mindfulness-based stress reduction for medical students: a narrative review
Bibliographic record
Abstract
BACKGROUND: Medical students are at high risk of depression, distress and burnout, which may adversely affect patient safety. There has been growing interest in mindfulness in medical education to improve medical student well-being. Mindfulness-based stress reduction (MBSR) is a commonly used, standardized format for teaching mindfulness skills. Previous research has suggested that MBSR may be of particular benefit for medical students. This narrative review aims to further investigate the benefits of MBSR for undergraduate medical students. METHODS: A search of the literature was performed using MedLine, Embase, ERIC, PSYCInfo, and CINAHL to identify relevant studies. A total of 102 papers were identified with this search. After review and application of inclusion and exclusion criteria, nine papers were included in the study. RESULTS: MBSR training for medical students was associated with increased measures of psychological well-being and self-compassion, as well as improvements in stress, psychological distress and mood. Evidence for effect on empathy was mixed, and the single paper measuring burnout showed no effect. Two studies identified qualitative themes which provided context for the quantitative results. CONCLUSIONS: MBSR benefits medical student well-being and decreases medical student psychological distress and depression.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".