How to reduce empathic distress and increase emotional skills in medical training? Experience of a Mindfulness-Based stress reduction class vs. control group in France
Bibliographic record
Abstract
Abstract Background: Improving student wellness through curricular activities is a topic of interest in medical school. Both distress and well-being are related to medical student empathy. Numerous data suggest that learning mindfulness skills help to reduce anxiety, stress and overall psychological distress. Moreover, there are still poor data on the impact of MBSR on medical students' empathy and emotional skills. Methods: We designed a controlled study including an intervention group (MBSR) and a wait-list control group. We aimed to explore the impact of an MBSR training in medical students on (1) empathy (2) emotional skills (identification, understanding, acceptance) and (3) self-care. Online assessments occurred at baseline and post intervention. We analyzed pre-post changes and explored intervention effects using a generalized mixed model. Results: Fifty-one medical students were included. 80% of students attended all MBSR classes. When compared with the control group, Personal Distress (PD) of the empathy subscale decreased significantly in the MBSR group (β=-3.55 [95%CI -5.09, -1.40], p<0.005). No other significant change was observed as for the empathy subscales. Students in the MBSR group increased their emotional skills as the ability to identify (p<.005, Cohen’s d=0,52) and understand (p=.02, Cohen’s d=0.62) one’s own emotion. The total Self-Compassion score (SCS) increased significantly in the MBSR group (β=-25.5 [95%CI 18.16, 32.86], p<0.001) assesses the ability to self-care. Conclusion: Results suggest that MBSR develops medical students' interpersonal resources and reduces empathy distress. Indeed, PD assesses the tendency to feel distress and discomfort in response to the distress of others and corresponds to a challenge in medical training. MBSR could be beneficially combined with other educational modalities to enhance each component of empathy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".