Interventions to Reduce the Consequences of Stress in Physicians
Bibliographic record
Abstract
A significant proportion of physicians and medical trainees experience stress-related anxiety and burnout resulting in increased absenteeism and disability, decreased patient satisfaction, and increased rates of medical errors. A review and meta-analysis was conducted to examine the effectiveness of interventions aimed at addressing stress, anxiety, and burnout in physicians and medical trainees. Twelve studies involving 1034 participants were included in three meta-analyses. Cognitive, behavioral, and mindfulness interventions were associated with decreased symptoms of anxiety in physicians (standard differences in means [SDM], -1.07; 95% confidence interval [CI], -1.39 to -0.74) and medical students (SDM, -0.55; 95% CI, -0.74 to -0.36). Interventions incorporating psychoeducation, interpersonal communication, and mindfulness meditation were associated with decreased burnout in physicians (SDM, -0.38; 95% CI, -0.49 to -0.26). Results from this review and meta-analysis provide support that cognitive, behavioral, and mindfulness-based approaches are effective in reducing stress in medical students and practicing physicians. There is emerging evidence that these models may also contribute to lower levels of burnout in physicians.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".