Five Practical Video Modules to Reduce Physician Burnout Through Mindfulness and Systematic Review of Evidence
Bibliographic record
Abstract
Burnout, characterized as depersonalization, emotional exhaustion, and a sense of reduced personal efficacy, is an ongoing problem faced by surgeons and medical trainees at all levels of training. Increasing evidence suggests that mindfulness interventions are effective in reducing burnout and stress among healthcare providers. With a goal to build our own programs, we sought to evaluate the evidence on mindfulness interventions among physicians and trainees. What we found went beyond a systematic review and has advanced our practices to a larger understanding of the interconnectivity between mindfulness, meditation, emotional intelligence, and leadership. In this article, we outline mindfulness and published interventions including their efficacy in reducing physician burnout and stress through a systematic review of randomized controlled trials (32 trials published between 2011 and 2023). In 4 distinct modules including 5 supplemental video modules, concepts that promote mindfulness are reviewed, providing a foundation for a mindfulness practice. These video modules include: (1) what mindfulness and meditation are, (2) a 5-minute meditation, (3) the Institute for Professional Excellence in Coaching energy leadership strategy, (4) building resilience through emotional intelligence, and (5) translating mindfulness into practice. This special topic aimed to open the conversation among plastic surgeons about burnout and the role that mindfulness can have to reduce its negative effects on our practices and our lives.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".