Digital Mindfulness Training for Burnout Reduction in Physicians: Clinician-Driven Approach
Bibliographic record
Abstract
BACKGROUND: Physician burnout is widespread in health care systems, with harmful consequences on physicians, patients, and health care organizations. Mindfulness training (MT) has proven effective in reducing burnout; however, its time-consuming requirements often pose challenges for physicians who are already struggling with their busy schedules. OBJECTIVE: This study aimed to design a short and pragmatic digital MT program with input from clinicians specifically to address burnout and to test its efficacy in physicians. METHODS: Two separate nonrandomized pilot studies were conducted. In the first study, 27 physicians received the digital MT in a podcast format, while in the second study, 29 physicians and nurse practitioners accessed the same training through a free app-based platform. The main outcome measure was cynicism, one dimension of burnout. The secondary outcome measures were emotional exhaustion (the second dimension of burnout), anxiety, depression, intolerance of uncertainty, empathy (personal distress, perspective taking, and empathic concern subscales), self-compassion, and mindfulness (nonreactivity and nonjudgment subscales). In the second study, worry, sleep disturbances, and difficulties in emotion regulation were also measured. Changes in outcomes were assessed using self-report questionnaires administered before and after the treatment and 1 month later as follow-up. RESULTS: Both studies showed that MT decreased cynicism (posttreatment: 33% reduction; P≤.04; r≥0.41 and follow-up: 33% reduction; P≤.04; r≥0.45), while improvements in emotional exhaustion were observed solely in the first study (25% reduction, P=.02, r=.50 at posttreatment; 25% reduction, P=.008, r=.62 at follow-up). There were also significant reductions in anxiety (P≤.01, r≥0.49 at posttreatment; P≤.01, r≥0.54 at follow-up), intolerance of uncertainty (P≤.03, r≥.57 at posttreatment; P<.001, r≥0.66 at follow-up), and personal distress (P=.03, r=0.43 at posttreatment; P=.03, r=0.46 at follow-up), while increases in self-compassion (P≤.02, r≥0.50 at posttreatment; P≤.006, r≥0.59 at follow-up) and mindfulness (nonreactivity: P≤.001, r≥0.69 at posttreatment; P≤.004, r≥0.58 at follow-up; nonjudgment: P≤.009, r≥0.50 at posttreatment; P≤.03, r≥0.60 at follow-up). In addition, the second study reported significant decreases in worry (P=.04, r=0.40 at posttreatment; P=.006, r=0.58 at follow-up), sleep disturbances (P=.04, r=0.42 at posttreatment; P=.01, r=0.53 at follow-up), and difficulties in emotion regulation (P=.005, r=0.54 at posttreatment; P<.001, r=0.70 at follow-up). However, no changes were observed over time for depression or perspective taking and empathic concern. Finally, both studies revealed significant positive correlations between burnout and anxiety (cynicism: r≥0.38; P≤.04; emotional exhaustion: r≥0.58; P≤.001). CONCLUSIONS: To our knowledge, this research is the first where clinicians were involved in designing an intervention targeting burnout. These findings suggest that this digital MT serves as a viable and effective tool for alleviating burnout and anxiety among physicians. TRIAL REGISTRATION: ClinicalTrials.gov NCT06145425; https://clinicaltrials.gov/study/NCT06145425.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".