Physician Anxiety and Burnout: Symptom Correlates and a Prospective Pilot Study of App-Delivered Mindfulness Training
Bibliographic record
Abstract
BACKGROUND: Physician burnout is on the rise, yet little is known about its relationship to anxiety. Mindfulness-based stress reduction has demonstrated decreases in anxiety, yet physicians have reported reluctance to engage in it due to significant time commitments. OBJECTIVE: The aims of this study are to assess whether app-based mindfulness training can reduce anxiety in physicians and to explore if anxiety and burnout are correlated, thus leading to a reduction in both anxiety and burnout. METHODS: This was a nonrandomized pilot study comprised of 34 physicians who worked in a large US health care network and reported having anxiety. The intervention was an app-based mindfulness program. The main outcome measure was anxiety, measured by the Generalized Anxiety Disorder-7 (GAD-7). The secondary outcome measures assessed burnout: cynicism and emotional exhaustion items from the Maslach Burnout Inventory. RESULTS: GAD-7 scores decreased significantly at posttreatment (1 month after treatment initiation, 48% reduction, P<.001) and at the 3-month follow-up (57% reduction, P<.001). There was a significant correlation between anxiety and burnout (cynicism: r=.43; P=.01; emotional exhaustion: r=.71; P<.001). There was also a significant decrease in cynicism (50% reduction, P=.003 at posttreatment; 50% reduction, P=.009 at follow-up) and emotional exhaustion at both time points (20% reduction, P<.001 at posttreatment; 20% reduction, P=.003 at follow-up). CONCLUSIONS: This pilot study is the first to test an app-based mindfulness training program targeted at reducing anxiety with physicians and to demonstrate that in physicians, anxiety is correlated with burnout. These findings suggest that this may be an effective tool to reduce anxiety and burnout in physicians. TRIAL REGISTRATION: ClinicalTrials.gov NCT04137081; https://www.clinicaltrials.gov/ct2/show/NCT04137081.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".