Pandemic-related factors predicting physician burnout beyond established organizational factors: cross-sectional results from the COPING survey
Bibliographic record
Abstract
The COVID-19 pandemic has increased physician burnout beyond high baseline levels. We aimed to determine whether pandemic-related factors contribute to physician burnout beyond known organizational factors. This was a cross-sectional survey of Canadian physicians using a convenience sample. Eligible participants included any physician currently holding a license to practice in Canada. Responses were gathered from May 13 to 12 June 2020. Risk factors measured included the newly developed Pandemic Experiences and Perceptions Scale (PEPS) subscales, contact with virus, pandemic preparation, and provincial caseload. The primary outcome was the Maslach Burnout Inventory (MBI). The primary outcome was completed by 309 respondents. Latent profile analysis found 107 (34.6%) respondents were burned out. In multivariate analysis, exhaustion was independently associated with PEPS adequacy, risk perception, and worklife subscales (adjusted R2 = 0.236, P < 0.001). Cynicism was associated with exhaustion, and PEPS worklife (adjusted R2 = 0.543, P < 0.001). Efficacy was associated with cynicism, PEPS worklife, and active cases (adjusted R2 = 0.152, P < 0.001). Structural equation modelling showed statistically significant direct paths between PEPS areas of worklife and all MBI subscales. Contact with virus, preparation, and PEPS risk perception added to the prediction of MBI exhaustion. Among a sample of Canadian physicians during the COVID-19 pandemic, adequacy of resources, risk perception, and quality of worklife were associated with burnout indices. To mitigate physician burnout organizations should work to improve working conditions, ensure adequate resources, and foster perceived control of risk of transmission.Trial Registration: NCT04379063.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".