Pandemic-related organizational factors predicting physician anxiety and depression: Cross-sectional results from the COPING survey
Bibliographic record
Abstract
Physician depression and anxiety increased during the COVID-19 pandemic. This study aimed to determine what pandemic-related factors might be responsible for this increase, using a cross-sectional survey of Canadian physicians. Risk factors measured included the Pandemic Experiences and Perceptions Scale (PEPS) subscales (impact, adequacy, risk perception and worklife), COVID-19 preparedness, level of contact with COVID-19, and the number of provincial COVID-19 cases. In total, 309 completed the primary outcomes, with 20.1% experiencing symptoms of depression and 43.2% of anxiety. Structural equation modeling analysis demonstrated significant relationships between risk perception and areas of worklife to anxiety and to depression. The effect of worklife quality was partially mediated through reductions in risk perception. Areas of worklife explained 6% of the variance of risk perception. The explanatory variables in the model described 33% of the variability in anxiety and 28% of the variability in depression. Areas of work-life and risk perception were both significant contributors to depression and anxiety among physicians during early stages of the pandemic. To reduce symptoms, the aim of healthcare organizations should be to ensure adequate resources, reduce risk perception, improve communication and education regarding what is known about pandemics or crises.Trial registration: ClinicalTrials.gov identifier: NCT04379063.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".