Canadian emergency physician psychological distress and burnout during the first 10 weeks of COVID‐19: A mixed‐methods study
Bibliographic record
Abstract
Objectives The aim of this study was to report burnout time trends and describe the psychological effects of working as a Canadian emergency physician during the first weeks of the coronavirus disease 2019 (COVID‐19) pandemic. Methods This was a mixed‐methods study. Emergency physicians completed a weekly online survey. The primary outcome was physician burnout as measured by the emotional exhaustion and depersonalization items, from the Maslach Burnout Inventory. We captured data on work patterns, aerosolizing procedures, testing and diagnosis of COVID‐19. Each week participants entered free text explaining their experiences and well‐being. Results There were 468 participants who worked in 143 Canadian hospitals. Burnout levels did not significantly change over time (emotional exhaustion P = 0.632, depersonalization P = 0.155). Three participants were diagnosed with COVID‐19. Being tested for COVID‐19 (odds ratio [OR] 11.5, 95% confidence interval [CI] 3.1–42.5) and the number of shifts worked (OR 1.3, 95% CI 1.1–1.5 per additional shift) were associated with high emotional exhaustion. Having been tested for COVID‐19 (OR 4.3, 95% CI 1.1–17.8) was also associated with high depersonalization. Personal safety, academic and educational work, personal protective equipment, the workforce, patient volumes, work patterns, and work environment had an impact on physician well‐being. A new financial reality and contrasting negative and positive experiences affected participants’ psychological health. Conclusion Emergency physician burnout levels remained stable during the initial 10 weeks of this pandemic. The impact of COVID‐19 on the work environment and personal perceptions and fears about the impact on lifestyle have affected physician well‐being.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".