Cross-sectional survey on physician burnout during the COVID-19 pandemic in Vancouver, Canada: the role of gender, ethnicity and sexual orientation
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of physician burnout during the pandemic and differences by gender, ethnicity or sexual orientation. DESIGN, SETTING AND PARTICIPANTS: We conducted a cross-sectional survey (August-October in 2020) of internal medicine physicians at two academic hospitals in Vancouver, Canada. PRIMARY AND SECONDARY OUTCOMES: Physician burnout and its components, emotional exhaustion, depersonalisation and personal accomplishment were measured using the Maslach Burnout Inventory. RESULTS: The response rate was 38% (n=302/803 respondents, 49% women,). The prevalence of burnout was 68% (emotional exhaustion 63%, depersonalisation 39%) and feeling low personal accomplishment 22%. In addition, 21% reported that they were considering quitting the profession or had quit a position. Women were more likely to report emotional exhaustion (OR 2.00, 95% CI: 1.07 to 3.73, p=0.03) and feeling low personal accomplishment (OR 2.26, 95% CI: 1.09 to 4.70, p=0.03) than men. Visible ethnic minority physicians were more likely to report feeling lower personal accomplishment than white physicians (OR 1.81, 95% CI: 1.28 to 2.55, p=0.001). There was no difference in emotional exhaustion or depersonalisation by ethnicity or sexual orientation. Physicians who reported that COVID-19 affected their burnout were more likely to report any burnout (OR: 3.74, 95% CI: 1.99 to 7.01, p<0.001) and consideration of quitting or quit (OR: 3.20, 95% CI: 1.34 to 7.66, p=0.009). CONCLUSION: Burnout affects 2 out of 3 internal medicine physicians during the pandemic. Women, ethnic minority physicians and those who feel that COVID-19 affects burnout were more likely to report components of burnout. Further understanding of factors driving feelings of low personal accomplishment in women and ethnic minority physicians is needed.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".