Impact of the COVID-19 pandemic upon self-reported physician burnout in Ontario, Canada: evidence from a repeated cross-sectional survey
Bibliographic record
Abstract
OBJECTIVES: To estimate the impact of the SARS-CoV-2 (COVID-19) pandemic on levels of burnout among physicians in Ontario, Canada, and to understand physician perceptions of the contributors and solutions to burnout. DESIGN: Repeated cross-sectional survey. SETTING: Active and retired physicians, residents and medical students in Canada's largest province were invited to participate in an online survey via an email newsletter. PARTICIPANTS: In the first survey wave (March 2020), 1400 members responded (representing 76.3% of those who could be confirmed to have received the survey and 3.1% of total membership). In the second wave (March 2021), 2638 responded (75.9% of confirmed survey recipients and 5.8% of membership). KEY OUTCOME MEASURE: Level of burnout was assessed using a validated, single-item, self-defined burnout measure where options ranged from 1 (no symptoms of burnout) to 5 (completely burned out). RESULTS: The overall rate of high levels of burnout (self-reported levels 4-5) increased from 28.0% in 2020 (99% CI: 24.3% to 31.7%) to 34.7% in 2021 (99% CI: 31.8% to 37.7%), a 1-year increase of 6.8 percentage points (p<0.01). After a full year of practising during the COVID-19 pandemic, respondents ranked 'patient expectations/patient accountability', 'reporting and administrative obligations' and 'practice environment' as the three factors that contributed most to burnout. Respondents ranked 'streamline and reduce required documentation/administrative work', 'provide fair compensation' and 'improve work-life balance' as the three most important solutions. CONCLUSIONS: During the first 12 months of the COVID-19 pandemic in Ontario, prevalence of high levels of burnout had significantly increased. The contributors and solutions ranked highest by physicians were system-level or organisational in nature.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".