Burnout in Canadian Physiatrists
Bibliographic record
Abstract
OBJECTIVE: The prevalence of burnout in Canadian physiatrists is unknown. This study describes the prevalence of burnout in Canadian physiatrists and explores predictors. DESIGN: This national cross-sectional web-based survey used convenience sampling, targeting Canadian physiatrists (staff, fellows, residents). The Checklist for Reporting Results of Internet E-Surveys was used to ensure reporting quality. Statistical analyses included descriptives, correlations, and logistic regressions. Survey items included personal and professional characteristics, and validated measures for burnout, relational compassion, emotion regulation, and moral injury. RESULTS: One hundred eighteen responses were collected from a possible 501 physiatrists across Canada. Majority were female (53%), White North American (55%), and working in an urban setting (93%). Forty-two percent ( n = 50) had burnout, 43% moral injury, and 40% difficulties regulating emotions. Burnout was more likely in females ( P = 0.0064; odds ratio 5.24, 95% confidence interval 1.60-17.3), and White respondents ( P = 0.0213; odds ratio 0.24, 95% confidence interval 0.07-0.81). Higher relational compassion conferred a lower risk of burnout ( P = 0.0006; odds ratio 0.80, 95% confidence interval 0.71-0.91); greater difficulty regulating emotions predicted higher risk of burnout ( P = 0.0406; odds ratio 1.06, 95% confidence interval 1.00-1.11), and moral injury ( P < 0.0001; 95% odds ratio 1.16, 95% confidence interval 1.09-1.24). CONCLUSIONS: Burnout affects 4 in 10 Canadian physiatrists. Physiatrists who are female, White, and report greater difficulties regulating emotions are at greater risk.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".