Resident physician burnout: insights from a Canadian multispecialty survey
Bibliographic record
Abstract
Abstract Background Burnout results from chronic exposure to stress: comprising emotional exhaustion (EE), depersonalisation (DP) and a reduced sense of personal achievement (PA). Only a few studies have examined burnout in Canadian residents, and no multispecialty studies using the Maslach Burnout Inventory-Health Sciences Survey (MBI-HSS) exist. The purpose of our study is to identify burnout prevalence, contributory factors and solutions. Methods A prospective 62-item survey, including the 22-item MBI-HSS, was sent to all Alberta residents, with a resident population of 1745. The association between burnout, EE, DP and PA with items in the survey was performed. Continuous data were evaluated using Student’s t-test or analysis of variance. Ordinal data were evaluated using Spearman’s correlation coefficient and Mann-Whitney U test. Nominal data were evaluated using χ2 test. Results Response rate was 41.1% (n=718), with burnout prevalence of 69.4%. 61.6% of residents demonstrated high EE, 47.8% high DP and 29.0% low PA. More hours worked, poor work–life balance, poor service-education balance, poor mental health support, experiencing intimidation/harassment and being unhappy with programme and with career choice were associated with higher burnout (p<0.001). 53.5% of residents experienced intimidation/harassment. Solutions to burnout included improved teaching, improved call/working hours, more wellness days and a change in medicine culture. Conclusion High prevalence of burnout in Canadian residents with contributory factors and solutions identified. We hope programmes across the world can use this information to improve the burden of burnout among residents.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".