Burnout in Canadian urology: Cohort analysis from the 2018 Canadian Urological Association census
Bibliographic record
Abstract
INTRODUCTION: Physician burnout is associated with medical error, patient dissatisfaction, and poorer physician health. Urologists have reported high levels of burnout and poor work-life integration compared with other physicians. Burnout rates among Canadian urologists has not been previously investigated. We aimed to establish the prevalence of Canadian urologist burnout and associated factors. METHODS: In the 2018 Canadian Urological Association census, the Maslach Burnout Inventory questions were assigned to all respondents. Responses from 105 practicing urologists were weighted by region and age group to represent 609 urologists in Canada. Burnout was defined as scoring high on the scales of emotional exhaustion or depersonalization. Demographic and practice variables were assessed to establish factors associated with burnout. Comparisons were made to the results of the 2016 American Urological Association census. RESULTS: Overall, 31.8% of respondents met the criteria for burnout. There was no effect of subspecialty practice or practice setting on burnout. On univariate analysis, rates of burnout were highest among urologists under financial strain (50.8%), female urologists (45.3%), and early-to-mid-career urologists (37.7-41.8%). Factors associated with demanding practices and poor work-life integration were predictive of burnout. A total of 12.2% of urologists reported seeking burnout resources and 54.0% wished there were better resources available. CONCLUSIONS: Urologist burnout in Canada is lower than reported in other countries, but contributing factors are similar. Urologists who report demanding clinical practices (particularly in early-to-mid career), poor work-life integration, financial strain, and female gender may benefit from directed intervention for prevention and management of burnout. Burnout resources for Canadian urologists require further development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".