Bibliographic record
Abstract
CONTEXT.—: Burnout affects 44% of physicians, negatively impacts physicians and the patient care that they provide, and can be assessed by the Maslach Burnout Inventory. Forces contributing to physician burnout have been identified and grouped into 7 dimensions. Burnout within pathology has not been well studied. OBJECTIVE.—: To identify the prevalence of burnout within Canadian pathology, drivers of burnout important in pathology, and pathologist burnout mitigation strategies at an individual and departmental level. DESIGN.—: An electronic survey was disseminated by participating departmental chiefs and the Canadian Association of Pathologists. Survey content included the Maslach Burnout Inventory and 3 free-text questions, including: "What do you find most stressful about your work?" and "What is working for you, at an individual or departmental level, to mitigate against burnout?" Comparative statistics were performed by using Pearson χ2. Significant relationships were sought between pathologist burnout and potential drivers, using Mann-Whitney and Kruskal-Wallis tests. Responses to the qualitative questions were themed and mapped onto the 7 dimensions of burnout. RESULTS.—: Four hundred twenty-seven pathologists participated in the survey from all 10 Canadian provinces. The prevalence of burnout in Canadian pathology was 58% (246 respondents), and there were significant differences by gender and years in practice. Drivers of pathologist burnout included workload and chronic work-related pain. The most frequently reported effective departmental strategy to mitigate against burnout mapped to "organizational culture," and the approach that most individual pathologists have taken to mitigate against burnout involves work-life integration. CONCLUSIONS.—: Burnout within Canadian laboratory medicine is prevalent, and workload is a major driver.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".