Burnout and Disengagement in Pathology: A Prepandemic Survey of Pathologists and Laboratory Professionals
Bibliographic record
Abstract
CONTEXT.—: Despite widely prevalent burnout and attendant disengagement in medicine, the specific patterns and drivers within pathology and laboratory medicine are uncommonly studied. OBJECTIVE.—: To assess the prevalence and drivers of burnout among pathology and laboratory medicine professionals, retrospectively, prior to the COVID-19 pandemic. DESIGN.—: This was a cross-sectional, mixed-methods study engaging pathology and laboratory medicine professionals as subjects. RESULTS.—: Of 2363 respondents, 438 identified as pathologists, 111 as pathology assistants, and 911 as pathology and laboratory professionals. The burnout rate was 58.4% (1380 of 2363) across all respondents in pathology and laboratory medicine. Burnout varied by job role (P < .01) and was highest among pathology and laboratory professionals. Disparities in burnout rate were observed by race. Fifty-six percent (1323 of 2363) of respondents felt that they had at least 1 symptom of burnout and were advancing toward a breaking point. Underlying factors ranked highly among all groups included control over workload and loss of meaning in work. CONCLUSIONS.—: Data provided from this cohort may help departments create successful strategies to reduce disengagement and burnout in the laboratory, especially during periods of increased stress as experienced during the COVID-19 pandemic. Further, these data may serve as a baseline comparison for future studies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".