Oncologist burnout and compassion fatigue: investigating time pressure at work as a predictor and the mediating role of work-family conflict
Bibliographic record
Abstract
BACKGROUND: Oncologists are at high risk of poor mental health. Prior research has focused on burnout, and has identified heavy workload as a key predictor. Compassion fatigue among physicians has generally received less attention, although medical specialties such as oncology may be especially at risk of compassion fatigue. We contribute to research by identifying predictors of both burnout and compassion fatigue among oncologists. In doing so, we distinguish between quantitative workload (e.g., work hours) and subjective work pressure, and test whether work-family conflict mediates the relationships between work pressure and burnout or compassion fatigue. METHODS: In a cross-sectional study, oncologists from across Canada (n = 312) completed questionnaires assessing burnout, compassion fatigue, workload, time pressure at work, work-family conflict, and other personal, family, and occupational characteristics. Analyses use Ordinary Least Squares regression. RESULTS: Subjective time pressure at work is a key predictor of both burnout and compassion fatigue. Our results also show that work-family conflict fully mediates these relationships. Overall, the models explain more of the variation in burnout as compared to compassion fatigue. CONCLUSIONS: Our study highlights the need to consider oncologists' subjective time pressure, in addition to quantitative workload, in interventions to improve mental health. The findings also highlight a need to better understand additional predictors of compassion fatigue.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".