Prevalence and Workplace Drivers of Burnout in Cancer Care Physicians in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: Provider well-being has become the fourth pillar of the quadruple aim for providing quality care. Exacerbated by the global COVID-19 pandemic, provider well-being has become a critical issue for health care systems worldwide. We describe the prevalence and key system-level drivers of burnout in oncologists in Ontario, Canada. METHODS: This is a cross-sectional survey study conducted in November-December 2019 of practicing cancer care physicians (surgical, medical, radiation, gynecologic oncology, and hematology) in Ontario, Canada. Ontario is Canada's largest province (with a population of 14.5 million), and has a single-payer publicly funded cancer system. The primary outcome was burnout experience assessed through the Maslach Burnout Inventory. RESULTS: A total of 418 physicians completed the questionnaire (response rate was 44% among confirmed oncologists). Seventy-three percent (n = 264 of 362) of oncologists had symptoms of burnout (high emotional exhaustion and/or depersonalization scores). Significant drivers of burnout identified in multivariable regression modeling included working in a hectic or chaotic atmosphere (odds ratio [OR] = 15.5; 95% CI, 3.4 to 71.5; P < .001), feeling unappreciated on the job (OR = 7.9; 95% CI, 2.9 to 21.3; P < .001), reporting poor or marginal control over workload (OR = 7.9; 95% CI, 2.9 to 21.3; P < .001), and not being comfortable talking to peers about workplace stress (OR = 3.0; 95% CI, 1.1 to 7.9; P < .001). Older age (≥ 56 years) was associated with lower odds of burnout (OR = 0.16; 95% CI, 0.1 to 0.4; P < .001). CONCLUSION: Nearly three quarters of participants met predefined standardized criteria for burnout. This number is striking, given the known impact of burnout on provider mental health, patient safety, and quality of care, and suggests Oncologists in Ontario may be a vulnerable group that warrants attention. Health care changes being driven by the COVID-19 pandemic provide an opportunity to rebuild new systems that address drivers of burnout. Creating richer peer-to-peer and leadership engagement opportunities among early- to mid-career individuals may be a worthwhile organizational strategy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".