Cancer care workers in Ontario: prevalence of burnout, job stress and job satisfaction.
Bibliographic record
Abstract
BACKGROUND: Cancer Care Ontario's Systemic Therapy Task Force recently reviewed the medical oncology system in the province. There has been growing concern about anecdotal reports of burnout, high levels of stress and staff leaving or decreasing their work hours. However, no research has systematically determined whether there is evidence to support or refute these reports. To this end, a confidential survey was undertaken. METHODS: A questionnaire was mailed to all 1016 personnel of the major providers of medical oncology services in Ontario. The questionnaire consisted of the Maslach Burnout Inventory, the 12-item General Health Questionnaire, a questionnaire to determine job satisfaction and stress, and questions to obtain demographic characteristics and to measure the staff's consideration of alternative work situations. RESULTS: The overall response rate was 70.9% (681 of 961 eligible subjects): by group it was 63.3% (131/207) for physicians, 80.9% (314/388) for allied health professionals and 64.5% (236/366) for support staff. The prevalence of emotional exhaustion were significantly higher among the physicians (53.3%) than among the allied health professionals (37.1%) and the support staff (30.5%) (p < or = 0.003); the same was true for feelings of depersonalization (22.1% v. 4.3% and 5.5% respectively) (p < or = 0.003). Feelings of low personal accomplishment were significantly higher among physicians (48.4%) and allied health professionals (54.0%) than among support staff (31.4%) (p < or = 0.002). About one-third of the respondents in each group reported that they have considered leaving for a job outside the cancer care system. Significantly more physicians (42.6%) than allied health professionals (7.6%) or support staff (4.5%) stated that they have considered leaving for a job outside the province. INTERPRETATION: The findings support the concern that medical oncology personnel are experiencing burnout and high levels of stress and that large numbers are considering leaving or decreasing their work hours. This is an important finding for the cancer care system, where highly trained and experienced health care workers are already in short supply.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".