Health of health care workers in Canadian nursing homes and pediatric hospitals: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Poor health of health care workers affects quality of care, but research and health data for health care workers are scarce. Our aim was to compare physical/mental health among health care worker groups 1) within nursing homes and pediatric hospitals, 2) between the 2 settings and 3) with the physical/mental health of the Canadian population. METHODS: Using cross-sectional data collected as part of the Translating Research in Elder Care program and the Translating Research on Pain in Children program, we examined the health of health care workers. In nursing homes, 169 registered nurses, 139 licensed practical nurses, 1506 care aides, 145 allied health care providers and 69 managers were surveyed. In pediatric hospitals, 63 physicians, 747 registered nurses, 155 allied health care providers, 49 nurse educators and 22 managers were surveyed. After standardization of the data for age and sex, we applied analyses of variance and general linear models, adjusted for multiple testing. RESULTS: Nursing home workers and registered nurses in pediatric hospitals had poorer mental health than the Canadian population. Scores were lowest for registered nurses in nursing homes (mean difference -4.4 [95% confidence interval -6.6 to -2.6]). Physicians in pediatric hospitals and allied health care providers in nursing homes had better physical health than the general population. We also found important differences in physical/mental health for care provider groups within and between care settings. INTERPRETATION: Mental health is especially poor among nursing home workers, who care for a highly vulnerable and medically complex population of older adults. Strategies including optimized work environments are needed to improve the physical and mental health of health care workers to ameliorate quality of patient care.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".