The Influence of Work Patterns on Indicators of Cardiometabolic Risk in Female Hospital Employees
Bibliographic record
Abstract
OBJECTIVE: This study explored the associations between work patterns and indicators of cardiometabolic risk in female hospital employees. BACKGROUND: Aspects of work environments potentially influence the health of employees; however, we have a poor understanding of how different hospital work patterns contribute to cardiovascular risk in female employees. METHODS: We conducted a cross-sectional study of 466 female employees from 2 hospitals in Ontario. Data were collected through self-report, physical examination, and use of hospital administrative work data. RESULTS: In the adjusted analyses, full-time work status, extended shift length, and working 35 or more paid overtime hours per year were significantly associated with metabolic syndrome. CONCLUSIONS: Different work patterns increase cardiometabolic risk in female employees, suggesting a need to better monitor the health of the workforce and implement healthy workplace policy.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".