A cross-sectional study of shift work, sleep quality and cardiometabolic risk in female hospital employees
Bibliographic record
Abstract
OBJECTIVES: Investigating the potential pathways linking shift work and cardiovascular diseases (CVD), this study aimed to identify whether sleep disturbances mediate the relationship between shift work and the metabolic syndrome, a cluster of CVD risk factors. DESIGN: Cross-sectional study. SETTING: A tertiary-level, acute care teaching hospital in Southeastern Ontario, Canada. PARTICIPANTS: Female hospital employees working a shift schedule of two 12 h days, two 12 h nights, followed by 5 days off (n=121) were compared with female day-only workers (n=150). PRIMARY AND SECONDARY OUTCOME MEASURES: Each of the seven components of the Pittsburgh Sleep Quality Index (PSQI) was measured. Of these, PSQI global score, sleep latency and sleep efficiency were examined as potential mediators in the relationship between shift work and the metabolic syndrome. RESULTS: Shift work status was associated with poor (>5) PSQI global score (OR=2.10, 95% CI 1.20 to 3.65), poor (≥2) sleep latency (OR=2.18, 95% CI 1.23 to 3.87) and poor (≥2) sleep efficiency (OR=2.11, 95% CI 1.16 to 3.84). Although shift work was associated with the metabolic syndrome (OR=2.29, 95% CI 1.12 to 4.70), the measured components of sleep quality did not mediate the relationship between shift work and the metabolic syndrome. CONCLUSIONS: Women working in a rapid forward rotating shift pattern have poorer sleep quality according to self-reported indicators of the validated PSQI and they have a higher prevalence of the metabolic syndrome compared with women who work during the day only. However, sleep quality did not mediate the relationship between shift work and the metabolic syndrome, suggesting that there are other psychophysiological pathways linking shift work to increased risk for CVD.
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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 | 0.000 | 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 | 0.000 |
| 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 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".