Shift Work and Obesity among Canadian Women: A Cross-Sectional Study Using a Novel Exposure Assessment Tool
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: It has been suggested that the association between shift work and chronic disease is mediated by an increase in obesity. However, investigations of the relationship between shift work and obesity reveal mixed findings. Using a recently developed exposure assessment tool, this study examined the association between shift work and obesity among Canadian women from two studies: a cohort of university alumni, and a population-based study. METHODS: Self-administered questionnaire data were used from healthy, currently employed females in a population-based study, the Ontario Women's Diet and Health case-control study (n = 1611 controls), and from a subset of a of university alumni from the Canadian Study of Diet, Lifestyle, and Health (n = 1097) cohort study. Overweight was defined as BMI≥25 to <30, and obesity as BMI≥30. Reported occupation was converted to occupational codes and linked to a probability of shift work value derived from Survey of Labour and Income Dynamics data. Regular evenings, nights, or rotating work comprised shift work. Polytomous logistic regression estimated the association between probability of shift work, categorized as near nil, low, medium, and high probability of shift work, on overweight and obesity, controlling for detected confounders. RESULTS: In the population-based sample, high probability of shift work was associated with obesity (reference = near nil probability of shift work, OR: 1.88, 95% CI: 1.01-3.51, p = 0.047). In the alumni cohort, no significant association was detected between shift work and overweight or obesity. CONCLUSIONS: As these analyses found a positive association between high probability of shift work exposure and obesity in a population-based sample, but not in an alumni cohort, it is suggested that the relationship between shift work and obesity is complex, and may be particularly susceptible to occupational and education-related factors within a given population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".