Adverse effect of long work hours on incident diabetes in 7065 Ontario workers followed for 12 years
Bibliographic record
Abstract
OBJECTIVE: According to the International Diabetes Federation, the most important challenge for prevention is now to identify social and environmental modifiable risk factors of diabetes. In this regard, long work hours have recently been linked with diabetes, but more high-quality prospective studies are needed. We evaluated the relationship between long work hours and the incidence of diabetes among 7065 workers over a 12-year period in Ontario, Canada. RESEARCH DESIGN AND METHODS: Data from Ontario respondents (35-74 years of age) to the 2003 Canadian Community Health Survey were prospectively linked to the Ontario Health Insurance Plan database for physician services and the Canadian Institute for Health Information Discharge Abstract Database for hospital admissions. Our sample consisted of actively employed participants with no previous diagnoses of diabetes. Cox proportional hazard regression models were then performed to evaluate the relationship between long work hours (≥45 hours per week) and the incidence of diabetes. RESULTS: Long work hours did not increase the risk of developing diabetes among men. However, among women, those usually working 45 hours or more per week had a significantly higher risk of diabetes than women working between 35 and 40 hours per week (HR: 1.63 (95% CI 1.04 to 2.57)). The effect was slightly attenuated when adjusted for the potentially mediating factors which are smoking, leisure time physical activity, alcohol consumption and body mass index. CONCLUSION: Working 45 hours or more per week was associated with an increased incidence of diabetes among women, but not men. Identifying modifiable risk factors such as long work hours is of major importance to improve prevention strategies and orient policy making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".