Sleep Problems and Workplace Injuries in Canada
Bibliographic record
Abstract
STUDY OBJECTIVE: To investigate the association between sleep problems and risk of work injuries among Canadian workers and to identify working groups most at risk for injuries. DESIGN: Population-based cross-sectional survey. SETTING: Canada Participants: Working-age respondents (15-64 years of age) who worked part or full-time in the last 12 months (n = 69,584). INTERVENTIONS: None. METHODS: This study used data from the Canadian Community Health Survey (CCHS) Cycle 1.1 2000-2001. MEASUREMENTS AND RESULTS: The main indicator of sleep problems was reporting trouble going to sleep or staying asleep. Stratified logistic regression models were used to calculate odds ratios (OR) and 95% confidence intervals (CI) for the association of sleep problems and work injury after adjusting for potential confounders and for the survey design. Trouble sleeping most of the time was significantly associated with work injury in both men (OR = 1.25, 95% CI = 1.01-1.55) and women (OR = 1.54, 95% CI = 1.25-1.91). The multivariate stratified analysis found that men in trades and transportation jobs (OR = 1.50, 95% CI = 1.09-2.08), women in processing and manufacturing jobs (OR = 2.46, 95% CI = 1.11-5.47), and women who work rotating shifts (OR = 1.71, 95% CI = 1.11-2.64) were at the highest increased risk for work injury associated with trouble sleeping. CONCLUSIONS: Trouble sleeping was associated with an increased risk of work injury. The number of injuries attributable to sleep problems was higher for women compared to men. While most job classes and shift types showed an increased risk of injury, some groups such as women in processing and manufacturing and those who work rotating shifts warrant further investigation and attention for intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".