Work and High-Risk Alcohol Consumption in the Canadian Workforce
Bibliographic record
Abstract
This study examined the associations between occupational groups; work-organization conditions based on task design; demands, social relations, and gratifications; and weekly high-risk alcohol consumption among Canadian workers. A secondary data analysis was performed on Cycle 2.1 of the Canadian Community Health Survey conducted by Statistics Canada in 2003. The sample consisted of 76,136 employees 15 years of age and older nested in 2,451 neighbourhoods. High-risk alcohol consumption is defined in accordance with Canadian guidelines for weekly low-risk alcohol consumption. The prevalence of weekly high-risk alcohol consumption is estimated to be 8.1% among workers. The results obtained using multilevel logistic regression analysis suggest that increased work hours and job insecurity are associated with elevated odds of high-risk alcohol consumption. Gender female, older age, being in couple and living with children associated with lower odds of high-risk drinking, while increased education, smoking, physical activities, and, and economic status were associated with higher odds. High-risk drinking varied between neighbourhoods, and gender moderates the contribution of physical demands. The results suggest that work made a limited contribution and non-work factors a greater contribution to weekly high-risk alcohol consumption. Limits and implications of these results are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".