Geographic variation in work injuries: a multilevel analysis of individual-level data and area-level factors within Canada
Bibliographic record
Abstract
PURPOSE: This study sought to examine provincial variation in work injuries and to assess whether contextual factors are associated with geographic variation in work injuries. METHODS: Individual-level data from the 2003 and 2005 Canadian Community Health Survey was obtained for a representative sample of 89,541 Canadians aged 15 to 75 years old who reported working in the past 12 months. A multilevel regression model was conducted to identify geographic variation and contextual factors associated with the likelihood of reporting an activity limiting work injury [corrected], while adjusting for demographic and work variables. RESULTS: Provincial differences in work injuries were observed, even after controlling for other risk factors. Workers in western provinces such as Saskatchewan (adjusted odds ratio [AOR], 1.30; 95% confidence interval [CI], 1.09-1.55), Alberta (AOR, 1.31; 95% CI, 1.13-1.51), and British Columbia (AOR, 1.46; 95% CI, 1.26-1.71) had a higher risk of work injuries compared with Ontario workers. Indicators of area-level material and social deprivation were not associated with work injury risk. CONCLUSIONS: Provincial differences in work injuries suggest that broader factors acting as determinants of work injuries are operating across workplaces at a provincial level. Future research needs to identify the provincial determinants and whether similar large area-level factors are driving work injuries in other countries.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".