Bibliographic record
Abstract
OBJECTIVES: This article compares work injury occurrence by occupational category, and examines its relationship with selected factors reflecting work organization and environment. Associations between work injury and socio-demographic and other health-related variables are also considered. DATA SOURCES: Data are from the 2003 Canadian Community Health Survey (cycle 2.1). ANALYTICAL TECHNIQUES: Cross-sectional estimates of the proportion of workers injured on the job were calculated by occupational category, and by selected work-related, personal and socio-demographic characteristics. Multivariate analyses were used to study associations between work injury and job-related factors, while controlling for other influences. MAIN RESULTS: In 2003, an estimated 630,000 Canadian workers experienced at least one activity-limiting occupational injury. Of people in trades, transport and equipment operation, 9% sustained an on-the-job injury, compared with 2% of workers in the "white-collar" sector. Work injury was more common in male (5%) than in female workers (2%). In multivariate analysis, some work-related variables were associated with occupational injury for both sexes: employment in trades, transport and equipment operation, primary industries, and processing, manufacturing and utilities; shift work; and heavy labour. Income under $60,000 and working long hours were associated with injury in men, but not in women. Women reporting their jobs as stressful had higher odds of injury; in men, no association with work stress emerged.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.148 | 0.052 |
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".