Comparing the risk factors associated with serious versus and less serious work‐related injuries in ontario between 1991 and 2006
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to examine and compare the demographic and labor market risks for more serious and less serious work-related injuries and illnesses. METHODS: Secondary analysis of accepted workers' compensation claims in Ontario, combined with labor force estimates for the period 1991 to 2006. Serious injuries and illnesses were claims resulting in wage replacement. Less serious injuries and illnesses were claims only requiring health care. Regression models examined the relationship between demographic and labor market characteristics (age, gender, industry, job tenure, and unemployment) and claim type. RESULTS: Relative risk estimates for serious and less serious claims were not concordant across age, gender and industry employment groups. For example, while the mining and utilities and the construction industry had an increased probability of reporting NLTCs, they had a decreased probability of reporting LTCs. CONCLUSIONS: The risk for serious and less serious work-related injury and illness claims differ by demographic and labor market groups. The use of composite measures that combine wage-replacement and health care only claims should be considered when using compensation data for surveillance and primary prevention targeting strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".