Trends in severity of work‐related traumatic injury and musculoskeletal disorder, Ontario 2004–2017
Bibliographic record
Abstract
OBJECTIVES: Traumatic injury surveillance can be enhanced by describing injury severity trends. This study reports trends in work-related injury severity for males and females over the period 2004-2017 in Ontario, Canada. METHODS: A weighted measure of workers' compensation benefit expenditures was used to define injury severity, obtained from the linkage of workers' compensation claims to emergency department (ED) records where the main injury or illness was attributed to work. Denominator counts were obtained from Statistics Canada's Labor Force Survey. Trends in the annual incidence of injury, classified as low, moderate, or high severity, were examined using regression modeling, stratified by age and sex. RESULTS: Over a 14-year observation period, there were 1,636,866 ED records included in the analyses. Overall, 57.6% of occupational injury records were classified as low severity, 29.5% as moderate severity, and 12.8% as high severity conditions. There was an increase in the incidence of high severity injuries among females (annual percent change (APC): 1.52%; 95% CI: 0.77, 2.28), while the incidence of low and moderate severity injuries generally declined for males and females. Among females, injuries attributed to animate mechanical forces and assault increased as causes of low, moderate, and high severity injuries. The incidence of concussion increased for both males (APC: 10.51%; 95% CI: 8.18, 12.88) and females (APC: 16.37%; 95% CI: 13.37, 19.45). CONCLUSION: The incidence of severe work-related injuries increased among females in Ontario between 2004 and 2017. The methods applied in this surveillance study of traumatic injury severity are plausibly generalizable to applications in other jurisdictions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".