O2C.2 Does region of residence matter for return-to-work after work-related injury? A comparative analysis of six canadian workers’ compensation jurisdictions
Bibliographic record
Abstract
Objectives To investigate regional differences in return-to-work following work-related injury and whether these differences persist after adjusting for individual characteristics. Methods Workers’ compensation claims from six Canadian provinces were used to create comparable cohorts of workers aged 15–80 with a work-related injury resulting in at least one disability day from 2011 to 2015. Workers’ residential postal codes were mapped to Census standard geographic units to categorize workers into six regions representing decreasing urban density and metropolitan influence (ranging from large urban areas of 100,000+people to rural areas of <10 000 people with no metropolitan influence). Cox regression models were used to estimate the effect of urban-rural residence on the likelihood of injured workers transitioning off work disability benefits within one-year post-injury, adjusting for confounders, including provincial compensation jurisdiction. Models were stratified by industry sectors. Results The cohort included 7 46 029 work disability claims, of which the majority resided in large urban areas (69%). Unadjusted models showed that workers residing in smaller urban and rural areas had a lower likelihood of transitioning off work disability benefits compared to those in large urban areas. Urban-rural differences persisted in adjusted models (e.g. HR=0.91 95% CI 0.89, 0.94 for workers in rural areas with no metropolitan influence). Industry-stratified models showed that greater differences existed between urban and rural places of residence for workers in the transportation and construction sectors, and smaller differences for workers in the health care and manufacturing sectors. Conclusions The main finding suggests that injured workers in more rural areas face barriers in returning to work and that workers’ compensation resources may need to be allocated to address these regional disparities. Future research will incorporate both individual and regional-level variables in a multilevel model framework to identify the characteristics that are the most important in explaining variability in work disability duration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".