Do differences in work disability duration by interjurisdictional claim status vary by industry and jurisdictional context?
Bibliographic record
Abstract
OBJECTIVES: To examine whether differences in work disability duration between out-of-province and within-province workers differed by industry and jurisdictional context. METHODS: Workers' compensation data were used to identify comparable lost time, work-related injury and musculoskeletal disorder claims accepted in six Canadian jurisdictions between 2006 and 2015. Out-of-province workers were identified as workers who filed claims in a different provincial jurisdiction to their province of residence. Coarsened exact matching was used to match out-of-province workers with within-province workers based on observable characteristics. Quantile regression models were used to estimate differences in cumulative disability days paid between out-of-province workers and within-province workers at different percentiles in the disability distribution, adjusting for confounders. RESULTS: Compared with within-province workers, out-of-province workers were paid more disability days even after matching and adjusting on observable characteristics. Differences between the two groups of workers were observed for short-duration, medium-duration and long-duration claims (differences of 1.57, 6.39, 21.42, 46.43 days at the 25th, 50th, 75th and 90th percentiles, respectively). Industry-specific models showed that differences were largest in construction, transportation and warehousing, and mining, quarrying and oil and gas extraction. Jurisdiction-specific models showed that differences were largest in the western provinces where out-of-province workers were concentrated in those sectors. CONCLUSIONS: Out-of-province workers are a vulnerable group with respect to risk of longer work disability duration. Workers' compensation systems, employers and healthcare providers may need to tailor specific interventions for these types of workers, particularly those employed in resource economy-dependent regions that are far from their regions of residence.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".