Differences in workers' compensation claim rates for within‐province and out‐of‐province workers in British Columbia, Canada, 2010–2017
Bibliographic record
Abstract
BACKGROUND: Little is known about the work-related injury and illness risk of out-of-province workers. This study examines whether there are differences in work-related injury and illness claim rates between within-province and out-of-province workers in British Columbia (BC), Canada. METHODS: Workers' compensation claim data for injuries and illnesses in BC from 2010 to 2017 were linked with denominator data from Statistics Canada. Multivariable negative binomial regression estimated the claim rate ratio (RR) and 95% confidence intervals (CI) for out-of-province workers with all, health care-only (HCO), short-term disability, long-term disability, and fatality (SLF), and serious injury (SI) claims, compared to within-province workers. RESULTS: Compared to within-province workers, out-of-province workers had a lower total claim rate (RR: 0.54, 95% CI: 0.52-0.57), adjusting for sex, age, industry sector, and year. Differences in rates differed by claim type, with the largest differences for HCO claims (RR: 0.49, 95% CI: 0.47-0.52) and smallest differences for SI claims (RR: 0.85, 95% CI: 0.78-0.92). Sex-stratified models showed larger differences for males than females, with older female out-of-province workers having elevated SI claim rates. Industry-specific models showed that, even in sectors with high proportions of out-of-province workers' claims, these workers have lower claim rates than within-province workers. CONCLUSIONS: Out-of-province workers generally have lower claim rates than within-province workers. The overall duration of work exposure, and underreporting or underclaiming, are factors that may explain these lower claim rates. Understanding the determinants and differences of these claim rates may improve the administration and adjudication of claims while also identifying where further prevention measures may be merited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".