Work injuries in internal migrants to Alberta, Canada. Do workers' compensation records provide an unbiased estimate of risk?
Bibliographic record
Abstract
INTRODUCTION: It is not known whether out-of-province Canadians, who travel to Alberta for work, are at increased risk of occupational injury. METHODS: Workers' compensation board (WCB) claims in 2013 to 2015 for those injured in Alberta were extracted by home province. Denominator data, from Statistics Canada, indicated the numbers from Alberta and Newfoundland and Labrador (NL) employed in Alberta in 2012. Both datasets were stratified by industry, age, and gender. Logistic regression estimated the risk of a worker from NL making a WCB claim in 2013 or 2014, stratified by time lost from work. Bias from under-reporting was examined in responses to injury questions in a cohort of trades' workers across Canada and in a pilot study in Fort McMurray, Alberta. RESULTS: Injury reporting rate in workers from NL was lower than those from Alberta, with a marked deficit (odds ratio [OR] = 0.17; 95% confidence interval [CI], 0.12-0.27) for injuries resulting in 1 to 30 days off work. Among the 1520 from Alberta in the trades' cohort, 327 participants reported 444 work injuries: 34.5% were reported to the WCB, rising to 69.4% in those treated by a physician. A total of 52 injuries in Alberta were recorded by 151 workers in the Fort McMurray cohort. In logistic regression, very similar factors predicted WCB reporting in the trades and Fort McMurray cohorts, but those from out-of-province or recently settled in Alberta were much less likely to report (OR = 0.02; 95% CI, 0.00-0.40). CONCLUSION: Differential rates of under-reporting explain in part the overall low estimates of injuries in interprovincial workers but not the deficit in time-loss 1 to 30 days.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".