The impact of regulatory workplace safety inspections on workers' compensation claim rates
Bibliographic record
Abstract
BACKGROUND: Studies on the impact of workplace safety inspections on work injuries have found mixed effectiveness. Most studies are from the United States, examining Occupational Health and Safety Administration (OSHA) regulatory inspections in manufacturing firms with more than 10 employees. This study examines whether regulatory inspections in Alberta, Canada, result in reductions in workers' compensation claims rates for inspected firms relative to comparable non-inspected firms. METHODS: Firm and claim-level data from the Workers' Compensation Board of Alberta were linked with regulatory enforcement data from the Government of Alberta for construction, manufacturing, and transportation firms with at least one full-time employee for 37 consecutive months. A matched difference-in-differences study design was used to estimate changes in lost-time claim rates for work-related injuries and musculoskeletal diseases of inspected and comparable non-inspected firms between the year pre-inspection and 2 years, post-inspection, controlling for firm-level characteristics. RESULTS: Inspections were not effective in reducing firm-level claim rates, with the exception of transportation firms with more than one inspection experiencing a 28% decrease in their claim rate in the second year post-inspection, relative to the change in non-inspected firms. In construction, inspected firms experienced a 12% increase in their claim rate in the first year post-inspection. No effect was observed in the manufacturing sector. CONCLUSIONS: Regulatory workplace safety inspections in Alberta generally do not result in greater reductions in firm-level claim rates in the construction, manufacturing, and transportation sectors. Inspections alone may not be sufficient to induce compliance or hazard management changes that lead to reductions in firm-level injuries.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".