0424 The Injury Prevention Effects of Regulatory Workplace Safety Inspections in British Columbia, Canada from 2001 to 2011
Bibliographic record
Abstract
Objectives To determine if inspections by the workplace safety regulator (WorkSafeBC) in the Canadian Province of British Columbia are associated with a reduction in firm injury rates. Method Injury, inspection, and claims data collected by WorkSafeBC were analysed to determine the lost-time rates for all single-location firms that had been in operation in British Columbia for at least four years between the years of 2001 and 2011. Log-linear generalised estimating equations analyses were conducted to examine the effect of a workplace inspection on the change in injury rates between the year of inspection and the following year. Models were adjusted for time and sector. Results 74 510 firms met the eligibility criteria, with about 3% of firms per year experiencing an inspection, over the time period of 2001 to 2008. The ratio of firms inspected varied by sector, for example, about 8% of primary resources firms were inspected per year during this same period. Inspected firms had a higher injury rate (10 SLF claims per 100 FTE annually) compared to non-inspected firms (4 claims per 100 FTE). Through GEE analyses, it was found that an inspection reduced injuries (beta coeff= -0.0048 (-0.0067, -0.0029)) in the year following an inspection. The effect was greater for larger (>=10FTE) (beta coeff=-0.015 (-0.021, -0.009)) versus smaller firms (<10FTE) (beta coeff= -0.0067 (-0.0094, -0.0040)). Conclusions These results suggest that inspections do have injury prevention effects, and the differing effects by firm characteristics may indicate the opportunity to target firms according to the most appropriate intervention.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 0.001 |
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".