O7C.6 Effectiveness of an audit-based occupational health and safety management system certification on firm injury rates in alberta, canada
Bibliographic record
Abstract
Objectives To determine if achieving an audit-based occupational health and safety management system certification (OHSMS) is associated with lower firm-level lost-time injury rates. Methods Firm-level workers’ compensation claims and OHSMS audit data were extracted from eligible industry sectors for the years 2000 to 2015. OHSMS and non-OHSMS firms were matched on observable baseline characteristics using coarsened exact matching methods. A difference-in-difference observational research design using population-averaged negative binomial regression models estimated the effect (incidence rate ratios (IRR) with 95% confidence intervals) of OHSMS certification on firm lost-time injury rates, adjusting for confounders. Models were stratified by industry sector and type of OHSMS (certification for small versus large firms). Results The cohort included 14,377 OHSMS firms matched to 11 338 non-OHSMS firms. Overall, OHSMS certification reduced lost time injuries (IRR: 0.86, 95% CI: 0.83–0.88), with a greater effect in 2011–15 (IRR: 0.79, 95% CI: 0.75–0.82). OHSMS certification was most effective in reducing injuries in the manufacturing (IRR: 0.75, 95% CI: 0.70–0.80), trade (IRR: 0.79, 95% CI: 0.73–0.86), and transportation (IRR: 0.80, 95% CI: 0.72–0.89) sectors. No effect was found in oil and gas (IRR: 1.05, 95% CI 0.91–1.20), business (IRR: 0.89, 95% CI: 0.89–1.10) and forestry (IRR: 1.05, 95% CI: 0.83–1.33) sectors. OHSMS certification for small firms was not associated with a reduction in injuries (IRR: 0.98, 95% CI: 0.91–1.06.) in contrast to OHSMS certification for large firms (IRR: 0.84, 95% CI: 0.82–0.87). Conclusions OHSMS certification is effective in reducing firm injury rates. Effectiveness varied by sector, time period, and small or large firm certification. Sectoral differences in OHSMS effectiveness suggest that the ability to prevent hazards targeted by certification may vary by work environment. Further, small firm OHSMS certification may not be identifying safer firms.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".