Evaluating the Effectiveness of an Occupational Health and Safety Management System Certification Program on Firm Work Injury Rates in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Occupational health and safety management systems (OHSMS) certification programs have the potential to improve workplace health and safety. In Canada, the Certificate of Recognition (COR) program is an example of such program and has been introduced in many industries and provinces. This study's objective was to identify whether the implementation of the COR program led to greater reduction in firm work-related injuries in Alberta, Canada. METHODS: Using firm- and claim-level data from the Workers' Compensation Board of Alberta and COR registration data from Government of Alberta, the effect of becoming COR-certified on firm-level injury rates was assessed using a matched difference-in-differences study design with population-averaged negative binomial regression models. RESULTS: A total of 14,377 certified firms were matched with 11,338 non-certified firms during the years 2000 to 2015. Firms that became certified had a greater reduction in the lost-time injury rate (IRR: 0.86, 95% CI 0.83-0.88) and disabling injury rate (IRR 0.97, 95% CI 0.94-1.00) relative to the change in injury rates among similar non-certified firms. The effectiveness of OHSMS certification was strongest in the transportation, manufacturing and trade sectors, in more recent years, and among firms certified using the standard COR program as opposed to the program adapted for small employers. CONCLUSIONS: The findings suggest that COR can be an effective program, but that the effectiveness of this program is dependent on the context in which it is implemented, such as the industry sector, time period, and type of audit program.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".