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Record W3019618137 · doi:10.1136/oem-2019-epi.177

O7C.6 Effectiveness of an audit-based occupational health and safety management system certification on firm injury rates in alberta, canada

2019· article· en· W3019618137 on OpenAlexaffabout
Chris McLeod, Robert Macpherson, Adebayo A. Aderounmu, Willianm Quirke, Mieke Koehoorn

Bibliographic record

VenueOccupational and Environmental Medicine · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsWorkers Compensation Board of British ColumbiaInstitute for Work & HealthUniversity of British Columbia
Fundersnot available
KeywordsCertificationAuditConfidence intervalBusinessAccountingOperations managementStatisticsEngineeringMathematicsEconomicsManagement

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.460

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.025
GPT teacher head0.366
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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