Development of a Preliminary Model for Evaluating Occupational Health and Safety Risk Management Maturity in Small and Medium-Sized Enterprises
Bibliographic record
Abstract
Management of occupational health and safety (OHS) risks is a crucial component of any business. Numerous investigations have shown that work-related injuries and deaths occur disproportionately in small-to-medium-sized enterprises (SMEs) and that this is clearly due to deficient management of OHS risks. The main goal of this work is to develop a base of indicators suitable for evaluating OHS risk management maturity in industrial SMEs. A preliminary model is then proposed for this evaluation, based on a small number of relevant indicators selected from a careful bibliographic review. The work begins with a critical review of the literature and analysis of known concepts, methods, tools and models of measurement of risk analysis maturity in order to extract relevant indicators. The most suitable indicators are then grouped to form the basis of a preliminary model for evaluating OHS risk management maturity in the SME setting. Our findings will help managers of SMEs make sound decisions in their quest to improve the OHS performance of their businesses.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".