Evaluating Occupational Safety and Health Management Systems: A Collaborative Approach
Bibliographic record
Abstract
A debate is ongoing in the literature on occupational safety and health management systems about whether, or at least to what extent, these systems actually produce real improvements in health and safety outcomes in workplaces and firms. Several limitations have been noted by critical observers who suggest that these systems may be more impressive on paper than they are in reality. One problem that has been identified as a possible limitation of the occupational safety and health management systems approach is the lack of fit between the systems and individual workplaces, often exacerbated by the use of generic ‘tick the boxes’ audit tools. To study whether this problem could be overcome, a team from the SafetyNet research programme at Memorial University set out to see whether an evaluation tool could be designed for a specific workplace through a collaborative process involving the company’s local managers, its workers, the union’s local and national representatives, and a visiting academic team. Between June 2005 and February 2008, Memorial University researchers worked with representatives from the United Steelworkers union national office, the union’s local executive at a large iron mining company in the province of Newfoundland and Labrador, and the company’s Joint Occupational Health and Safety Committee and its managers, to develop and test a tailored evaluation tool and to study its feasibility and impact. The objective was to determine whether a ‘made to measure’ tool for evaluating an occupational safety and health management system might make a distinct contribution to local health and safety outcomes. A related issue concerned what this kind of participatory approach to health and safety management could contribute to the efficacy of this and other companies’ approach to managing occupational safety and health, and what kind of roles this approach offered to members of the workforce. The results of the pilot project were, on the whole, positive but with a few interesting gaps and limitations.
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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 | 0.305 | 0.282 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.031 | 0.021 |
| Open science | 0.012 | 0.030 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".