Developing guidelines for good practice in the economic evaluation of occupational safety and health interventions
Bibliographic record
Abstract
OBJECTIVES: One of the objectives of a recently held workshop in Amsterdam, the Netherlands, was to advance methods for the economic evaluation of occupational safety and health (OSH) interventions at the corporate and societal level. Drawing from that workshop, we discuss issues to consider when developing guidelines for good practice (ie, a reference case). METHODS: The Economics of Occupational Safety and Health (ECOSH) workshop was held in conjunction with the Repository of Occupational Well-being Economic Research (ROWER) initiative in the fall of 2009 and brought together researchers, employers, unions, policymakers, and other stakeholders. Through presentations, break-out sessions, and group discussions, efforts were made to develop a consensus on key elements for good practice. This manuscript integrates these efforts along with earlier contributions in this area. RESULTS: We propose some framework principles and a set of recommendations to serve as the foundations for developing a reference case. We argue that a reference case can be invaluable for the OSH field because it encourages sound principles to be consistently applied in studies. Furthermore, it can ensure that studies are more readily comparable regardless of the intervention type, jurisdiction, or sector. CONCLUSIONS: Developing guidelines for good practice in the economic evaluation of OSH interventions that meet the needs of all stakeholders requires discussion as well as time. The ECOSH/ROWER initiative has served as a good starting point for this objective.
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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 | 0.690 | 0.789 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.025 | 0.017 |
| Research integrity | 0.043 | 0.041 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".