Economic evaluation of occupational health and safety programmes in health care
Bibliographic record
Abstract
BACKGROUND: Evidence-based resource allocation in the public health care sector requires reliable economic evaluations that are different from those needed in the commercial sector. AIMS: To describe a framework for conducting economic evaluations of occupational health and safety (OHS) programmes in health care developed with sector stakeholders. To define key resources and outcomes to be considered in economic evaluations of OHS programmes and to integrate these into a comprehensive framework. METHODS: Participatory action research supported by mixed qualitative and quantitative methods, including a multi-stakeholder working group, 25 key informant interviews, a 41-member Delphi panel and structured nominal group discussions. RESULTS: We found three resources had top priority: OHS staff time, training the workers and programme planning, promotion and evaluation. Similarly, five outcomes had top priority: number of injuries, safety climate, job satisfaction, quality of care and work days lost. The resulting framework was built around seven principles of good practice that stakeholders can use to assist them in conducting economic evaluations of OHS programmes. CONCLUSIONS: Use of a framework resulting from this participatory action research approach may increase the quality of economic evaluations of OHS programmes and facilitate programme comparisons for evidence-based resource allocation decisions. The principles may be applicable to other service sectors funded from general taxes and more broadly to economic evaluations of OHS programmes in general.
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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 | 0.302 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".