Economic evaluations of occupational health interventions from a corporate perspective – a systematic review of methodological quality
Bibliographic record
Abstract
OBJECTIVE: Using a standardized quality criteria list, we appraised the methodological quality of economic evaluations of occupational safety and health (OSH) interventions conducted from a corporate perspective. METHODS: The primary literature search was conducted in Medline and Embase. Supplemental searches were conducted in the Cochrane NHS Economic Evaluation Database, the National Institute for Occupational Safety and Health (NIOSH) database, the Ryerson International Labour, Occupational Safety and Health Index, scans of reference lists, and researchers' own literature database. Independently, two researchers selected articles based on title, keywords, and abstract, and if needed, fulltext. Disagreements were resolved by a consensus procedure. Articles were selected based on seven criteria addressing study population, type of intervention, comparative intervention, outcome, costs, language, and perspective. Two reviewers independently judged methodological quality using the Consensus on Health Economic Criteria (CHEC-list), a 19-item standardized quality criteria list. Disagreements in judgment were also resolved by consensus. Data were analyzed descriptively. RESULTS: A total of 34 studies were included. Of these, only 44% of the studies met more than 50% of the quality criteria. Of the 19 quality criteria, 8 were met by 50% or more of the studies. The 11 least fulfilled criteria related to (i) performance of a sensitivity analysis, (ii) selection of perspective, (iii) description of study population, (iv) discussion of generalizability, (v) description of competing alternatives, (vi) presentation of the research question, (vii) measurement of outcomes, (viii) measurement of costs, (ix) valuation of costs, (x) declaration of researchers' independence, and (xi) discussion of ethical and distributional issues. CONCLUSIONS: Apart from a few exceptions, the overall methodological quality of the economic evaluations of OSH interventions from a corporate perspective was poor. As such, there is a risk of biased results. The quality of future evaluations needs to be improved to increase the validity of their conclusions and recommendations.
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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 | 0.285 | 0.570 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.018 |
| Bibliometrics | 0.031 | 0.023 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".