Cost‐benefit analysis of investment in occupational health and safety in Colombian companies
Bibliographic record
Abstract
OBJECTIVE: To determine whether investment in preventive measures by a Colombian insurer reduces rates of work-related injuries and results in positive returns from these investments. METHODS: The study is based on monthly panel data of 2011-2015 of 303 medium and large companies affiliated with a private insurer in Colombia. We undertook regression modeling analysis to assess the effectiveness of incremental investments in occupational health and safety (OHS) prevention measures. The cost-benefit analysis is from the insurer's perspective. RESULTS: Investment in OHS per full-time equivalent was statistically significant at the 1% level. We estimated that 4919 injuries were averted through these investments, resulting in the avoidance of $3 949 957 in costs. Our results suggest that the investments were worth undertaking from the insurer's perspective. CONCLUSIONS: This paper provides new empirical evidence on the effectiveness and cost-benefit of OHS investments in a middle-income country. Incremental investment in OHS can be effective and cost-beneficial.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".