Innovations in applied decision theory for health and safety
Bibliographic record
Abstract
OBJECTIVES: There are established methods for occupational epidemiological cohort analysis, such as proportional hazards regression, that are well suited to aetiological research and yield parameter estimates that allow for succinct communication among academics. However, these methods are not necessarily well suited for evaluation of health impacts of policy choices and communication to decision makers. An informed decision about a policy that impacts health and safety requires a valid estimate of the policy's potential impact. METHODS: We propose methods for data summarisation that may facilitate communication with managers, workers and their advocates. We calculate measures of effect in a framework for competing events, graphically display potential impacts on cause-specific mortality under policy alternatives and contrast these results to estimates obtained using standard Poisson regression methods. Methods are illustrated using a cohort mortality study of 28 546 Ontario uranium miners hired between 1950 and 1996 and followed through 2007. RESULTS: A standard regression analysis yields a positive association between cumulative radon progeny exposure and all-cause mortality (log(RR per 100 WLM)=0.09; SE=0.02). The proposed method yields an estimate of the expected gain in life expectancy (approximately 6 months per worker) and reduction of 261 lung cancer deaths under a policy that eliminated occupational radon progeny exposure. CONCLUSIONS: The proposed method shifts attention from covariate-adjusted risk ratios or rate ratios to estimates of deaths that are avoided or delayed under a potential policy. The approach may help inform decision-making and strengthen the connection of epidemiological approaches to data analysis with developments in decision theory and systems engineering to improve health and safety.
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".