Cumulative prevention benefits and costs saved by drinking driving policies: estimates for Ontario, 1970-2006
Bibliographic record
Abstract
Progress in assessing the impact of specific policies on the drinking driving problem, and advances in valuing the avoidable costs that result from alcohol-related problems suggest that estimating the impact of policies can be determined with a precision that has not previously been possible. Using research-based estimates of the effects of drinking driving policies in Ontario and elsewhere, we estimate their impact in terms of deaths, injuries, and collisions prevented and costs saved. Estimating these benefits required three steps: 1) reviews of studies to estimate the impact of successful drinking driving policies in Ontario (legal limit (per se) law, raising drinking age from 18 to 19 years, remedial measures, RIDE spot-check program, graduated licensing, administrative licence suspensions, and maintaining the public monopoly on alcohol sales); 2) calculation of lprevention benefitsr to estimate the numbers of deaths, injuries, and collisions prevented; and 3) application of monetary values derived from two methods (human capital n discounted future earnings, willingness to pay) to estimate costs averted. We estimated the total deaths, injuries, and collisions prevented and costs averted for each policy from the year of its introduction to 2006. Between 1970 and 2006, drinking driving policies and programs prevented an estimated total of 4,887 deaths, 178,238 injuries and 132,182 property damage only collisions in Ontario, and total costs averted were estimated at CAN$8.5 Billion (human capital n discounted future earnings method) or CAN$78 Billion (willingness to pay method). These results suggest that drinking driving policies have been of substantial cumulative value to society in preventing deaths, injuries, collisions, and monetary costs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".