The Influence of the Age and Sex Distributions of Drivers on the Reduction of Impaired Crashes: Ontario, 1974-1999
Bibliographic record
Abstract
Legislative changes and public media campaigns to prevent impaired driving are often cited as explanations for the reduction in the rate of impaired crashes over the past 25 years in most of the industrialized world. Other factors may have contributed to these reductions, such as changes in the age and sex distribution of the driver population. The primary purpose of this article is to assess the extent to which the reduction in impaired crashes in Ontario, Canada, may be attributable to the changing age and sex distribution of drivers. In Ontario, the rate of impaired crashes declined by 78.1% from 1974 to 1999. During this time period, the average age of drivers increased from 39.4 years in 1974 to 43.2 in 1999. Similarly, from 1974 to 1999 the percentage of all drivers that were women increased from 39.6% to 46.8%. Since statistics show the likelihood of impaired crashes is lower for both older drivers and women, the reduction of impaired crashes is partially due to these demographic changes. Using indirect standardization, the aging population accounted for an 8.6% decline in the rate of impaired crashes. The changing sex distribution of drivers accounted for a 9.4% decline in impaired crashes. Other global factors may also help to explain the reduction of impaired crashes, such as general road safety improvements and reductions in per adult consumption of alcohol.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".