Trends in alcohol-impaired driving in Canada
Bibliographic record
Abstract
Drinking and driving continues to be a major road safety problem in Canada with 744 persons killed in crashes involving a drinking driver and 37 per cent of fatally injured drivers testing positive for alcohol in 2010, the most recent data year available. This paper describes recent trends in drinking and driving in Canada to better understand the current situation, and to determine whether the magnitude of the problem has been increasing or decreasing. Multiple indicators are used to examine trends in drinking driving behaviour and alcohol-related fatalities. Data sources include: A National Fatality Database, a comprehensive source of national data compiled annually by the Traffic Injury Research Foundation (TIRF) from coroner/medical examiner files and police reports on fatal crashes; and the Road Safety Monitor (RSM), an annual National Public Opinion Poll on Drinking and Driving conducted by TIRF. From 1995 to 2010 in Canada, there has been a continued and fairly consistent decrease in the number of fatalities involving a drinking driver in absolute terms as well as when these numbers are standardized into per capita and per licensed driver rates. The number and percent of fatally injured drivers testing positive for alcohol have also declined over this study period. Survey data from the RSM further show that the percentage of those who reported driving after they thought they were over the legal limit has also decreased consistently and significantly since 2008. Despite the apparent decreasing trend in drinking driving fatalities and behaviour since 1995, reductions have been relatively modest in recent years, and fatalities in crashes involving drivers who have consumed alcohol remain at high unacceptable levels.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".