Driving after drinking in Canada: findings from the Canadian Addiction Survey.
Bibliographic record
Abstract
BACKGROUND: Despite substantial decreases in the magnitude of the alcohol-crash problem over the past 25 years, many Canadians continue to drive under the influence of alcohol, causing thousands of serious injuries and deaths every year. METHODS: Data from the 2004 Canadian Addiction Survey (CAS) were used to determine the prevalence of self-reported driving after drinking and the characteristics of those who engage in the behaviour. RESULTS: Overall, 11.6% of licensed drivers in Canada reported operating a vehicle within an hour of consuming two or more drinks containing alcohol. Less than 5% of licensed drivers accounted for 86% of the more than 20 million (estimated) past-year drinking and driving occurrences. Drinking Drivers reported more extensive and problematic use of alcohol, and were more likely to report illegal drug use relative to Non-drinking Drivers. CONCLUSION: Driving after drinking remains a common behaviour among Canadian drivers. Those who persist in driving after drinking can be distinguished from other drivers on the basis of their greater use of alcohol and drugs. Those who drive after drinking frequently consume even greater quantities of alcohol on more frequent occasions and are more likely to experience problems as a result of their drinking. These findings suggest that countermeasure efforts need to be continued on all levels and expanded to specifically target high-risk heavy drinkers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".