Impacts of drinking‐age legislation on alcohol‐impaired driving crimes among young people in <scp>Canada</scp> , 2009–13
Bibliographic record
Abstract
BACKGROUND AND AIM: In Canada, the minimum legal drinking age (MLDA) is 18 years in Alberta, Manitoba and Québec and 19 in the rest of the country. Given that public health organizations have not only recommended increasing the MLDA to 19 years, but also have identified 21 years as ideal, the current study tested whether drivers slightly older than the MLDA had significant and abrupt increases in alcohol-impaired driving (AID) crimes, compared with their counterparts just younger than the MLDA. DESIGN: Regression-discontinuity approach. SETTING: Canada. SAMPLE: AID criminal incidents by drivers aged 15-23 years (female, n = 10 706; male, n = 44 973). MEASUREMENTS: Police-reported AID incidents from the Canadian 2009-13 Uniform Crime Reporting Survey. FINDINGS: Significant gender × MLDA effects supported gender-specific models. Compared with males slightly younger than the MLDA, those just older had abrupt increases in AID incidents of 42.8% [95% confidence interval (CI) = 20.4-66.3%, P < 0.001], 28.1% (95% CI = 16.0-40.7%, P < 0.001) and 35.1% (95% CI = 22.4-48.4%, P < 0.001) in provinces with an MLDA of 18 years, 19 years and across the country, respectively. Among females, AID incidents increased by 39.9% (95% CI = 1.9-79.6%, P = 0.040) in provinces with an MLDA of 18 years, and by 19.4% (95% CI = 2.1-37.4%, P = 0.028) at the national level. CONCLUSION: Release from drinking-age restrictions appears to be associated with increases in alcohol-impaired driving offenses among young drivers in Canada, ranging from 28 to 43% among males and from 19 to 40% among females.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".