Alcohol in fatal crashes involving Mexican and Canadian drivers in the USA
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of alcohol involvement and impairment in fatal crashes in the USA involving Mexican and Canadian drivers. METHODS: Drivers in fatal crashes in the USA were identified during 1998 to 2008 from the Fatality Analysis Reporting System, and the prevalence of alcohol involvement and impairment (defined as blood alcohol concentrations ≥0.01 g/dl and ≥0.08 g/dl, respectively) was compared among drivers licensed in Mexico (n=687), Canada (n=598), and the USA (n=561908). RESULTS: The prevalence of alcohol involvement was 27% for US drivers, 27% for Mexican drivers, and 11% for Canadian drivers. Alcohol impairment was found in 23% of US drivers, 23% of Mexican drivers, and 8% of Canadian drivers. With adjustment for driver demographic characteristics and survival status and for crash circumstances, the prevalence of alcohol involvement was significantly lower for Canadian drivers (adjusted prevalence ratio (PR) 0.63, 95% CI 0.49 to 0.80) than for US drivers, and was similar between Mexican and US drivers (adjusted PR 0.91, 95% CI 0.81 to 1.02). CONCLUSIONS: Alcohol involvement in fatal motor vehicle crashes in the USA is similarly prevalent in US and Mexican drivers, but is substantially less common in Canadian drivers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".