Self-reported driving under the influence of alcohol and cannabis among Ontario students: Associations with graduated licensing, risk taking, and substance abuse
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Bibliographic record
Abstract
OBJECTIVE: This article describes the patterns of self-reported driving under the influence of alcohol (DUIA) and driving under the influence of cannabis (DUIC) among licensed Ontario students in 2009 and examines their associations with graduated licensing, risk taking, and substance use problems for understanding DUIA and DUIC behaviors. Ontario's graduated licensing system requires new drivers to hold a G1 license for a minimum of 8 months and a G2 license for a minimum of 12 months before a full and unrestricted G license can be obtained. Among other restrictions, G1 drivers must maintain a 0 blood alcohol content (BAC), have an experienced driver in the passenger seat, not drive on any high-speed expressways, and not drive between the hours of midnight and 5 a.m. A G2 license is more similar to a G license, with fewer restrictions. METHOD: This study analyzed data from the 2009 Ontario Student Drug Use and Health Survey (OSDUHS). The OSDUHS is a biennial population-based survey of students (grades 7 to 12) in Ontario, Canada. RESULTS: The results showed that 16.3% of licensed students in Ontario reported DUIC and 11.5% reported DUIA during the past year. After controlling for the effect of age, type of license emerged as a robust predictor for both DUIA and DUIC behavior, because students with a G2 and full license were significantly more likely to report DUIA and DUIC than drivers with a G1 license. Multivariate analyses suggested that risk-seeking behaviors were more important for understanding DUIA behavior than for DUIC behavior. Elevated problem indicators for alcohol and for cannabis were associated with DUIA and DUIC, respectively. CONCLUSIONS: Though much attention has been paid to drinking and driving among adolescents, this research shows that more Ontario students now report driving after cannabis use than after drinking alcohol. The results identify important correlates of both behaviors that may be useful for prevention purposes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it