The association between single and dual use of cannabis and alcohol and driving under the influence and riding with an impaired driver in a large sample of Canadian adolescents
Bibliographic record
Abstract
OBJECTIVE: Dual use of cannabis and alcohol has increased in adolescents, but limited research has examined how it relates to impaired driving or riding with an impaired driver (IDR) compared to single substance use. This study aimed to examine the odds of alcohol- and/or cannabis-IDR among adolescents based on their use of alcohol and/or cannabis, and whether associations differed by gender and age. METHODS: Cross-sectional survey data were used from a sample of 69,621 students attending 182 Canadian secondary schools in the 2021/22 school year. Multilevel logistic regression estimated the odds of exclusive alcohol-IDR, exclusive cannabis-IDR, and both alcohol and cannabis IDR (alcohol-cannabis-IDR). Substance use interactions with gender and age were tested. RESULTS: Overall, 14.7% of participants reported IDR; 7.5% reported exclusive alcohol-IDR, 3.2% reported exclusive cannabis-IDR, 4.0% reported alcohol-cannabis-IDR, and 7.4% were unsure if they had experienced IDR. The prevalence of IDR varied across substance use groups, 8.0% among nonuse, 21.9% among alcohol-only use, 35.9% among cannabis-only use, and 49.6% among dual use groups. Gender diverse, older, and students with lower socioeconomic status exhibited a higher likelihood of reporting alcohol-cannabis-IDR. Dual use was significantly associated with 9.5 times higher odds of alcohol-cannabis-IDR compared to alcohol-only use, and 3.0 times higher odds compared to cannabis-only use. Dual use was also associated with an increased likelihood of either alcohol- or cannabis-IDR. CONCLUSIONS: This study highlights that all students, regardless of substance use, are at risk of IDR, but students engaged in dual use of alcohol and cannabis face an elevated risk compared to both peers who do not use substances and those who use only a single substance. These findings emphasize the importance of targeted interventions that address the risks associated with IDR.
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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 | 0.000 | 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 | 0.000 | 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 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".