Driving under the Influence of Cannabis or Alcohol in a Cohort of High-frequency Cannabis Users: Prevalence and Reflections on Current Interventions
Bibliographic record
Abstract
Driving under the influence of alcohol or cannabis is a major public health concern, as both are major risk factors for motor vehicle accidents (MVAs). Prevalence levels for both driving-risk behaviours have increased in Canada in recent years, despite punitive laws and enforcement aimed at impaired driving. Young drivers are a major risk group, due to their common substance use and MVA involvement. Data from a cohort of N=102 high-frequency cannabis users [18–28 years old, 70 males and 32 females] who were also active alcohol users, recruited by mass advertising from university student populations in Toronto, indicated that a significantly (p=0.009) higher proportion of the sample [35.0%] had driven a car while under the influence of cannabis than had driven while under the influence of alcohol [4.9%] or of a combination of cannabis and alcohol [3.9%] in the 30 days prior to the assessment. Multiple explanations of this finding are possible. First, law-enforcement and practical deterrence effects for alcohol- versus cannabis-impaired driving in Canada may be substantially different. Second, cannabis users may generally believe that the impairment effects of cannabis are limited, and frequent users may specifically believe in their ability to control cannabis's effects on driving. Implications for interventions and policy are discussed.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".