Toking and driving: Characteristics of Canadian university students who drive after cannabis use---an exploratory pilot study
Bibliographic record
Abstract
Cannabis use is increasingly prevalent among young adults in Canada. Due to cannabis’ impairment effects, driving under the influence of cannabis has recently developed into a traffic-safety concern, yet little is known about the specific circumstances and factors characterizing this behavior among young people. In this study, we interviewed a sample of university students (n = 45; age 18–28 years) in Toronto who had driven a car after cannabis use in the past year. The study collected information on respondents’ sociodemographic characteristics, cannabis and other drug use, cannabis use and driving (CUD) experiences, law enforcement and accident exposure, perceptions of cannabis and alcohol impairment effects as well as future anticipated substance use and driving behaviors. Results indicated that: CUD originated primarily from social settings; that impairment risks from cannabis were perceived to be low; and that the level of anticipated future CUD was high. Furthermore, high frequency of CUD in the past year was associated with high frequency of cannabis use. Interventions aiming at CUD among young people need to be anchored in the specific sociocultural settings of this behavior; targeted information needs to address cannabis’ impairment potential for driving; possibilities for harm-reduction measures for CUD need to be considered.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".