Perceptions and Attitudes Related to Driving after Cannabis Use in Canadian and US Adults
Bibliographic record
Abstract
Background: This study examined the risk perceptions related to driving after cannabis use (DACU) among Canadian and US adults who used cannabis in the past six months. Methods: Perceptions of danger, normative beliefs, perceived likelihood of negative consequences, and other driving-related variables were collected via online surveys in Canadian (n = 158; 50.0% female, 84.8% White, mean age = 32.73 years [SD = 10.61]) and US participants (n = 678; 50.9% female, 73.6% White, mean age = 33.85 years [SD = 10.12]). Driving cognitions and DACU quantity/frequency were compared between samples using univariate analyses of variance, and Spearman’s (ρ) correlations were performed to examine associations between driving cognitions and DACU quantity/frequency. Results: The two samples did not significantly differ in self-reported level of cannabis use, lifetime quantity of DACU, or the number of times they drove within two hours of cannabis use in the past three months ( Ps > .12). Compared to US participants, Canadians perceived driving within two hours of cannabis use as more dangerous ( P < 0.001, η p 2 = 0.013) and reported more of their friends would disapprove of DACU ( P = 0.03, η p 2 = 0.006). There were no differences in the number of friends who would refuse to ride with a driver who had used cannabis ( P = 0.15) or the perceived likelihood of negative consequences ( Ps > 0.07). More favorable perceptions were significantly correlated with greater lifetime DACU and driving within two hours of use (ρ = 0.25-0.53, Ps < 0.01). Conclusions: These findings reveal differences in distal risk factors for DACU between Canada and the US and may inform prevention efforts focusing on perceptions of risk and social acceptance of DACU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".