Self-Reported Collision Risk Associated With Cannabis Use and Driving After Cannabis Use Among Ontario Adults
Bibliographic record
Abstract
OBJECTIVE: This study examined the effects of cannabis use and driving after cannabis use on self-reported collision involvement within the previous 12 months while controlling for demographics, driving exposure, binge drinking, and driving after drinking based on a large representative sample of adults in Ontario. METHODS: Data are based on the CAMH Monitor, an ongoing cross-sectional telephone survey of Ontario adults aged 18 and older, conducted by the Centre for Addiction and Mental Health. Data on drivers who reported driving at least one kilometer per week and who responded to the collision item from 2002 to 2007 were merged into one data set (n = 8481). Logistic regression analysis of self-reported collision risk posed by cannabis use (lifetime and past 12 months), driving after cannabis use (past 12 months), and driving after drinking among drinkers (past 12 months) was implemented, controlling for the effects of gender, age, region, income, education, marital status, kilometers driven in a typical week, and consuming five or more drinks of alcohol on one occasion (past 12 months). Due to list-wise deletion of cases the logistic regression sample was reduced (n = 6907). RESULTS: Several demographic factors were found to be significantly associated with self-reported collision involvement. The logistic regression model revealed that age, region, income, marital status, and number of kilometers driven in a typical week, were all significantly related to collision involvement, after adjusting for other factors. Respondents who reported having driven after cannabis use within the past 12 months had increased risk of collision involvement (odds ratio [OR] = 1.84) compared to those who never drove after using cannabis, a greater risk than that associated with having reported driving after drinking within the past 12 months (OR = 1.34). CONCLUSION: Further investigation of the impact of driving after cannabis use on collision risk and factors that may modify that relationship is warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".