Determinants and Prevalence of Cannabis-Impaired Driving Among North American Participants of a Brief Intervention for Cannabis Use: A Preliminary Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The legalization of cannabis in several U.S. states and Canada has raised concerns over cannabis-impaired driving. However, a paucity of data exists on cannabis consumption patterns and factors that affect risky behaviors associated with cannabis use. As a result, policy makers, insurers, and industry stakeholders have limited quantitative evidence to assess the severity of the problem. OBJECTIVE The objective of this preliminary study was to quantify the prevalence of cannabis-impaired driving and understand the factors that determine the propensity to drive impaired from users of a digital health educational intervention for cannabis use. METHODS Data were analyzed from 1,140 participants who completed “Check Your Cannabis” (CYC) between March and December 2019. The CYC asks a brief set of questions about an individual’s cannabis use, as well as questions about personal beliefs and behaviors. An ordered probit model was used to test relationships between cannabis use, demographics and driving behaviors. RESULTS While gender and age were not statistically significant factors in respondents reporting cannabis-impaired driving, high-risk behaviors were significant determinants of the probability of cannabis-impaired driving. Every 5-point increase in the ASSIST score increased the probability of sometimes driving after cannabis use by 4% (P<.001). Polysubstance use was also a statistically significant determinant of cannabis-impaired driving. Compared to the base group of participants who reported never drinking alcohol or using other substances with cannabis, those who sometimes drink or use other substances with cannabis were 13% (P<.001) more likely to sometimes or always drive after using cannabis. The largest amount spent on cannabis any given day was also a statistically significant predictor of cannabis-impaired driving, however, this effect was small. For example, an increased maximum expenditure on cannabis of $500 increased the probability of reporting sometimes driving after cannabis use by 5% (P=.02). CONCLUSIONS To our knowledge this is the first study to examine associations between self-reported cannabis use and driving behaviors. Our analysis indicates that contrary to current research and public perceptions, age and gender were not factors. However, largest amount spent on any given day, higher ASSIST scores, and polysubstance use was positively and significantly associated with driving under the influence of cannabis. Based on these results, public health campaigns and other interventions may have greater impact if they focus resources on problematic cannabis users rather than youth or the general population. Future research may investigate if spending patterns may give insight on those who purchase cannabis from non-retail sources.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".