Determinants and Prevalence of Cannabis-Impaired Driving Among North American Participants of a Brief Intervention for Cannabis Use: A Preliminary Study (Preprint)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».