Elevated Demand and Proportionate Substance-related Reinforcement are Associated with Driving after Cannabis Use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Objective: Cannabis is the most prevalent illicit drug detected among drug-impaired drivers and the most frequently used illicit drug on college campuses. Behavioural economic variables, such as demand and proportionate substance-related reinforcement, have been identified as risk factors for driving after substance use. Though driving after cannabis use (DACU) represents a significant public health concern, no previous research has investigated behavioural economic predictors of DACU among college cannabis users. The present study evaluated the hypothesis that elevated cannabis demand and proportionate substance-related reinforcement would be associated with DACU among college cannabis users. Method: Participants were 132 college students who reported cannabis use on 4 or more days in the past month. Participants completed a Marijuana Purchase Task that assessed hypothetical cannabis consumption across 20 prices, the Adolescent Reinforcement Survey Schedule–Substance Use Version to measure past-month activity participation and enjoyment from substance-related and substance-free activities, and questions regarding DACU. Results: In ordinal logistic regression models that controlled for past-month cannabis use frequency, gender, age, and ethnicity, cannabis demand (intensity) and substance-related reinforcement were both significantly associated with DACU. Conclusions: These results provide evidence that demand and substance-related reinforcement are associated with DACU. Intervention approaches aiming to reduce DACU among college students should target demand and engagement in substance-free activities. Objectif: Le cannabis est la drogue illicite la plus fréquemment détectée parmi les conducteurs aux facultés affaiblies par la drogue et la drogue illicite la plus fréquemment consommée sur les campus universitaires. Des variables économiques comportementales, telles que la demande et un renforcement proportionnel lié à la substance, ont été identifiés comme facteurs de risque de conduite après une consommation de drogue. Bien que conduire après avoir consommé du cannabis (DACU) représente un problème de santé publique important, aucune étude antérieure n’a enquêté sur les prédicateurs économiques comportementaux de la DACU chez les consommateurs de cannabis des collèges. La présente étude a évalué l’hypothèse selon laquelle une demande élevée de cannabis et un renforcement proportionnel lié à la substance seraient associés à la DACU chez les consommateurs de cannabis des collèges. Méthode: Les participants étaient 132 étudiants ayant déclaré avoir consommé du cannabis au moins 4 jours au cours du dernier mois. Les participants ont achevé une tâche d’achat de marijuana (MPT) qui évaluait la consommation hypothétique de cannabis selon 20 prix d’achat, le programme d’enquête sur le renforcement des adolescents – la version sur la consommation de substances, afin de mesurer la participation et le plaisir de l’activité au cours des mois précédents et des questions relatives à la conduite après la consommation de cannabis. Résultats: Dans les modèles de régression logistique ordinale qui contrôlaient la fréquence, le sexe, l’âge et l’appartenance ethnique du cannabis, la demande de cannabis (intensité) et le renforcement lié à la substance étaient tous deux significativement associés à la DACU. Conclusions: Ces résultats démontrent que la demande et le renforcement lié à la substance sont associés à la DACU. Les approches d’intervention visant à réduire la DACU parmi les étudiants doivent cibler la demande et la participation à des activités sans substance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it