Emerging adult perceptions of higher-risk cannabis consumption behaviours
Notice bibliographique
Résumé
BACKGROUND: Emerging adults have the highest cannabis consumption rates in Canada and are among the most vulnerable to cannabis-related harms. Since certain cannabis consumption behaviours carry greater risks of harm, the Lower-Risk Cannabis Use Guidelines (LRCUG) provide harm reduction strategies. To address a critical gap in the literature, the current study examined emerging adults' awareness of the guidelines and perceptions of higher-risk cannabis consumption behaviours identified within the LRCUG. METHODS: Emerging adults (N = 653) between the ages of 18-25 years were recruited from across Canada. Participants were presented with five vignettes depicting a character's cannabis consumption behaviours. Each vignette focused on a unique aspect of the character's consumption (frequency, polysubstance use, family history of mental illness, method of consumption, and potency). Participants were randomly assigned to one of three conditions within each of the five vignettes that were altered to capture varying levels of risk (e.g. weekly, almost daily, or daily consumption). Following each vignette, participants were asked to respond to four items relating to overall risk of harm, cognitive health, physical health, and mental health. RESULTS: Participants perceived: (1) frequent consumption to be associated with greater risks than less frequent consumption; (2) simultaneous consumption of cannabis and tobacco as being associated with higher risk of harm, yet no difference between simultaneous consumption of cannabis and alcohol or cannabis consumption alone; (3) consuming cannabis with a family history of psychosis or substance use disorder as being associated with greater overall risk than consumption with no family history; (4) smoking and vaping cannabis as associated with more risk than ingesting edibles; and (5) higher-potency THC-dominant strains as being associated with more risk than lower-potency CBD-dominant strains, yet no difference between the two higher-potency THC-dominant strains. CONCLUSIONS: While emerging adults seemed to appreciate the risks associated with some cannabis consumption behaviours, they had difficulty identifying appropriate levels of harm of other higher-risk behaviours. Through an improved understanding of emerging adult perceptions, effective education campaigns should be designed to improve the awareness of cannabis risks and encourage the uptake of harm reduction awareness and strategies.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 tête enseignante, 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 ».