The Good, The Bad, and The Uncertain: A Systematic Review of the Impacts of Recreational Cannabis Legalization
Notice bibliographique
Résumé
Background: Recreational cannabis legalization has become more prevalent over the past decade, with recreational cannabis now legal in 13 U.S. states and nationally in Uruguay, Canada, and Malta. At the same time, there is evidence for increasing rates of cannabis use and consequences from use (i.e., crime and motor vehicle accidents) among some age groups, particularly in North America. Thus, it is crucial to understand the role of recreational legalization on person-level outcomes, such as cannabis consumption and health-outcomes. Previous reviews have focused on outcomes in the U.S., but there remains a need for a broader international evaluation of the effect of legalization. Additionally, there is a dearth of reviews examining recreational cannabis legalization as past literature has focused more attention on medical cannabis legalization. The current review examined the role of recreational cannabis legalization on a variety of person-centered outcomes to understand if the consequences of legalization. Method: A comprehensive systematic review was conducted in accordance with PRISMA guidelines. Inclusion criteria involved: empirical quantitative research, article available in English, a research design evaluating the impact of recreational cannabis legalization (e.g., pre vs. post), and person-level cannabis use, and/or clinical outcomes included as dependent variables (opposed to outcomes such as changes in the price or potency of cannabis). Two individuals independently coded studies for eligibility and extracted information. The search revealed 69 studies that met criteria for inclusion. Results: Our search revealed five main categories of outcomes associated with recreational cannabis legalization: cannabis and substance use behaviors, health-care impacts, attitudes surrounding cannabis, crime-related outcomes, and driving-related outcomes. The studies were predominantly cross-sectional designs and most took place within the U.S. The extant literature revealed inconsistencies surrounding the role of recreational legalization. In the case of cannabis use behaviours, there was mixed evidence for different subpopulations. For example, legalization was associated with increased cannabis use for college samples, but there were inconsistent findings for adolescents. Additionally, some studies pointed towards negative consequences associated with legalization, such as increased cannabis use in clinical populations and increased cannabis-related healthcare visits. Other studies suggested no impact of legalization through either no change in examined outcomes or heterogenous conclusions (e.g., cannabis-related attitudes). Finally, there was also evidence of positive consequences for recreational legalization, such as decreased drug-related arrests and cigarette use. Conclusions: Overall, the balance of research suggests there is no clear support for any large magnitude person-level impact of recreational cannabis legalization. This review indicates that more conclusive evidence regarding the impact of recreational legalization is still required and reveals a need for more longitudinal study designs with longer-term follow-ups as well as studies from a broader range of geographic jurisdictions. As recreational cannabis legalization becomes implemented globally, a systematic evaluation of the outcomes associated with enactment is of increasing relevance.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,015 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,007 |
| Bibliométrie | 0,011 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 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 ».