The clouded debate: A systematic review of comparative longitudinal studies examining the impact of recreational cannabis legalization on key public health outcomes
Notice bibliographique
Résumé
Background: Ineffective cannabis regulatory frameworks such as prohibition have sparked interest in alternative solutions to reduce individual and societal harms. While it has been suggested that the recreational legalization process has yielded early successes, the relatively recent implementation of the novel policies has provided a modest time frame for a truly thorough establishment and assessment of key population-level indicators. The following systematic review focuses on identifying the downstream public health sequelae of cannabis legalization policies, including parameters such as cannabis consumption rates, hospitalization rates, vehicular accidents and fatalities, criminal activity, and suicidal behaviors, as well as other substance use trends. Methods: An exhaustive search of the MEDLINE and Google Scholar databases were performed to identify high-quality (1) longitudinal studies, which (2) compared key public health outcomes between regions which had and had not implemented recreational cannabis legalization (RML) policies, (3) using distinct databases and/or time frames. Thirty-two original research articles were retained for review. Results: Adult past-month cannabis consumption (26+ years) seems to have significantly increased following RML, whereas young adult (18-26 years) and adolescent (12-17 years) populations do not show a significant rise in past-month cannabis use. RML shows preliminary trends in increasing service use (such as hospitalizations, emergency department visits, or poisonings) or vehicular traffic fatalities. Preliminary evidence suggests that RML is related to potential increases in serious/violent crimes, and heterogeneous effects on suicidal behaviors. While the research does not illustrate that RML is linked to changing consumptions patterns of cigarette, stimulant, or opioid use, alcohol use may be on the rise, and opioid prescribing patterns are shown to be significantly correlated with RML. Conclusion: The current data supports the notion that RML is correlated with altered cannabis consumption in adults, potentially increased criminal activity, and a decline in opioid quantities and prescriptions provided to patients. Future work should address additional knowledge gaps for vulnerable populations, such as individuals with mental health problems or persons consuming cannabis frequently/at higher THC doses. The effects of varying legalization models should also be evaluated for their potentially differing impacts on population-level outcomes.
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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 | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».