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Enregistrement W4385613431 · doi:10.1111/add.16314

How to interpret studies on the impact of legalizing cannabis

2023· letter· en· W4385613431 sur OpenAlexaffabout
Jakob Manthey, Michael J. Armstrong, Tobias Hayer, Daniel T. Myran, Rosalie Liccardo Pacula, Rosario Queirolo, Jürgen Rehm, Marielle Wirth, Frank Zobel

Notice bibliographique

RevueAddiction · 2023
Typeletter
Langueen
DomaineMedicine
ThématiqueCannabis and Cannabinoid Research
Établissements canadiensPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental HealthBruyèreUniversity of OttawaBrock University
Organismes subventionnairesnon disponible
Mots-clésLegalizationCannabisLegislaturePolitical scienceEnvironmental healthMedicinePsychiatryLaw

Résumé

récupéré en direct d'OpenAlex

We appreciate the systematic review of cannabis legalization’s impacts in Canada by Hall et al. [1], whose content overlaps that of a previous review [2]. Summaries of the ever-growing legalization evidence base are important for both researchers and policymakers. For example, Germany’s Health Ministry asked us to review the literature to inform that country’s legislative planning. After surveying 164 studies from Canada, Uruguay and the United States [3], our conclusions were similar to those of Hall et al. However, we wish to highlight three points that merit greater consideration in future research. First, before legalization, cannabis use in Canada had already been increasing for years [4]. Cannabis use prevalence and cannabis-related emergency department visits in Ontario were already rising before 2018’s legalization [5, 6], and there were apparent changes in alcohol sales after medical cannabis usage expanded in 2015 [7]. This means that any results from simple before-and-after legalization comparisons should be interpreted with great caution. We therefore recommend that studies of post-legalization changes account for pre-legalization trends and appropriately identified control groups to avoid overstating legalization’s impacts. This could be conducted, for example, by estimating trends prior to legalization, predicting those trends out to the future and then comparing them with actual results [8]. Secondly, after legalization, it took time for Canada’s new legal market to establish itself: store counts grew every year [9], as did consumers’ willingness to disclose usage [10]. This means that researchers’ post-legalization time-frames greatly affect their likelihood of detecting effects and that literature reviews should not give the same weight to studies of, for example, the first year of legalization as to those covering year 3. For example, when we compared studies with fewer than 2 years of post-legalization data to those with more than 2, the latter provided much clearer indications of increased consumption and health outcomes [3]. We therefore recommend that other researchers place more emphasis on longer-term post-treatment study designs. Thirdly, although Canada legalized nation-wide, there were meaningful implementation differences among its 13 provinces and territories regarding retailing (e.g. government-owned versus business), consumption (e.g. public smoking allowed versus banned) and products allowed. These differences have subsequently been seen in their outcomes. For example, hospitalizations for cannabis poisonings in children increased overall after legalization, but the increases differed according to the degree of commercialization and the product types sold [11]. For academics and policymakers, those interjurisdictional differences are at least as interesting as the national averages. We therefore recommend that researchers pay more attention to this heterogeneity in study designs. For all these reasons, it is important for researchers to treat cannabis legalization as a complex process, rather than an instantaneous binary intervention. Where data quality permits, studies should increasingly account for past societal trends, ongoing market maturation and interjurisdictional differences. Researchers should also complement survey-based studies with other methods, such as analysis of work-place toxicological tests [12] or wastewater data [13]. Jakob Manthey: Conceptualization (lead); writing—original draft (lead); writing—review and editing (equal). Michael J. Armstrong: Conceptualization (supporting); writing—review and editing (lead). Tobias Hayer: Conceptualization (supporting); writing—review and editing (supporting). Daniel T. Myran: Conceptualization (supporting); writing—review and editing (supporting). Rosalie Liccardo Pacula: Conceptualization (supporting); writing—review and editing (supporting). Rosario Queirolo: Conceptualization (supporting); writing—review and editing (supporting). Jürgen Rehm: Conceptualization (supporting); writing—review and editing (supporting). Marielle Wirth: Writing—review and editing (support); Frank Zobel: Conceptualization (supporting); writing—review and editing (supporting). None. Open Access funding enabled and organized by Projekt DEAL. J.M. has worked as consultant for and received honoraria from public health agencies. All other authors do not declare any conflicts of interest. Not applicable.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,594
Score d'incertitude au seuil0,610

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,059
Tête enseignante GPT0,370
Écart entre enseignants0,311 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations10
Publié2023
Routes d'admission2
Résumé présentoui

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