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Enregistrement W3025816693 · doi:10.1002/wps.20742

The effects of recreational cannabis legalization might depend upon the policy model

2020· article· en· W3025816693 sur OpenAlex
Rosario Queirolo

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Notice bibliographique

RevueWorld Psychiatry · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueCannabis and Cannabinoid Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLegalizationRecreationCannabisPolitical scienceConsumption (sociology)CommercializationCitationGeographyLawSociologyPsychologySocial science

Résumé

récupéré en direct d'OpenAlex

Since 2012, when Colorado and Washington State started the path to legalize cannabis for recreational purposes, the trend has been growing. Uruguay became in 2013 the first country to legalize the whole process: from production to distribution, commercialization and consumption. Canada followed suit in 2018. By January 2020, eleven states in the US, Uruguay and Canada have legal access to recreational cannabis for adults, and other countries have started the legalization process or the discussion about it, as is the case of Luxembourg and New Zealand. Each of these experiences of legalizing cannabis is different from the others1. Legalization in the US and Canada has followed a deeply commercial model, while legalization in Uruguay is heavily regulated and controlled by the government2. Even in Canada, there are significant differences in the set of rules that each province has opted to follow while legalizing. For example, in some Canadian territories the minimum age for use is 18 years, while in others it is 21. The features of each legalization policy model might have a different impact on the expected outcomes. Some regulatory policies might increase certain legalization adverse effects, while decreasing other neg­ative impacts. For example, the Uruguayan cannabis legislation forbids the selling of cannabis edibles, which might reduce intoxications among minors but increases the percentage of users that smoke cannabis. So, it is important to compare the effects of the different models of cannabis legalization and not assume that all the experiences will produce the same results. In other words, it is important to take advantage of the existing variance of policy design. The way in which you regulate might lead to different effects on public health and the other objectives that the policy is designed for3. Hall and Lynskey’s paper4 mentions several ways to assess the public health impact of legalizing recreational cannabis use, on the basis of the US experience. The authors provide a very significant contribution to the emerging debate on the importance of reaching an agreement on a group of indicators to be monitored, possibly aggregating them in an index to measure their overall impact on public health5. They also recommend that the evaluation looks at outcomes in the short run but also in the long term. For example, they point out that legalization might “enable more adults to use cannabis for a longer period of their lives”. It will be necessary to keep track of the impact of this prolonged use on car crash fatalities and injuries, as well as on emergency department attendances related to cannabis consumption. The authors also call the attention to the possibility that cannabis legalization becomes a federal national policy in the US, which will reduce cannabis prices, because cannabis industry will try to enhance profits by increasing the size of the market. In order to evaluate the impact of the current legalization experiences, it is crucial to measure their effects both on public health and on users’ criminalization and contacts with illegal activities. The Uruguayan cannabis regulation model is a middle-ground option between prohibition and commercialization, in which the government imposes strict regulations for users: mandatory registry, maximum amount of cannabis per user (40 g per month and 480 g per year), no advertisement, no selling to tourists, no edibles allowed. These restrictions were planned to control consumption and accomplish the public health goal of the regulation. The Uruguayan government-oriented model with strict regulations has had a positive impact on controlling substance quality as well as on reducing users’ contact with illegal activities. Available data on frequent cannabis users suggest that Uruguayans abandoned prensado, a poor quality cannabis sold illegally, and moved to use flowers. Also, they reduced their contacts with illegal dealers and selling points. In that sense, in Uruguay, the regulation made cannabis use safer than before5. However, the same restrictions might have kept the black market alive, because many users refuse the registry. Among the goals that cannabis legali­zations pursue, minimizing youth consump­tion is frequently mentioned (see, for example, the Canadian Cannabis Act6). In Uruguay, at this moment, there is no evidence about the impact of legalization on youth consumption produced by research using a control group, but cannabis use among young people had been increasing before 2013, and the trend has apparently remained almost the same after legalization was implemented7. Regardless of the evidence, why should we expect a reduction in consumption among adolescents with legalization? It could be argued that, although minors do not have legal access, the increase in cannabis accessibility is likely to lead to more youth consumption. Hall and Lynskey emphasize the impor­tance of assessing the public health effects of cannabis legalization. I would add that it is essential to evaluate the effects of the different legalization policies on all the out­comes they are designed to accomplish, keeping in mind that each legalization mod­el could improve some outcomes while wors­ening others. In order to do that, funding to collect good quality data and conduct research that includes control groups is essential. Coming up with agreements about which indicators should be monitored would be extremely useful, in order to allow collection of comparable data in the different territories where legalization is taking place. By doing that, we will be able to evaluate the impact of different policy designs and contribute to a more evidence-based discussion about the pros and cons of each model.

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.

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)0,000
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: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,918
Score d'incertitude au seuil0,288

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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 recherche0,0000,000
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 Opus0,010
Tête enseignante GPT0,290
Écart entre enseignants0,280 · 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