MétaCan
Menu
Retour à la cohorte
Enregistrement W4200162257 · doi:10.1111/add.15752

We need convincing data to support a public health approach to cannabis regulation

2021· letter· en· W4200162257 sur OpenAlexaffabout
Rebecca Jesseman

Notice bibliographique

RevueAddiction · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueCannabis and Cannabinoid Research
Établissements canadiensCanadian Centre on Substance Use and Addiction
Organismes subventionnairesnon disponible
Mots-clésCannabisLegalizationLegislationLegislatureContext (archaeology)Public healthDecriminalizationPolitical scienceBusinessPublic relationsPublic administrationMedicineLawPsychiatryGeography

Résumé

récupéré en direct d'OpenAlex

Agreed measures of cannabis use and impacts are essential in evaluating regulatory change and supporting policy decisions driven by public health evidence. A shared understanding of measures also responds to the call for greater collaboration and coordination in cannabis research. The iCannToolkit provides a collaborative foundation that can be expanded to additional domains and to address emerging and context-specific data needs. The article by Lorenzetti et al. highlights the challenges posed by the lack of agreed minimum standards for quantifying cannabis use or dosage [1]. It also demonstrates a collaborative approach to reach agreement on measures, with the potential for expansion to support a more comprehensive understanding of the impacts of cannabis regulation. Canada's regulated retail cannabis market launched in October 2018, joining only Uruguay and a handful of US states that had previously legalized non-medical sales and use. Canada's Cannabis Act sets out clear objectives for legalization, focused upon public health and public safety [2]. It also mandates that a review of the administration, operation and impact of the act be conducted 3 years following implementation. Currently, in 2021, more than 20 states in the United States have passed or are in the process of passing legislation permitting adult non-medical cannabis use and steps towards legal retail are being taken internationally, including in Mexico, Malta, Switzerland, Luxembourg and the Netherlands. Those considering legalization are looking to learn from the experience of those who have gone before. Also, Canada's legislative review of the Cannabis Act is poised to begin, opening a window during which public health, public safety, academia, government and industry, among others, will attempt to influence regulatory adjustments. To monitor the impact of legalization Canada has invested in two national population surveys, the National Cannabis Survey and the Canadian Cannabis Survey, and has provided funds that are supporting research initiatives across the country [3]. Although many of these studies include measures of consumption, associated risk behaviour and source, there is limited comparability between them. Even Canada's two national surveys use different time-frames and sampling methods to report current rates of use and cannot be compared [4, 5]. In the absence of agreed measures, answers to basic questions concerning changes in consumption, product sources and risk behaviour are subject to variation. Data on prevalence vary according to how a question is asked [6]. That variation allows strategic selection of data that support particular interests, and therefore poses a challenge to objective public health and safety policy decisions. For example, a survey question that asks respondents how often they smoke cannabis may miss the increasing rates of consumption via alternative methods such as vaping and ingestion, resulting in an underestimate and even a perceived decline in overall use. Consistent data regarding the impacts of cannabis use and policy are therefore essential to support policy decisions that reflect public health and safety interests; particularly in the context of increasing industry influence [7]. Policymakers in Canada determining whether the Cannabis Act is tracking towards its objectives, and whether regulatory change is required or appropriate, are faced with the challenge of determining which data to trust. On an international scale, policymakers faced with the question of legalization are looking to determine whether it has increased or mitigated harms, and how. At all levels, the public health sector needs valid and reliable data to make a convincing case to introduce and retain public health restrictions that frustrate the profit interests of the private sector; for example, those on advertising, marketing and taxation [7, 8]. As Lorenzetti et al. acknowledge, the iCannToolkit will not fill all of these knowledge gaps. Standardized, or at least comparable, measurements of cannabis consumption must be accompanied by retail, health and criminal justice data to create a comprehensive picture of the impacts of legalization. Priorities include measures of equity, product sales and illegal market share [6]. The iCannToolkit demonstrates the potential to apply a similar Delphi procedure to generate additional core measures, informed by a comprehensive list of options currently in use across different contexts. Such a list, or menu, is currently in development and is intended to provide a publicly available reference for those interested in measuring cannabis use towards the objective of increasing data sharing and comparability. The variation in regulatory and retail approaches within Canada, across states in the United States and in the planned Swiss pilot studies frames an incredible natural experiment in which to test common measures across settings. A shared understanding of measures also responds to the call for greater collaboration and coordination in cannabis research [6, 9]. The greatest challenge for the field may be in identifying appropriate leadership, capacity and funding to support a coordinated approach. None.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,226
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,110
Tête enseignante GPT0,337
Écart entre enseignants0,227 · 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.

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

Citations4
Publié2021
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueAddictionMême sujetCannabis and Cannabinoid ResearchTravaux en français237 207