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Record W4385613431 · doi:10.1111/add.16314

How to interpret studies on the impact of legalizing cannabis

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

Bibliographic record

VenueAddiction · 2023
Typeletter
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental HealthBruyèreUniversity of OttawaBrock University
Fundersnot available
KeywordsLegalizationCannabisLegislaturePolitical scienceEnvironmental healthMedicinePsychiatryLaw

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.594
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.370
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2023
Admission routes2
Has abstractyes

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