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Record W3025816693 · doi:10.1002/wps.20742

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

2020· article· en· W3025816693 on OpenAlexaboutno aff
Rosario Queirolo

Bibliographic record

VenueWorld Psychiatry · 2020
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsLegalizationRecreationCannabisPolitical scienceConsumption (sociology)CommercializationCitationGeographyLawSociologyPsychologySocial science

Abstract

fetched live from 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.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0060.006
Open science0.0020.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0250.001

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 head0.010
GPT teacher head0.290
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations31
Published2020
Admission routes1
Has abstractyes

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