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Record W4385613487 · doi:10.1111/add.16312

Commentary on Allaf et al.: Comparing countries with different legal cannabis markets can inform on the impact of regulating product type and potency

2023· letter· en· W4385613487 on OpenAlexaboutno aff
Martine Skumlien, Sam Craft

Bibliographic record

VenueAddiction · 2023
Typeletter
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsCannabisHarmLegislationBusinessProduct (mathematics)PopulationPotencyEnvironmental healthLegalizationMedicineLawPolitical sciencePsychiatry

Abstract

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Regulating the type and potency of cannabis products that can be sold in a legal market may influence the rate of cannabis-related harms. Comparing countries with differing frameworks for legalisation can provide insight into effective strategies for minimising harm, and data must be routinely collected to monitor important health outcomes. The potential benefits and harms of legalising cannabis are hotly debated in many countries. However, these likely depend on the regulatory framework, such as whether restrictions are placed on product types and potency [1]. Comparing states that have taken different approaches to legalisation can help identify which strategies are effective for minimising cannabis-related harms in a legal market. In their systematic review, Allaf et al. [2] found that legalisation or decriminalisation of cannabis was followed by a rise in acute cannabis poisoning in paediatric patients, where it appeared to be driven in part by increased use and availability of edibles. Strict regulation on the manufacturing, sale and advertisement of edibles may, therefore, lower the risk of accidental exposure in the paediatric population. This could include mandating plain, child-resistant packaging, prohibiting the sale of products that may appeal to children (e.g. gummies, animal-shaped products) or that mimic existing trademarked confectionary products or banning the sale of edibles altogether [3]. Edibles and concentrates also typically have higher Δ9-tetrahydrocannabinol (THC) potency compared with flower products [4]. Consuming higher potency cannabis is linked with increased risk of adverse effects, further amplifying the risk of poisoning with accidental exposure [5]. In a recent article, Hall et al. [6] propose that cannabis potency can be regulated in legal markets by (i) banning high-THC products; (ii) capping THC content in cannabis products; or (iii) increasing cannabis taxes in proportion to THC content. Regulating products according to standard THC units (1 unit = 5 mg) should also be considered [7, 8]. However, greater restrictions on legal cannabis markets could also shift users into the illicit market if the latter can offer consumers greater product choice at lower prices, which would confer its own risks. Ultimately, research is still needed to establish how restrictions on potency, and the availability and prevalence of high-potency cannabis products (including flower, edibles, concentrates and extracts), affect poisoning rates. Research on the effectiveness of product restrictions for minimising cannabis-related harm can benefit from the diverse regulatory approaches taken by different countries that have legalised cannabis. Only studies from the United States (US) and Canada, and one study from Thailand, were identified by the review by Allaf et al. [2]. However, many countries across Africa, Europe and South America, as well as the Australian Capital Territory, have decriminalised or legalised recreational or medicinal cannabis in the past decade. Myriad factors may influence the effect of legalisation or decriminalisation in any given country, such as the existing culture and typical practices around cannabis use and the degree to which public health factors are permitted to drive the regulatory framework. For instance, Uruguay, the first country to fully regulate its recreational cannabis market, has taken a relatively restrictive and public health-driven approach [9]. Uruguay allows sale of flower-based products exclusively, which can only be obtained from a pharmacy, approved Cannabis Social Clubs, or home cultivation and with a yearly limit of 480 g per person. A cap of 9% THC applies to pharmacy cannabis, although the ban on extracts and edibles limits the de facto potency cap to the biological THC ceiling in cannabis flower, which is ~35% [10]. By contrast, the 23 US states that have legalised cannabis for recreational purposes to date have typically adopted more commercially driven models, allowing a wide range of product types and potency levels [11]. For instance, the sale of edibles is permitted in all 23 states, many of which have no legal restrictions on the permitted THC dosage per serving or package [12]. Uruguay and the US states of Colorado and Washington both legalised cannabis ~10 years ago, allowing for the comparison of longer-term trends in two countries whose approaches to legalisation have differed substantially. Comparisons between these jurisdictions could serve as a useful guideline to other countries which are considering different frameworks for cannabis legalisation. However, it is noteworthy that in their review, Allaf et al. [2] did not identify any studies measuring changes in cannabis-related poisonings following legalisation in Uruguay. It is essential that as more jurisdictions look to legalise cannabis through different regulatory frameworks, robust data collection systems are set up to monitor and evaluate the impact on important health indicators. Martine Skumlien: Conceptualization (lead); writing—original draft (lead); writing—review and editing (lead). Sam Craft: Conceptualization (supporting); writing—review and editing (supporting). None. MS is funded by a grant from the Engineering and Physical Sciences Research Council (EPSRC), grant number EP/V026917/1. SC is funded by grant MR/N0137941/1 for the GW4 BIOMED MRC DTP, awarded to the Universities of Bath, Bristol, Cardiff, and Exeter from the Medical Research Council (MRC). The authors have no competing interests to declare. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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)0.000
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 score0.252
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.292
Teacher spread0.273 · 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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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