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Record W4313641795 · doi:10.1111/add.16128

Increase in cannabis‐related emergency department presentations in the period immediately before legalization requires explanation

2023· letter· en· W4313641795 on OpenAlexaboutno aff
Bobby P. Smyth, Peter McCarron

Bibliographic record

VenueAddiction · 2023
Typeletter
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsLegalizationCannabisGovernment (linguistics)LegislationMedicineEmergency departmentLegislaturePolitical sciencePsychiatryLaw

Abstract

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Myran et al. [1] recently reported on changes in cannabis related emergency department (ED) attendances in Ontario across a period of major policy change in Canada. The conclusion that ‘cannabis related ED visits decreased following recreational cannabis legalization with strict retail controls’ is misleading. Three time periods were included, a pre-legalization phase (January 2016–September 2018), a post legalization period (October 2018–February 2020) when there was strict retail control and finally a period of commercialization (March 2020–May 2021). The authors focus on the expansion of government-regulated cannabis stores, which increased greatly during commercialization. The period of most dramatic change in cannabis-related ED attendances occurs during the pre-legalization period, where increases of 2% per month were reported. It seems highly unlikely that this constitutes a sustained secular trend as it implies a greater than 10-fold increase in attendances per decade. The use of this relatively brief period of major acceleration in ED attendances as the secular trend generates the finding that cannabis-attributable ED visits decreased following legalization. What factors may explain the rapidly increasing rate of ED attendances in the 2 years before legalization, and are these really independent of the legalization process itself? The Canadian government announced its intention to legalize cannabis in late 2015 [2]. Legalization legislation was unveiled in April 2017 [3]. The many steps involved in a legislative process can be confusing to the general public and may result in the assumption that an activity is legal once government declares its intention to legalize [4]. The incremental liberalization of cannabis policy in Canada appears to have also influenced police behaviour, with year on year declines in cannabis arrests from 2011 onwards while rates of use were relatively stable [5, 6]. This indicates incremental reduction in enforcement of laws, which were due to be repealed, and similar patterns are evident in United States [7]. Although there is some debate about the effectiveness of penalties as deterrents, they are certainly not going to have any effect if not used [8]. Perhaps the most important oversight by the authors regarding the pre-legalization phase is the existence and expansion of a vibrant grey market of cannabis dispensaries in Ontario, operating under the guise of ‘medical’ cannabis [9, 10]. In May 2016, the Mayor of Toronto stated, ‘The speed with which these storefronts are proliferating, and the concentration of dispensaries in some areas of our city, is alarming’ [11]. The importance of grey market dispensaries was highlighted in a recent study of youth attending addiction treatment in Ontario, which found that dispensaries were the most common source of cannabis before legalization [12]. The failure to provide context regarding the utilized pre-legalization phase makes it difficult for readers to know how the results might translate to other settings. During this period of preparation for legalization in Ontario, there was undermining of injunctive norms against use by the political leadership, incremental deprioritization of enforcement by police and an expanding network of cannabis dispensaries in the grey market. Legalization should be viewed as a long-term process lasting years and commencing well in advance of the date of enactment of legislation [13]. In Ontario, the process arguably started in late 2015. Future studies using time series analysis should select time periods before the process commencing, for example the period 2010 to 2015 in this case, to establish secular trends that are uncontaminated by the legalization process itself. None. None to declare. Bobby Smyth: Conceptualization; writing – original draft; writing – review and editing. Peter McCarron: Writing – original draft; writing – review and editing.

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)0.001
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 score0.795
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.311
Teacher spread0.290 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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