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Record W4412411499 · doi:10.1111/add.70146

Cannabis use patterns and comparison trends in Canada

2025· letter· en· W4412411499 on OpenAlexafffundabout
Benedikt Fischer, Didier Jutras‐Aswad, Bernard Le Foll, Daniel T. Myran

Bibliographic record

VenueAddiction · 2025
Typeletter
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de MontréalUniversity of OttawaWaypoint Centre for Mental Health CareOttawa HospitalSimon Fraser UniversityUniversity of TorontoBruyèreCentre for Addiction and Mental HealthUniversity of the Fraser Valley
FundersUniversity of TorontoIndiviorDepartment of Psychiatry, University of TorontoUniversity of Ottawa
KeywordsCannabisEnvironmental healthMedicinePsychologyPsychiatry

Abstract

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In a recent noteworthy article, Caulkins examined long-term cannabis use trends in the USA (1979–2022), with reference to associations with different cannabis policy periods [1]. These evolving trends translated into declines in cannabis use until the early-1990s, partial recovery towards the early-2000s and substantial increases since then. In addition, recent US-based increases in cannabis use coincided with expansions of intensive use, where growing proportions of cannabis consumers report daily/near-daily use; their numbers recently exceeded the number of daily/near-daily drinkers. Some limited – while less consistent – related data indicator measures are available for the neighboring North American country of Canada, the first G-7 country to implement a national cannabis legalization policy in 2018 [2]. For example, in the Canadian Substance Use Survey (CSUS), a conglomerate of iterative national general population surveys (for ages 15+ years) over time, the prevalence of cannabis use (in past 12 months) increased from 9.1% (2011) to 14.8% (2017) pre-legalization, and rose further to 20.7% (2019) and 32.4% (2023) post-legalization [3]. On this basis, the prevalence of cannabis use grew by more than 250%, but rose to more than fourfold (from an estimated 2.4 million to 10.9 million individuals) in total population size during this 13-year period. In comparison, the prevalence of alcohol use (past year) remained generally steady, involving slightly below four-fifths of the population (from 78% in 2011 to 79.3% in 2023) through corresponding years. Notably, for standard indicators of intensive use of either substance, the CSUS found that 25.4% of cannabis users (an estimated 2.8 million individuals) reported daily/near-daily use, whereas 13.4% of alcohol users (3.6 million individuals) reported typically consuming five or more drinks per drinking occasion in 2023 [3-5]. In addition and comprising a somewhat more extensive scope of time, the CAMH Monitor, a longstanding general adult population (18+ years) survey conducted by the Centre for Addiction and Mental Health (CAMH) for Ontario, Canada's most populous province, found the prevalence of cannabis use (in the past 12 months) to be steady at low levels in the pre-2000s (e.g. 8.1% in 1977 and 10.4% in 1999); this then continuously increased through a pre-legalization liberalization period (to 19.4% in 2017) and underwent additional substantial increases following the implementation of legalization (to 25.6% in 2019 and 31.3% in 2023) [6]. Conversely, the prevalence of alcohol use registered some minor elevations in earlier years (e.g. 1984–1995), but-- similar to the national data shown - remained generally steady around the 80% mark throughout and for recent measurements (78.3% in 2023). In 2023, 25.8% of Ontario adults (an estimated 2.97 million individuals) reported any cannabis use, while 8.6% (988 000 individuals) – or a third of self-reported users – reported daily use (in the past 3 months), with indications of multifold increases over the past decade; in comparison, 10.3% (1.18 million individuals) – or about one-in-eight of the self-reported (9.0 million individuals) alcohol consumers – reported daily alcohol use, with only small corresponding changes [6, 7]. Canada-based data indicate evolving cannabis use patterns that are similar to those in the USA, including the overall stark recent increases in use rates, especially during cannabis policy liberalization periods (e.g. medical legalization as implemented from the early-2000s onwards, and non-medical legalization, as implemented in 2018) [1, 8, 9]. Moreover, data indicate relatively large and apparently growing proportions of cannabis consumers involved in intensive and/or high-risk use, as defined by standard measures [4, 5, 7]. While alcohol use far exceeds cannabis use in prevalence, the current numbers of intensive cannabis users may be nearing the total numbers of intensive alcohol users. On this basis, the data presented overall confirm cannabis to be an increasing priority substance for public health-oriented interventions [10-12]. B.F. led the conceptualization of the article; the authors jointly collected and interpreted related data for the study. B.F. led the writing of the article; D.J.A., B.L.F. and D.M. edited and revised the article for substantive intellectual content. All authors approved the final article submitted for publication. None. B.F., D.J.-A. and D.M. have held research grants and/or contracts in the areas of substance use, health, policy from public funding and government organizations (i.e. public-only sources) in the last 5 years. B.F. receives general research support from the Waypoint Centre for Mental Health Care; he acknowledges prior contract-based funding between the Canadian Centre on Substance Use and Addiction (CCSA) and the University of the Fraser Valley, and temporary employment by Health Canada (2021–2022). D.J.-A. acknowledges a clinical scientist career award from Fonds de Recherche du Québec (FRQS); he has received study materials from Cardiol Therapeutics for clinical trials. B.L.F. has obtained research support (e.g. research funding/in-kind support, expert consultancy, other support) from Indivior, Indivia, Canopy Growth Corporation, ThirdBridge, Shinogi, Changemark and Bioprojet; he furthermore acknowledges general research support from the CAMH, the Waypoint Centre for Mental Health Care, a clinician–scientist award from the Dept of Family and Community Medicine and a Chair in Addiction Psychiatry from the Department of Psychiatry, University of Toronto. D.M. is supported by a Canada Research Chair at the University of Ottawa Department of Family Medicine. All data presented in this article are accessible in the public domain (e.g. in the form of journal articles, survey reports, websites). Population estimates presented are either based on those reported by the original data (e.g., survey) source or estimated based on standard/publicly available population statistics.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.364
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.037
GPT teacher head0.310
Teacher spread0.274 · 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.

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
Published2025
Admission routes3
Has abstractyes

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