An overview of select cannabis use and supply indicators pre- and post-legalization in Canada
Bibliographic record
Abstract
BACKGROUND: Canada implemented the legalization and regulation of non-medical cannabis use, production and sale in 2018 aiming to improve public health and safety. While outcomes from legalization reforms in other jurisdictions mostly rely on US-based data have been assessed to be mixed, Canadian data are only emerging. We compiled select population-level data on key indicators to gauge initial developments from pre- to post-legalization of cannabis in Canada. METHODS: We examined indicators data focusing on the following topics: prevalence of cannabis use, frequency of use, methods/products of consumption, driving after cannabis use, and cannabis sourcing. Indicator data were obtained mostly from national and some provincial population surveys. Prevalence or percentages for the indicators pre- and post-legalization (e.g., 2017- 2020), including confidence intervals were reported, with changes noted, as available in and indicated by the data sources. RESULTS: Data suggested selected increases in cannabis use prevalence, mostly among mid- and older- but possibly also younger (e.g., under legal use age) users. Frequency of use and driving after cannabis use among active users do not appear to have changed. Methods of cannabis use show diversifying trends, with decreases in smoking and increases in alternatives use modes (e.g., edibles, vaping). There is a clearly increasing trend towards accessing cannabis from legal sources among adults, while under-legal-use-age youth do not appear to experience heightened barriers to obtaining cannabis in legalization contexts. CONCLUSIONS: Preliminary indicators on cannabis legalization in Canada show a mixed picture, some similar to US-based developments. While some use increases are observed, these do not necessarily represent indications of increases in cannabis-related harm, also since key (e.g., hospitalization or injury) data are lacking to date. There is a gradual embracing of legal supply sources of cannabis among users, which can be expected to serve public health and safety objectives. At the same time, cannabis use and access among under-age users as a principally vulnerable group do not appear to be hindered or reduced by legalization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.035 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".