How have cannabis use and related indicators changed since legalization of cannabis for non-medical purposes? Results of the Canadian Cannabis Survey 2018–2022
Bibliographic record
Abstract
BACKGROUND: Cannabis use for non-medical purposes was legalized and regulated in Canada through the Cannabis Act in October 2018. This paper examined trends in use of cannabis for non-medical purposes and related indicators from pre- to post-legalization in Canada (2018-2022). METHODS: =10,048). Cannabis measures include questions about use, types of products, sources, risk perceptions and beliefs, and exposure to public education campaigns and health warnings. Adjusted logistic regression models tested differences in outcomes over time. RESULTS: Past 12-month cannabis consumption increased among Canadians from 22 % in 2018 to 27 % in 2022 (AOR=1.41;99 % CI:1.28-1.54). Similarly, daily/almost daily (DAD) consumption increased from 5 % in 2018 to 7 % in 2022 (AOR=1.36;99 % CI:1.16-1.59). Consumption of dried flower, hash/kief, and concentrates/extracts (e.g., wax, shatter, budder) decreased since 2018, whereas consumption of edibles, beverages and vape pens/cartridges increased (p < 0.001). Legal purchasing increased from 4 % in 2018 to 69 % in 2022, while accessing cannabis through social and illegal sources decreased over time (p < 0.001). CONCLUSION: More Canadians are reporting cannabis consumption since legalization and regulation of cannabis for non-medical purposes, continuing a pre-existing trend despite an increase in awareness of the risks of consuming cannabis. Trends in product use indicate a transition from dried flower and concentrates/extracts towards consumption of cannabis foods, drinks and vape pens/cartridges. The legal market is increasingly displacing the illicit cannabis market in Canada.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".