The impact of cannabis legalization for recreational purposes: The Canadian experience
Bibliographic record
Abstract
Abstract Cannabis Legalization for Recreational Purposes took place in Canada in October 2018. One of the federal government’s stated goals with this legalization was to protect Canadian youth from cannabis-related harms. The Canadian model differs from other jurisdictions that legalized recreational cannabis use, especially with regard to a higher degree of government regulation of the cannabis market. Another difference is the development and endorsement of lower-risk cannabis use guidelines to educate the public and health professionals. Here, we will present the changes in the regulation of the Canadian cannabis market. We will also present some changes in the epidemiology and parameters of cannabis use (modes of use, potency of cannabis) among adults and youths. Although it is clear that prevalence of use has increased in some groups (notably older adults), results for youth are mixed, with the majority of studies showing no pronounced increase. A trend of a decrease in youth cannabis use seen pre-legalization may have reversed. Data about changes in the age of initiation, the influence of legalization on sex and gender, and race/ ethnicity are limited, with evidence suggesting that the age of initiation slightly increased and the prevalence of use has become more similar between females and males. The development and utility of the lower-risk cannabis use guidelines will be also presented. Disclosure of Interest B. Le Foll Grant / Research support from: . Dr. Le Foll has in-kind donations of cannabis products from Aurora Cannabis Enterprises Inc. . Dr. Le Foll has obtained industry funding from Canopy Growth Corporation (through research grants handled by the Centre for Addiction and Mental Health and the University of Toronto)
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".