Impacts de la légalisation du cannabis récréatif sur la santé mentale : une recension des écrits
Bibliographic record
Abstract
Objectives To review the known impacts of recreational cannabis legalization on mental health and substance use, in the context of recent changes in the status of cannabis laws in Canada. Methods PubMed database was systematically searched using various terms regarding mental health and cannabis legalization. Two independent investigators then assessed a total of 272 titles and abstracts and 11 articles were ultimately found eligible for review. Results Most studies measuring the impact of legalization on cannabis use showed an increase in cannabis use after the legalization. Moreover, no study demonstrated a reduction of cannabis use after legalization of recreational cannabis. All three studies regarding health care contacts demonstrated an increase in the number of cannabis-related emergency department visits after cannabis legalization. Two studies revealed reduction of perceived risks associated with cannabis after legalization, while another study offered opposite results. To interpret these observational results accurately, we also need to consider the long-term trends that prevailed before the changes in cannabis laws. Conclusions The quantity of evidence on the impacts of recreational cannabis legalization on mental health and substance use is limited. Further research is needed to strengthen these results and explore the effects of cannabis legalization on other mental health issues such as psychosis.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".