Effects of Recreational Cannabis Legalization on Mental Health: Scoping Review
Bibliographic record
Abstract
OBJECTIVE: Recreational cannabis legalization (RCL) is expanding rapidly. RCL's effects on mental health issues are of particular concern because cannabis use is more frequent among people receiving psychiatric care and is associated with several psychiatric disorders. The authors conducted a scoping review to examine the evidence and discern gaps in the literature concerning the effects of RCL on mental health and to assess the factors responsible for an observed heterogeneity in research results. METHODS: This scoping literature review followed PRISMA guidelines. Five databases-MEDLINE, CINAHL, Embase, APA PsycInfo, and Web of Science-were searched for English- or French-language reports published between January 1, 2012, and April 30, 2023. RESULTS: Twenty-eight studies from the United States and Canada were found. The studies were classified by category of the study's data (patients receiving psychiatric care [k=1], death records [k=4], emergency department or hospital records [k=10], and the general population [k=13]) and by the diagnosis (schizophrenia or psychoses, mood disorders, anxiety disorders and symptoms, suicide or suicidal ideation, or other mental health issues) examined. The review findings revealed a paucity of research and indicated mixed and largely inconclusive results of the studies examined. Research gaps were found in the examination of potential changes in cannabis use patterns among people receiving psychiatric care and in the availability of longitudinal studies. CONCLUSIONS: Clinicians, researchers, and policy makers need to collaborate to address the research gaps and to develop evidence-based policies that reflect a thorough understanding of the effects associated with RCL.
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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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".