Exploring the impact of drug decriminalization and legalization policies on mental health outcomes: A scoping review
Bibliographic record
Abstract
As countries increasingly adopt more liberal drug policies, concerns have emerged about their broader health and social impacts. A complex bidirectional relationship exists between problematic drug use and mental health conditions. This is particularly evident in the co-occurrence of mental health disorders with substance use disorders (SUDs). However, the broader mental health effects of drug policy remain underexplored. This review aims to map existing research on non-SUD mental health outcomes associated with drug decriminalization and legalization policies. We conducted a scoping review following JBI guidelines and the PRISMA-ScR checklist. Studies published between January 2001 and December 2024 were included if they examined non-SUD mental health outcomes related to drug policy changes, with a focus on decriminalization, legalization, or commercialization. We searched Medline, EMBASE, CINAHL, PsycInfo, and Web of Science, and manually screened relevant policy reports. Only English-language studies were included. Data extraction and analysis were conducted using Covidence, with a descriptive summary of study characteristics and findings. A total of 55 studies met inclusion criteria, comprising 16 review papers and 39 original research articles (37 quantitative and 2 qualitative). Most evidence came from the United States (n = 29) and Canada (n = 18). No studies examined the mental health impacts of non-cannabis drug policies or decriminalization frameworks. The most frequently assessed outcomes were psychosis, suicide, and depression. This review maps the current evidence base and identifies major gaps, especially concerning decriminalization and policies targeting substances other than cannabis. The heterogeneity in study designs and policy contexts highlights the need for multi-faceted, context-sensitive research to inform future policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".