The Good, The Bad, and The Uncertain: A Systematic Review of the Impacts of Recreational Cannabis Legalization
Bibliographic record
Abstract
Background: Recreational cannabis legalization has become more prevalent over the past decade, with recreational cannabis now legal in 13 U.S. states and nationally in Uruguay, Canada, and Malta. At the same time, there is evidence for increasing rates of cannabis use and consequences from use (i.e., crime and motor vehicle accidents) among some age groups, particularly in North America. Thus, it is crucial to understand the role of recreational legalization on person-level outcomes, such as cannabis consumption and health-outcomes. Previous reviews have focused on outcomes in the U.S., but there remains a need for a broader international evaluation of the effect of legalization. Additionally, there is a dearth of reviews examining recreational cannabis legalization as past literature has focused more attention on medical cannabis legalization. The current review examined the role of recreational cannabis legalization on a variety of person-centered outcomes to understand if the consequences of legalization. Method: A comprehensive systematic review was conducted in accordance with PRISMA guidelines. Inclusion criteria involved: empirical quantitative research, article available in English, a research design evaluating the impact of recreational cannabis legalization (e.g., pre vs. post), and person-level cannabis use, and/or clinical outcomes included as dependent variables (opposed to outcomes such as changes in the price or potency of cannabis). Two individuals independently coded studies for eligibility and extracted information. The search revealed 69 studies that met criteria for inclusion. Results: Our search revealed five main categories of outcomes associated with recreational cannabis legalization: cannabis and substance use behaviors, health-care impacts, attitudes surrounding cannabis, crime-related outcomes, and driving-related outcomes. The studies were predominantly cross-sectional designs and most took place within the U.S. The extant literature revealed inconsistencies surrounding the role of recreational legalization. In the case of cannabis use behaviours, there was mixed evidence for different subpopulations. For example, legalization was associated with increased cannabis use for college samples, but there were inconsistent findings for adolescents. Additionally, some studies pointed towards negative consequences associated with legalization, such as increased cannabis use in clinical populations and increased cannabis-related healthcare visits. Other studies suggested no impact of legalization through either no change in examined outcomes or heterogenous conclusions (e.g., cannabis-related attitudes). Finally, there was also evidence of positive consequences for recreational legalization, such as decreased drug-related arrests and cigarette use. Conclusions: Overall, the balance of research suggests there is no clear support for any large magnitude person-level impact of recreational cannabis legalization. This review indicates that more conclusive evidence regarding the impact of recreational legalization is still required and reveals a need for more longitudinal study designs with longer-term follow-ups as well as studies from a broader range of geographic jurisdictions. As recreational cannabis legalization becomes implemented globally, a systematic evaluation of the outcomes associated with enactment is of increasing relevance.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".