Effects of cannabis legalization on the use of cannabis and other substances
Bibliographic record
Abstract
PURPOSE OF REVIEW: As more jurisdictions legalize cannabis for non-medical use, the evidence on how legalization policies affect cannabis use and the use of other substances remains inconclusive and contradictory. This review aims to summarize recent research findings on the impact of recreational cannabis legalization (RCL) on cannabis and other substance use among different population groups, such as youth and adults. RECENT FINDINGS: Recent literature reports mixed findings regarding changes in the prevalence of cannabis use after the adoption of RCL. Most studies found no significant association between RCL and changes in cannabis use among youth in European countries, Uruguay, the US, and Canada. However, some studies have reported increases in cannabis use among youth and adults in the US and Canada, although these increases seem to predate RCL. Additionally, there has been a marked increase in unintentional pediatric ingestion of cannabis edibles postlegalization, and an association between RCL and increased alcohol, vaping, and e-cigarette use among adolescents and young adults. SUMMARY: Overall, the effects of cannabis legalization on cannabis use appear to be mixed. Further monitoring and evaluation research is needed to provide longer-term evidence and a more comprehensive understanding of the effects of RCL.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".