Impact evaluations of drug decriminalisation and legal regulation on drug use, health and social harms: a systematic review
Bibliographic record
Abstract
OBJECTIVES: To review the metrics and findings of studies evaluating effects of drug decriminalisation or legal regulation on drug availability, use or related health and social harms globally. DESIGN: Systematic review with narrative synthesis. DATA SOURCES: We searched MEDLINE, Embase, PsycINFO, Web of Science and six additional databases for publications from 1 January 1970 through 4 October 2018. INCLUSION CRITERIA: drug decriminalisation or legal regulation. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers screened titles, abstracts and articles for inclusion. Extraction and quality appraisal (modified Downs and Black checklist) were performed by one reviewer and checked by a second, with discrepancies resolved by a third. We coded study-level outcome measures into metric groupings and categorised the estimated direction of association between the legal change and outcomes of interest. RESULTS: We screened 4860 titles and 221 full-texts and included 114 articles. Most (n=104, 91.2%) were from the USA, evaluated cannabis reform (n=109, 95.6%) and focussed on legal regulation (n=96, 84.2%). 224 study outcome measures were categorised into 32 metrics, most commonly prevalence (39.5% of studies), frequency (14.0%) or perceived harmfulness (10.5%) of use of the decriminalised or regulated drug; or use of tobacco, alcohol or other drugs (12.3%). Across all substance use metrics, legal reform was most often not associated with changes in use. CONCLUSIONS: Studies evaluating drug decriminalisation and legal regulation are concentrated in the USA and on cannabis legalisation. Despite the range of outcomes potentially impacted by drug law reform, extant research is narrowly focussed, with a particular emphasis on the prevalence of use. Metrics in drug law reform evaluations require improved alignment with relevant health and social outcomes.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".