The Implementation of Drug Checking Services for People Who Use Drugs: A Systematic Review
Bibliographic record
Abstract
THIS PREPRINT HAS BEEN UPDATED AND FORMALLY PUBLISHED IN _ADDICTION_ [https://onlinelibrary.wiley.com/doi/10.1111/add.15734] (HTTPS://DOI.ORG/10.1111/ADD.15734 [https://doi.org/10.1111/add.15734]). A ONE-PAGE FACT SHEET [https://cdpe.org/publication/drug-checking-services-for-people-who-use-drugs-a-systematic-review/] IS ALSO AVAILABLE. BACKGROUND AND AIMS: Drug checking services (DCS) provide people who use drugs (PWUD) with chemical analysis results of their drug samples, while simultaneously monitoring the unregulated drug market. We sought to identify and synthesize literature on the following domains: (a) influence of DCS on behaviour of PWUD; (b) monitoring of drug markets by DCS; and (c) outcomes related to models of DCS. METHODS: This review followed PRISMA guidelines and was pre-registered in PROSPERO (CRD42018105366). A systematic literature search was conducted in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, PsycINFO, Scopus, Web of Science, and Dissertations & Theses Global. Eligible studies were peer-reviewed articles and conference abstracts, or grey literature, published in any language since 1990 and including original data on the domains. We assessed risk of bias for quantitative peer-reviewed articles reporting on behaviour or models of DCS using National Institutes of Health tools. RESULTS: We screened 2,463 titles and abstracts and 156 full-texts, with 90 studies meeting inclusion criteria. Most (n=65, 72.2%) were from Europe and used cross-sectional designs (n=79, 87.7%). Monitoring of drug markets by DCS (n=63, 70%) was most commonly reported, followed by influence of DCS on behaviour (n=31, 34.4%) and outcomes related to models of DCS (n=17, 18.9%). The most common outcome measures were detection of unexpected substances (n=50, 55.6%), expected substances (n=44, 48.9%), new psychoactive substances_ _(n=40, 44.4%), and drugs of concern (n=32, 36.5%) by DCS. CONCLUSIONS: Monitoring of drug markets by DCS is well established in Europe and increasingly in North America. There is an emerging evidence base demonstrating the capacity of DCS to influence behavioural intention, and a smaller subset of findings on its impact on the enacted behaviour of PWUD. Further research is needed on enacted behaviours and corresponding health outcomes including overdose, particularly among people who inject drugs or use opioids.
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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 | 0.023 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".