Drug checking services for people who use drugs: a systematic review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Drug checking services provide people who use drugs 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) the influence of drug checking services on the behaviour of people who use drugs; (b) monitoring of drug markets by drug checking services; and (c) outcomes related to models of drug checking services. METHODS: Systematic review. 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 and 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 drug checking services using National Institutes of Health tools. RESULTS: We screened 2463 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 drug checking services (n = 63, 70%) was the most reported domain, followed by the influence of drug checking services on behaviour (n = 31, 34.4%), including intent to use, actual use and disposal of the drug, and outcomes related to models of drug checking services (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 drug checking services. CONCLUSIONS: Drug checking services appear to influence behavioural intentions and the behaviour of people who use drugs, particularly when results from drug checking services are unexpected or drugs of concern. Monitoring of drug markets by drug checking services is well established in Europe, and increasingly in North America. Concerns about drug contents and negative health consequences facilitate the use of drug checking services; lack of concern; trust in drug sellers; lack of accessibility of drug checking services; and legal and privacy concerns are barriers to use.
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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".