A Realist Review of How Community-Based Drug Checking Services Could Be Designed and Implemented to Promote Engagement of People Who Use Drugs
Bibliographic record
Abstract
With rising numbers of drug-related deaths in the UK and globally, exploration of interventions that seek to reduce drug-related harm is essential. Drug checking services (DCS) allow people to submit drug samples for chemical analysis and receive feedback about the sample, as well as harm reduction advice. The use of DCS is often linked to festival and/or nightlife settings and to so-called 'recreational' drug use, but research has also shown the potential of community-based DCS as an intervention serving more varied demographics of people who use drugs, including more marginalised individuals and those experiencing drug dependence. Whilst there is a growing evidence base on the effectiveness of drug checking as a harm reduction intervention, there is still limited evidence of the underlying mechanisms and processes within DCS which may aid implementation and subsequent engagement of people who use drugs. This presents a challenge to understanding why engagement differs across types of DCS, and how best to develop and deliver services across different contexts and for different populations. To explore the contexts and mechanisms which impact engagement in community-based DCS, a realist review was undertaken to synthesise the international evidence for the delivery and implementation of DCS. There were 133 sources included in the review. From these sources the underlying contexts, mechanisms, and outcomes relating to DCS implementation and engagement were developed and refined into seven programme theories. The findings of this review are theoretically novel and hold practical relevance for the design of DCS, with implications for optimisation, tailoring, and implementing services to reach individuals in different settings.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".