'It’s not going to be a one size fits all': a qualitative exploration of the potential utility of three drug checking service models in Scotland
Bibliographic record
Abstract
BACKGROUND: Scotland currently has the highest rates of drug-related deaths in Europe, so drug checking services are being explored due to their potential role in reducing these deaths and related harms. Drug checking services allow individuals to submit presumed psychoactive drug samples for analysis, and then receive individualised feedback and counselling. This paper explores participants' views on the advantages and challenges of three hypothetical service models, to inform future service delivery in Scotland. METHODS: Semi-structured interviews were conducted with 43 people: 27 professional stakeholders, 11 people with experience of drug use, and five family members across three cities. Vignettes were used to provide short descriptions of three hypothetical service models during the interviews. Interviews were audio-recorded, transcribed and analysed using thematic analysis. RESULTS: Participants identified advantages and challenges for each of the three potential service models. The third sector (not-for-profit) model was favoured overall by participants, and the NHS substance use treatment service was the least popular. Participants also noted that multiple drug checking sites within one city, along with outreach models would be advantageous, to meet the diverse needs of different groups of people who use drugs. CONCLUSIONS: Drug checking services need to be tailored to local context and needs, with a range of service models being possible, in order to meet the needs of a heterogeneous group of people who use drugs. Addressing issues around stigma, accessibility, and concerns about the potential impact of accessing drug checking on access to and outcomes of drug treatment, are essential for successful service delivery.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".