Planning and implementing community-based drug checking services in Scotland: a qualitative exploration using the consolidated framework for implementation research
Bibliographic record
Abstract
Abstract Background Drug checking services (DCS) provide harm reduction support and advice to individuals based on chemical analysis of submitted substances of concern. Whilst there are currently no DCS in Scotland, community-based services are being planned in three cities. Methods In this paper, we report qualitative findings based on interviews with 43 participants, focused on perceptions of DCS and their implementation. Participants were relevant professionals, those with experience of drug use, and family members of those with experience of drug use. The Consolidated Framework for Implementation Research (CFIR) was used to inform data collection and analysis. We report findings under nine constructs/themes across the five CFIR domains. Results Participants noted the importance of DCS being implemented in low-threshold, trusted services with a harm reduction ethos, and outlined a range of further service design considerations such as speed of testing, and information provided through the analysis process. In relation to the ‘inner setting’, a key finding related to the potential value of leveraging existing resources in order to expand both reach and effectiveness of drug trend communication. The approach of local and national police to DCS, and the attitudes of the public and local community, were described as important external factors which could influence the success (or otherwise) of implementation. Bringing together a range of stakeholders in dialogue and developing tailored communication strategies were seen as ways to build support for DCS. Overall, we found high levels of support and perceived need for DCS amongst all stakeholder groups. Conclusions Our findings present initial implementation considerations for Scotland which could be further explored as DCS are operationalised. Further, our focus on implementation contexts is relevant to research on DCS more generally, given the minimal consideration of such issues in the literature.
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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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".