What is needed for implementing drug checking services in the context of the overdose crisis? A qualitative study to explore perspectives of potential service users
Bibliographic record
Abstract
BACKGROUND: The current illicit drug overdose crisis within North America and other countries requires expanded and new responses to address unpredictable and potentially lethal substances, including fentanyl analogues, in the unregulated drug market. Community-wide drug checking is being increasingly explored as one such public health response. We explored how drug checking could be implemented as a potential harm reduction response to the overdose crisis, from the perspective of potential service users. METHODS: The research was guided by the Consolidated Framework for Implementation Research (CFIR). We conducted a qualitative, pre-implementation study to inform development and implementation of drug checking services that are acceptable to people who use substances and meet their needs. University and community researchers conducted 27 in-depth interviews with potential service users at prospective drug checking sites. We inductively developed emerging themes to inform the implementation of drug checking services within the five domains of the CFIR, and identified the most relevant constructs. RESULTS: Implementing community drug checking faces significant challenges within the current context of criminalization and stigmatization of substance use and people who use/sell drugs, and trauma experienced by potential service users. Participants identified significant risks in accessing drug checking, and that confidential and anonymous services are critical to address these. Engaging people with lived experience in the service can help establish trust. The relative advantage of drug checking needs to outweigh risks through provision of accurate results conveyed in a respectful, non-judgemental way. Drug checking should provide knowledge relevant to using and/or selling drugs and informing one's own harm reduction. CONCLUSIONS: For service users, the extent to which the implementation of drug checking can respond to and mitigate the risks of being criminalized and stigmatized is critical to the acceptability and success of community drug checking. The culture and compatibility of the service, setting and staff with harm reduction principles and practices is essential.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".