“Everyone says ‘safe sex’ but no one ever says ‘safe drugs’, you know?”: Perspectives on the intersection of drug checking services, drug policy and the overdose crisis
Bibliographic record
Abstract
BACKGROUND: Overdose deaths have continued to rise in the US despite heightened public attention and resources. Drug checking shows promise for integration into existing services for people who use drugs (PWUD) across North America. Amidst the backdrop of rising overdose deaths and emerging funds for harm reduction initiatives, this manuscript explores the landscape of drug checking services in North America and perspectives on improved integration with a diverse set of PWUD based in San Francisco and North American drug checking experts. METHODS: Two separate samples of drug checking stakeholders, 'providers' and 'clients' were recruited. Providers participated in in-depth semi-structured qualitative interviews over Zoom on their experiences advocating for and operating drug checking services in the US and Canada. Clients were people who used drugs and lived in or commuted to the San Francisco Bay Area and participated in semi-structured interviews in November 2022. Interviews were transcribed fully and analyzed using thematic analysis methods. RESULTS: Providers and clients identified ongoing instability in the North American drug supply that is exacerbating overdose risk while also identifying groups that would benefit from greater access to drug checking services. Both groups believed the paradoxical impacts of the fentanyl crisis at the core of drug checking services created barriers to the implementation and expansion of these services, hurting PWUD and their providers. Additionally, clients and providers reflected on the social and policy challenges to expansion and improvement of drug checking in their communities. CONCLUSION: Drug checking remains underleveraged, particularly with respect to the most vulnerable PWUD. Clients and providers contended that these services must become more responsive to an ever changing and dangerous drug supply in North America.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.033 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.012 |
| 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".