Implementation opportunities and challenges to piloting a community-based drug-checking intervention for sexual and gender minority men in Vancouver, Canada: a qualitative study
Bibliographic record
Abstract
Abstract Background In response to the overdose crisis, a collaborative group of two community-based organizations, a health authority and a research institute in Vancouver, Canada, implemented a pilot community-based drug checking (CBDC) intervention for sexual and gender minority (SGM) men. This study identified key factors that influenced the implementation of the CBDC intervention, including opportunities and challenges. Methods We conducted semi-structured interviews with seven pertinent parties involved in the CBDC, including policymakers, researchers and representatives from community-based organizations. These interviews were coded and analyzed using domains and constructs of the Consolidated Framework for Implementation Research. Results While drug-related stigma was identified as a challenge to deliver drug checking services, participants described the context of the overdose crisis as a key facilitator to engage collaboration between relevant organizations (e.g., health authorities, medical health officers, community organizations) to design, resource and implement the CBDC intervention. The implementation of the CBDC intervention was also influenced by SGM-specific needs and resources (e.g., lack of information about the drug supply). The high level of interest of SGM organizations in providing harm reduction services combined with the need to expand drug checking into community spaces represented two key opportunities for the CBDC intervention. Here, SGM organizations were recognized as valued partners that fostered a broader culture of harm reduction. Participants’ emphasis that knowing the composition of one’s drugs is a “right to know”, particularly in the context of a highly contaminated illicit drug market, emerged as a key implementation factor. Lastly, participants emphasized the importance of involving SGM community groups at all stages of the implementation process to ensure that the CBDC intervention is appropriately tailored to SGM men. Conclusions The context of the overdose crisis and the involvement of SGM organizations were key facilitators to the implementation of a drug checking intervention in SGM community spaces. This study offers contextualized understandings about how SGM knowledge and experiences can contribute to implement tailored drug checking interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".