Opportunities and challenges for implementing drug checking services in British Columbia, Canada: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Amidst the ongoing drug poisoning crisis across North America, drug checking services (DCS) are increasingly being implemented as an intervention intended to reduce drug-related harms. This study sought to identify key opportunities and challenges influencing the implementation of DCS in British Columbia (BC), Canada. METHODS: Between January 2020 and July 2021, semi-structured, in-depth interviews were conducted with 21 individuals involved in the implementation of DCS across BC (i.e., policymakers, health authority personnel, community organization representatives and service providers). The Consolidated Framework for Implementation Research (CFIR) was used to guide coding and analysis of the interviews. RESULTS: By bringing in a wealth of knowledge about community needs and concerns, in addition to a passion and energy for social justice and health equity, community members and organizations with a dedication for harm reduction played a critical role in the successful implementation of DCS in BC. Other significant facilitators to implementation included the preventive benefits of DCS that made the intervention compelling to policy influencers and decision makers, the provincial public health emergency regarding overdose that shifted the regulatory environment of DCS, the adaptability of DCS to meet concerns and needs in various contexts, including via ongoing processes of reflection and evaluation. Barriers to implementation included criminalization and stigmatization of drug use and people who use drugs and lack of funding for community-led implementation actions. CONCLUSIONS: Alongside structural reforms that address the underlying contextual factors that influence implementation (e.g., decriminalization of drugs, increased funding for DCS), centering community expertise throughout implementation is critical to the success of DCS. Our findings provide important insights into how BC can successfully implement systems-level harm reduction interventions and offer insights for other jurisdictions in their implementation of DCS.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 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".