Accessing and Implementing Community Drug Checking in Smaller Urban Vancouver Island: Contextual Factors to Consider
Bibliographic record
Abstract
The criminalized drug supply in British Columbia and, on a larger scale, in North America is unregulated and leaves those who access the supply to navigate consumption of substances that may be of unknown composition. Drug checking has increasingly been used as a harm reduction measure that provides individuals with greater information about the substances they consume, share, manufacture, and distribute. There is a growing body of evidence related to the acceptability, implementation, service delivery models, and impacts of drug checking. However, much of this research is centered in large urban regions. This follows a trend of inequitable access to harm reduction services within smaller urban centers with a concentration of harm reduction resources and research in large urban regions. This research focuses on the experience of those who will be accessing and implementing drug checking, with specific focus on the context of smaller urban geographic location informs these activities. Data collection tools were informed by the outer context domain of the Consolidated Framework for Implementation Research, to capture experiences related to service implementation and accessibility of drug checking within a smaller urban setting and 39 in-depth interviews were conducted. We identified six core factors related to smaller urban context: community and political climate; lack of anonymity and experiences of stigma; social groups and personal relationships; resource availability; geographic profile; and criminalization. Consideration of these factors in drug checking program development and implementation can support equity-oriented services within smaller urban settings.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".