Investigating the Influence of Drug Checking Services on People Who Use Drugs
Bibliographic record
Abstract
Background: Against the backdrop of a nationwide drug toxicity and overdose crisis, drug checking services (DCS) – including Toronto’s Drug Checking Service (T-DCS) – have emerged as part of a comprehensive approach to preventing overdose mortality in Canada. DCS provide people who use drugs (PWUD) with chemical analysis results of their drug samples while simultaneously monitoring the unregulated drug market. This thesis project investigates the impacts of DCS on behaviours and risks experienced by PWUD in Toronto, Ontario. Methods: A systematic review synthesized literature on: (a) the influence of DCS on the behaviour of PWUD; (b) monitoring of drug markets by DCS; and (c) outcomes related to models of DCS. A dual phase qualitative approach explored the perceptions of PWUD (N=24) on facilitators, barriers, and outcomes of T-DCS, as well as of interest-holders (N=16) of T-DCS on its applications and improvements. Reflexive thematic analysis was undertaken to explore patterns across the datasets. Results: Inclusive of 90 studies, the systematic review found monitoring of drug markets by DCS (n=63, 70%) was most reported, followed by the influence of DCS on behaviour (n=31, 34.4%). Among PWUD, facilitators to T-DCS included receiving and sharing information about drug contents, responding to unexpected drug effects and appearances, as well as informing drug consumption and drug market engagement. Outcomes from service use included sharing information, seeking additional information and services, changing use of substances, and altering drug market engagement. Among interest-holders, applications of information from T-DCS included educating others, advocating for policy changes, developing policies, and altering clinical practice. Barriers and improvements related to technology used for sample analysis, service capacity, outputs, and processes, and drug criminalization. Conclusions: DCS influence the behaviour of PWUD, particularly when results are unexpected or drugs of concern. Facilitators and outcomes among PWUD, as well as applications of T-DCS among interest-holders, were often driven by motivations to improve health and related outcomes for PWUD and broader communities in the context of an unpredictable and toxic unregulated drug supply. Findings can be informative in optimizing T-DCS and as DCS increasingly play a role within a comprehensive approach to overdose prevention in Canada and elsewhere.
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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 | 0.028 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".