Towards cross-Canada monitoring of the unregulated street drug supply
Bibliographic record
Abstract
BACKGROUND: The well-being of people who use drugs (PWUD) continues to be threatened by substances of unknown type or quantity in the unregulated street drug supply. Current efforts to monitor the drug supply are limited in population reach and comparability. This restricts capacity to identify and develop measures that safeguard the health of PWUD. This study describes the development of a low-barrier system for monitoring the contents of drugs in the unregulated street supply. Early results for pilot sites are presented and compared across regions. METHODS: The drug content monitoring system integrates a low-barrier survey and broad spectrum urine toxicology screening to compare substances expected to be consumed and those actually in the drug supply. The system prototype was developed by harm reduction pilot projects in British Columbia (BC) and Montreal with participation of PWUD. Data were collected from harm reduction supply distribution site clients in BC, Edmonton and Montreal between May 2018-March 2019. Survey and urine toxicology data were linked via anonymous codes and analyzed descriptively by region for trends in self-reported and detected use. RESULTS: The sample consisted of 878 participants from 40 sites across 3 regions. Reported use of substances, their detection, and concordance between the two varied across regions. Methamphetamine use was reported and detected most frequently in BC (reported: 62.8%; detected: 72.2%) and Edmonton (58.3%; 68.8%). In Montreal, high concordance was also observed between reported (74.5%) and detected (86.5%) cocaine/crack use. Among those with fentanyl detected, the percentage of participants who used fentanyl unintentionally ranged from 36.1% in BC, 78.6% in Edmonton and 90.9% in Montreal. CONCLUSIONS: This study is the first to describe a feasible, scalable monitoring system for the unregulated drug supply that can contrast expected and actual drug use and compare trends across regions. The system used principles of flexibility, capacity-building and community participation in its design. Results are well-suited to meet the needs of PWUD and inform the local harm reduction services they rely on. Further standardization of the survey tool and knowledge mobilization is needed to expand the system to new jurisdictions.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".