Exploring Drug Sourcing among Regular Prescription Opioid Users in Canada: Data from Toronto and Victoria
Bibliographic record
Abstract
Recent North American data document increased prescription opioid (PO) misuse in general and street-drug-user populations. One aspect of this phenomenon – as distinct from illicit drug use – appears to be sourcing, since POs may be obtained through various forms of diversion from the medical system and other sources. However, the overall function of street-drug markets for POs remains unclear. Regular street users of POs in Toronto (N = 43) and Victoria (N = 39) were recruited by community-based methods and completed an interviewer-administered questionnaire exploring features of PO sourcing from street-drug markets. Respondents were PO- and non-opioid poly-drug users, with few holding their own prescription for POs. Regular sources for POs were more common in Toronto. Sizeable proportions of respondents in both sites reported exchanging and selling illicit drugs, involving both PO and non-PO drugs in Toronto, yet mainly restricted to the latter in Victoria. Respondents suggested a possible demarcation line between street market sources for POs and street market sources for illicit drugs. The availability of, demand for, and prices for PO-drugs was observed to have increased in recent years. Street-drug markets appear to be one key source feeding increasing levels of PO use among street users. Our data suggest that there may be distinct market patterns for POs, findings which are important for developing interventions and future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".