A qualitative exploration of prescription opioid injection among street-based drug users in Toronto: behaviours, preferences and drug availability
Bibliographic record
Abstract
BACKGROUND: There is evidence of a high prevalence of prescription opioid (PO) and crack use among street drug users in Toronto. The purpose of this qualitative study was to describe drug use behaviours and preferences as well as the social and environmental context surrounding the use of these drugs among young and old street-based drug injection drug users (IDUs). METHODS: In-depth interviews were conducted with 25 PO injectors. Topics covered included drug use history, types of drugs used, how drugs were purchased and transitions to PO use. Interviews were taped and transcribed. Content analysis was conducted to identify themes. RESULTS: Five prominent themes emerged from the interviews: 1) Combination of crack and prescription opioids, 2) First injection experience and transition to prescription opioids, 3) Drug preferences and availability, 4) Housing and income and 5) Obtaining drugs. There was consensus that OxyContin and crack were the most commonly available drugs on the streets of Toronto. Drug use preferences and behaviours were influenced by the availability of drugs, the desired effect, ease of administration and expectations around the purity of the drugs. Distinct experiences were observed among younger users as compared to older users. In particular, the initiation of injection drug use and experimentation with POs among younger users was influenced by their experiences on the street, their peers and general curiosity. CONCLUSION: Given the current profile of street-based drug market in Toronto and the emergence of crack and POs as two predominant illicit drug groups, understanding drug use patterns and socio-economic factors among younger and older users in this population has important implications for preventive and therapeutic interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".