Comparing heroin users and prescription opioid users in a Canadian multi‐site population of illicit opioid users
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Recent data suggest increasing prescription opioid and decreasing heroin use among street drug users, yet little is known on possible differential use characteristics and outcomes associated with these drugs. [While we recognise that, correctly, these populations would need to be labelled as opioid 'abusers' or 'non-medical users', we rely on the simpler terms 'use' and 'users' for the population under study within the wider context of them being engaged overall in illicit opioid use activities.] This study compared drug use, health, and socio-economic characteristics between heroin (H)-only, prescription opioid (PO)-only and mixed heroin and prescription (PO & H) users in a Canadian multi-site cohort of illicit opioid and other drug users (OPICAN). DESIGN AND METHODS: Data from the most recent (2005) multi-component assessment of the H-only (n = 94), PO-only (n = 304) and PO & H (n = 86) cohort sub-samples were analysed. Based on bivariate analyses of variables of interest, a multinomial logistic regression analysis (MLRA) model was computed, comparing PO-only and PO & H groups to the H-only reference group, respectively. RESULTS: H-only users were found in two of the seven study sites. Based on the MLRA, PO-only and PO & H users, compared to H-only users, were more likely to: be older, use benzodiazepines and cocaine, use drop-in shelters and less likely to use walk-in clinics. PO-only users were also more likely to: be white; receive legal income; use drugs by non-injection; have physical health problems; and use private physician services. DISCUSSION AND CONCLUSIONS: Our study underscores the increasing prevalence of PO compared to heroin use in the study population. Differences between PO-only and H-only users were more pronounced than differences between PO-only and PO & H users. PO-only use may be associated with lowered health risks and social burdens, yet concerns regarding polysubstance use and drug sourcing arise. Challenges for targeted interventions are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".