Comparing Injecting and Non-Injecting Illicit Opioid Users in a Multisite Canadian Sample (OPICAN Cohort)
Bibliographic record
Abstract
Illicit opioid use in Canada and elsewhere increasingly involves a variety of opioids and non-injection routes of administration. Injection and non-injection opioid users tend to differ in various key characteristics. From a public health perspective, non-injection routes of opioid use tend to be less harmful due to lesser morbidity and mortality risks. Our study compared current injectors (80%) and non-injectors (20%) in a multi-site sample of regular illicit opioid users from across Canada ('OPICAN' study). In bivariate analysis, injectors and non-injectors differed by prevalence in social and health characteristics as well as drug use. Logistic regression analysis identified city, drug use, housing status and mental health problems as independent predictors of injection status. Further analysis revealed that the majority of current non-injectors had an injection history. Our results reinforce the need to explore potential interventions aimed at preventing the transition from non-injectors to injecting, or facilitating the transition of injectors to non-injecting, as initiated in several other contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".