Injection drug use among street-involved youth in a Canadian setting
Bibliographic record
Abstract
Background: Street-involved youth contend with an array of health and social challenges, including elevated rates of blood-borne infections and mortality. In addition, there has been growing concern regarding high-risk drug use among street-involved youth, in particular injection drug use. We undertook this study to examine the prevalence of injection drug use and associated risks among street-involved youth in Vancouver, Canada. Methods: From September 2005 to November 2007, baseline data were collected for the At-Risk Youth Study (ARYS), a prospective cohort of street-recruited youth aged 14 to 26 in Vancouver, Canada. Using multiple logistic regression, we compared youth with and without a history of injection. Results: The sample included 560 youth among whom the median age was 21.9 years, 179 (32%) were female, and 230 (41.1%) reported prior injection drug use. Factors associated with injection drug use in multivariate analyses included age ≥ 22 years (adjusted odds ratio [AOR] = 1.18, 95% CI: 1.10–1.28); sex work involvement (AOR = 2.17, 95% CI: 1.35–3.50); non-fatal overdose (AOR = 2.10, 95% CI: 1.38–3.20); and hepatitis C (HCV) infection (AOR = 22.61, 95% CI: 7.78–65.70). Conclusion: These findings highlight an alarmingly high prevalence of injection drug use among street-involved youth and demonstrate its association with an array of risks and harms, including sex work involvement, overdose, and HCV infection. These findings point to the need for a broad set of policies and interventions to prevent the initiation of injection drug use and address the risks faced by street-involved youth who are actively injecting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".