Inability to access addiction treatment predicts injection initiation among street-involved youth in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: Preventing injection drug use among vulnerable youth is critical for reducing serious drug-related harms. Addiction treatment is one evidence-based intervention to decrease problematic substance use; however, youth frequently report being unable to access treatment services and the impact of this on drug use trajectories remains largely unexplored. This study examines the relationship between being unable to access addiction treatment and injection initiation among street-involved youth. METHODS: Data were derived from the At-Risk Youth Study (ARYS), a prospective cohort of street-involved youth aged 14-26 who use illicit drugs, from September 2005 to May 2014. An extended Cox model with time-dependent variables was used to identify factors independently associated with injection initiation. RESULTS: Among 462 participants who were injection naïve at baseline, 97 (21 %) initiated injection drug use over study follow-up and 129 (28 %) reported trying but being unable to access addiction treatment in the previous 6 months at some point during the study period. The most frequently reported reason for being unable to access treatment was being put on a wait list. In a multivariable Cox regression analysis, being unable to access addiction treatment remained independently associated with a more rapid rate of injection initiation (Adjusted Hazard Ratio =2.02; 95 % Confidence Interval: 1.12-3.62), after adjusting for potential confounders. CONCLUSION: Inability to access addiction treatment was common among our sample and associated with injection initiation. Findings highlight the need for easily accessible, evidence-based addiction treatment for high-risk youth as a means to prevent injection initiation and subsequent serious drug-related harms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".