Binge drug use among street-involved youth in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: Binge drug use has been associated with increased risk of HIV infection and other serious health-related harms among adult drug user populations. This study sought to determine the prevalence and correlates of binge drug use among street-involved youth in a Canadian setting. METHODS: From Sept 2005 to May 2012, data were collected from the At-Risk Youth Study (ARYS), a prospective cohort of street-involved youth aged 14 - 26 who use illicit drugs. Multivariate generalized estimating equations (GEE) was used to identify factors associated with binge drug use. RESULTS: Of the 987 participants included in this analysis, 41.5% reported binge drug use at baseline, and another 59.1% reported binge drug use at some point during the study. In multivariate GEE analysis, older age (adjusted odds ratio [AOR] = 1.11), homelessness (AOR = 1.67), drug injecting (AOR = 1.63), non-fatal overdose (AOR = 1.98), public injecting (AOR 1.42), being a victim of violence (AOR = 1.38), sex work (AOR = 2.51) and participation in drug dealing (AOR = 2.04) were independently associated with binge drug use in the previous 6 months (all p<0.05). DISCUSSION: The prevalence of reporting binge drug use among the youth was high in this setting and was independently associated with a range of high-risk activities and markers of vulnerability. Querying high-risk youth about binge drug use may help prioritize those in greatest need of addiction treatment strategies and public health 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 | 0.001 | 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".