Increases and decreases in drug use attributed to housing status among street-involved youth in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: Among a cohort of drug-using street-involved youth, we sought to identify the prevalence of reporting increases and decreases in illicit drug use due to their current housing status and to identify factors associated with reporting these changes. FINDINGS: This longitudinal study was based on data collected between June 2008 and May 2012 from a prospective cohort of street-involved youth aged 14-26 in Vancouver, Canada. At semi-annual study follow-up visits, youth were asked if their drug use was affected by their housing status. Using generalized estimating equations, we identified factors associated with perceived increases and decreases in drug use attributed to housing status. Among our sample of 536 participants at baseline, 164 (31%) youth reported increasing their drug use due to their housing situation and 71 (13%) reported decreasing their drug use. In multivariate analysis, factors that were positively associated with perceived increases in drug use attributed to housing status included the following: being homeless, engaging in sex work and drug dealing. Regular employment was negatively associated with increasing drug use due to housing status. Among those who reported decreasing their drug use, only homelessness was significant in bivariate analysis. CONCLUSION: Perceived changes in drug use due to housing status were relatively common in this setting and were associated with being homeless and, among those who increased their drug use, engaging in risky income generation activities. These findings suggest that structural factors, particularly housing and economic opportunities, may be crucial interventions for reducing or limiting drug use among street-involved youth.
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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 | 0.003 |
| Science and technology studies | 0.003 | 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".