Risks Surrounding Drug Trade Involvement Among Street-Involved Youth
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Street-involved youth have been shown to be involved in the street-level illicit drug trade in a number of jurisdictions, though little is known about risk factors and sequelae of this behavior. The present study was therefore conducted to investigate factors associated with the street-level drug trade involvement among street-based youth. METHODS: We used logistic regression to examine factors associated with drug dealing among participants in the At-Risk Youth Study in Vancouver, Canada. We also examined motivations for drug trade involvement and types of drugs sold by participants. RESULTS: Overall, 529 street-involved youth were followed during the study period, of whom 307 (58.0%) reported having been involved in the drug trade in the last six months. In a logistic regression analysis, crack cocaine use (Adjusted Odds Ratio [AOR] = 1.84, 95% CI: 1.28-2.67), homelessness (AOR = 1.58, 95% CI: 1.04-2.40), and self-reported police assault [corrected] (AOR = 1.85, 95% CI: 1.14-3.00) were independently associated with drug dealing among cohort participants. Among participants who reported drug dealing, 263 (85.6%) individuals stated that the main reason that they sold drugs was to pay for their personal drug use. CONCLUSIONS: In our setting, street-involved youth implicated in the drug trade are characterized by drug-related and sociodemographic vulnerabilities. These individuals also appear to be motivated by drug dependence and report elevated levels of physical confrontation with police [corrected]. Our findings have immediate implications for drug strategies targeting street-level drug dealing.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it