Risks Surrounding Drug Trade Involvement Among Street-Involved Youth
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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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".