Elevated risk of incarceration among street-involved youth who initiate drug dealing
Bibliographic record
Abstract
BACKGROUND: Street-involved youth are known to be an economically vulnerable population that commonly resorts to risky activities such as drug dealing to generate income. While incarceration is common among people who use illicit drugs and associated with increased economic vulnerability, interventions among this population remain inadequate. Although previous research has documented the role of incarceration in further entrenching youth in both the criminal justice system and street life, less is known whether recent incarceration predicts initiating drug dealing among vulnerable youth. This study examines the relationship between incarceration and drug dealing initiation among street-involved youth. METHODS: Between September 2005 and November 2014, data were collected through the At-Risk Youth Study, a cohort of street-involved youth who use illicit drugs, in Vancouver, Canada. An extended Cox model with time-dependent variables was used to examine the relationship between recent incarceration and initiation into drug dealing, controlling for relevant confounders. RESULTS: Among 1172 youth enrolled, only 194 (16.6%) were drug dealing naïve at baseline and completed at least one additional study visit to facilitate the assessment of drug dealing initiation. Among this sample, 56 (29%) subsequently initiated drug dealing. In final multivariable Cox regression analysis, recent incarceration was significantly associated with initiating drug dealing (adjusted hazard ratio = 2.31; 95% confidence interval (CI) 1.21-4.42), after adjusting for potential confounders. Measures of recent incarceration lagged to the prior study follow-up were not found to predict initiation of drug dealing (hazard ratio = 1.50; 95% CI 0.66-3.42). CONCLUSIONS: These findings suggest that among this study sample, incarceration does not appear to significantly propel youth to initiate drug dealing. However, the initiation of drug dealing among youth coincides with an increased risk of incarceration and their consequent vulnerability to the significant harms associated therein. Given that existing services tailored to street-involved youth are inadequate, evidence-based interventions should be invested and scaled up as a public health priority.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".