Incarceration Among Street-Involved Youth in a Canadian Study : Implications for Health and Policy Interventions
Bibliographic record
Abstract
Background Risk factors for incarceration have been well described among adult drug using populations; however, less is known about incarceration among at-risk youth. This study examines the prevalence and correlates of incarceration among street-involved youth in a Canadian setting. Methods From September 2005 to May 2012, data were collected from the At-Risk Youth Study, a prospective cohort of street-involved youth aged 14 – 26 who use illicit drugs. Generalized estimating equation (GEE) logistic regression was used to identify factors associated with recent incarceration defined as incarceration in the previous six months. Results Among 1019 participants, 362 (36%) reported having been recently incarcerated during the study period. In multivariate GEE analysis, homelessness (adjusted odds ratio [AOR]= 1.60), daily crystal methamphetamine use (AOR= 1.56), public injecting (AOR= 1.33), drug dealing (AOR= 1.48) and being a victim of violence (AOR= 1.68) were independently associated with incarceration (all p <0.05). Conversely, female gender (AOR= 0.48), lesbian, gay, bisexual, transgender or two-spirited (LGBTT) identification (AOR= 0.47) and increasing age of first hard drug use (AOR= 0.96) were negatively associated with incarceration (all p <0.05). Conclusion Incarceration was common among our study sample. Youth who were homeless, used crystal methamphetamine, and engaged in risky behaviors including public injection and drug dealing were significantly more likely to have been recently incarcerated. Structural interventions including expanding addiction treatment and supportive housing for at-risk youth may help reduce criminal justice involvement among this population and associated health, social and fiscal costs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".