Reorienting risk to resilience: street-involved youth perspectives on preventing the transition to injection drug use
Bibliographic record
Abstract
BACKGROUND: The Youth Injection Prevention (YIP) project aimed to identify factors associated with the prevention of transitioning to injection drug use (IDU) among street-involved youth (youth who had spent at least 3 consecutive nights without a fixed address or without their parents/caregivers in the previous six months) aged 16-24 years in Metro Vancouver, British Columbia. METHODS: Ten focus groups were conducted by youth collaborators (peer-researchers) with street-involved youth (n = 47) from November 2009-April 2010. Audio recordings and focus group observational notes were transcribed verbatim and emergent themes identified by open coding and categorizing. RESULTS: Through ongoing data analysis we identified that youth produced risk and deficiency rather than resiliency-based answers. This enabled the questioning guide to be reframed into a strengths-based guide in a timely manner. Factors youth identified that prevented them from IDU initiation were grouped into three domains loosely derived from the risk environment framework: Individual (fear and self-worth), Social Environment (stigma and group norms - including street-entrenched adults who actively discouraged youth from IDU, support/inclusion, family/friend drug use and responsibilities), and Physical/Economic Environment (safe/engaging spaces). Engaging youth collaborators in the research ensured relevance and validity of the study. CONCLUSION: Participants emphasized having personal goals and ties to social networks, supportive family and role models, and the need for safe and stable housing as key to resiliency. Gaining the perspectives of street-involved youth on factors that prevent IDU provides a complementary perspective to risk-based studies and encourages strength-based approaches for coaching and care of at-risk youth and upon which prevention programs should be built.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".