Employment and risk of injection drug use initiation among street involved youth in Canadian setting
Bibliographic record
Abstract
Objective Youth unemployment has been associated with labour market and health disparities. However, employment as a determinant of high-risk health behaviour among marginalized young people has not been well described. We sought to assess a potential relationship between employment status and initiation of intravenous drug use among a prospective cohort of street-involved youth. Method We followed injecting naïve youth in the At-Risk Youth Study, a cohort of street-involved youth aged 14-26 in Vancouver, Canada, and employed Cox regression analyses to examine whether employment was associated with injection initiation. Results Among 422 injecting naïve youth recruited between September 2005 and November 2011, 77 participants transitioned from non-injection to injection drug use, for an incidence density of 10.3 (95% confidence interval [CI]: 8.0-12.6) per 100 person years. Results demonstrating that employment was inversely associated with injection initiation (adjusted hazard ratio: 0.53; 95% CI: 0.33-0.85) were robust to adjustment for a range of potential confounders. Conclusion A lack of employment among street-involved youth was associated with the initiation of injection drug use, a practice that predisposes individuals to serious long-term health consequences. Future research should examine if reducing barriers to labour market involvement among street-involved youth prevents transitions into high-risk drug use.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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".