The relationship between violence and engagement in drug dealing and sex work among street-involved youth
Bibliographic record
Abstract
OBJECTIVES—Street-involved youth are highly vulnerable to violence. While involvement in income-generating activities within illicit drug scenes are recognized as shaping youths’ vulnerability to violence, the relative contributions of different income-generating activities remain understudied. We sought to examine the independent effects of drug dealing and sex work on experiencing violence among street-involved youth. METHODS—Data were derived from a prospective cohort of street-involved youth aged 14–26 who use drugs in Vancouver, Canada, between September 2005 and May 2014. Multivariable generalized estimating equations were used to examine the impact of involvement in drug dealing and sex work on experiencing violence. RESULTS—Among 1,152 participants, including 364 (31.6%) women, 740 (64.2%) reported having experienced violence at some point during the study period. In multivariable analysis, involvement in drug dealing but not sex work remained independently associated with experiencing violence among females (adjusted odds ratio [AOR]: 1.43; 95% confidence interval [CI]: 1.08 – 1.90) and males (AOR: 1.50; 95% CI: 1.25 – 1.80), while involvement in sex work only was not associated with violence among females (AOR: 1.15; 95% CI: 0.76 – 1.74) or males (AOR: 1.42; 95% CI: 0.81 – 2.48). CONCLUSION—Findings indicate that involvement in drug dealing is a major factor associated with experiencing violence among our sample. In addition to conventional interventions, such as addiction treatment, novel approaches are needed to reduce the risk of violence for drug-using youth who are actively engaged in drug dealing. The potential for low-threshold employment and decriminalization of drug use to mitigate violence warrants further study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".