Socio-economic marginalization in the structural production of vulnerability to violence among people who use illicit drugs
Bibliographic record
Abstract
Objective: Many people who use illicit drugs (PWUD) face challenges to their financial stability. Resulting activities that PWUD undertake to generate income may increase their vulnerability to violence. We therefore examined the relationship between income generation and exposure to violence across a wide range of income generating activities among HIV-positive and HIV-negative PWUD living in Vancouver, Canada. Methods: Data were derived from cohorts of HIV-seropositive and HIV-seronegative PWUD (n=1876) between December 2005 and November 2012. We estimated the relationship between different types of income generation and suffering any kind of violence using bivariate and multivariate generalized estimating equations (GEE), as well as the characteristics of violent interactions. Results: Exposure to violence was reported among 977 (52%) study participants over the study period. In multivariate models controlling for socio-demographic characteristics, mental health status, and drug use patterns, violence was independently and positively associated with participation in street-based income generation activities (i.e., recycling, squeegeeing, and panhandling; adjusted odds ratio [AOR]=1.39, 95% confidence interval [CI]=1.23-1.57), sex work (AOR=1.23, 95%CI=1.00-1.50), drug dealing (AOR=1.63, 95%CI=1.44-1.84), and theft and other acquisitive criminal activity (AOR=1.51, 95%CI=1.27-1.80). Engagement in regular, self or temporary employment was not associated with being exposed to violence. Strangers were the most common perpetrators of violence (46.7%) and beatings the most common type of exposure (70.8%). Conclusions: These results suggest that economic activities expose individuals to contexts associated with social and structural vulnerability to violence. The creation of safe economic opportunities that minimize vulnerability to violence among PWUD is therefore urgently required.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".