Experiences of violence during the COVID-19 pandemic among people who use drugs in a Canadian setting: a gender-based cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: People who use drugs (PWUD) experience disproportionately high rates of violent victimization. Emerging research has demonstrated that the COVID-19 pandemic has exacerbated violence against some priority populations (e.g., women), however there is limited research examining the impact of the pandemic on the experiences of violence of PWUD. METHODS: Using data collected between July and November 2020 from three prospective cohort studies of PWUD in Vancouver, Canada, we employed multivariable logistic regression stratified by gender to identify factors associated with recent experiences of violence, including the receipt of COVID-19 emergency income support. RESULTS: In total, 77 (17.3%) of 446 men, and 54 (18.8%) of 288 women experienced violence in the previous six months. Further, 33% of men and 48% of women who experienced violence reported that their experience of violence was intensified since the COVID-19 pandemic began. In the multivariable analyses, sex work (Adjusted Odds Ratio [AOR] = 2.15, 95% confidence interval [CI]: 1.06-4.35) and moderate to severe anxiety or depression (AOR = 3.00, 95% CI: 1.37-6.57) were associated with experiencing violence among women. Among men, drug dealing (AOR = 1.93, 95%CI: 1.10-3.38), street-based income sources (AOR = 1.93, 95%CI: 1.10-3.38), homelessness (AOR = 2.54, 95%CI: 1.40-4.62), and regular employment (AOR = 2.97, 95% CI: 1.75-5.04) were associated with experiencing violence. CONCLUSION: Our study results suggest economic conditions and gender were major factors associated with experiencing violence among our sample of PWUD during COVID-19. These findings highlight criminalization of drug use and widespread socioeconomic challenges as barriers to addressing violence among PWUD during periods of crisis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".