“Street sweeps”: The municipal government-enforced confiscation of personal belongings among unstably housed people who use drugs in Vancouver, Canada
Bibliographic record
Abstract
OBJECTIVES: Street sweeps, involving the state-enforced removal of makeshift residences and confiscation of personal belongings from people in public spaces, are a common tool employed by urban governments to address public health and safety concerns. Amidst the ongoing housing and toxic drug poisoning crises in Vancouver, Canada, we sought to characterize experiences of confiscation of personal belongings by city workers among unstably-housed people who use drugs people who use drugs. STUDY DESIGN: Cohort study. METHODS: We used multivariable generalized estimating equations models to longitudinal data derived from unstably-housed people who use drugs (including those reporting homelessness and living in single room occupancy hotels) participating in three harmonized cohort studies of community-recruited people who use drugs in Vancouver in 2021-2023. RESULTS: In total, 13.6 % of 691 eligible participants (and 23.6 % of 233 reporting homelessness) reported municipal government-enforced belonging confiscation in the past six months at least once. In multivariable analyses, violent victimization (adjusted odds ratio [AOR] = 2.14; 95 % confidence interval [CI]: 1.27, 3.60) and inability to access health/social services (AOR = 2.19, 95 % CI: 1.32, 3.65) were significantly and positively associated with belonging confiscation, and so was non-fatal overdose (AOR = 1.94, 95 % CI: 1.01, 3.74) among those reporting homelessness. CONCLUSION: Findings underscore that confiscation of belongings was relatively widespread among our cohort of people who use drugs and concentrated among individuals who are structurally marginalized, emphasizing the need to end street sweeps and prioritize development of dignified housing and harm reduction policies involving affected communities in decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".