Declining trends in exposures to harmful policing among people who inject drugs in Vancouver, Canada
Bibliographic record
Abstract
Introduction: In 2006, the Vancouver Police Department (VPD) developed an organization-wide drug policy approach, which included endorsing harm reduction strategies for people who inject drugs (PWID). We sought to examine rates of potentially harmful policing exposures and associated HIV risk behaviour among PWID in Vancouver, Canada before and after the VPD policy change. Methods: Data were derived from two prospective cohort studies of PWID. Multivariable generalized estimating equation models were used to examine changes in the risk of confiscation of drug use paraphernalia and physical violence by the police, as well as changes in the relationship between exposures to the two policing practices and sharing of drug use paraphernalia, before and after the policy change. Results: Among 2193 participants, including 757 (34.5%) women, the rates of experiencing police confiscation of drug use paraphernalia declined from 22.3% in 2002 to 2.8% in 2014, and the rates of reporting experiencing physical violence by the police also declined from 14.1% in 2004 to 2.9% in 2014. In multivariable analyses, the post-policy change period remained independently and negatively associated with reports of confiscation of drug use paraphernalia (adjusted odds ratio (AOR): 0.25; 95% confidence interval (CI): 0.21 to 0.31) and reported physical violence by the police (AOR: 0.76; 95% CI: 0.63 to 0.91). However, experiencing both confiscation of drug use paraphernalia and physical violence by the police (AOR: 1.92; 95% CI: 1.10 to 3.33) and experiencing only confiscation of drug use paraphernalia (AOR: 1.71; 95% CI: 1.34 to 2.19) remained independently and positively associated with sharing of drug use paraphernalia during the post-policy change period. Conclusions: In our study, two policing practices known to increase HIV risk among PWID have declined significantly since the local police launched an evidence-based drug policy approach. However, these practices remained independently associated with elevated HIV risk after the post-policy change. Although there remains a continued need to ensure that policing activities do not undermine public health efforts, these findings demonstrate that a major shift towards a public health approach to policing is possible for a municipal police force.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".