Police and public health partnerships: Evidence from the evaluation of Vancouver's supervised injection facility
Bibliographic record
Abstract
In various settings, drug market policing strategies have been found to have unintended negative effects on health service use among injection drug users (IDU). This has prompted calls for more effective coordination of policing and public health efforts. In Vancouver, Canada, a supervised injection facility (SIF) was established in 2003. We sought to determine if local police impacted utilization of the SIF. We used generalized estimating equations (GEE) to prospectively identify the prevalence and correlates of being referred by local police to Vancouver's SIF among IDU participating in the Scientific Evaluation of Supervised Injecting (SEOSI) cohort during the period of December 2003 to November 2005. Among 1090 SIF clients enrolled in SEOSI, 182 (16.7%) individuals reported having ever been referred to the SIF by local police. At baseline, 22 (2.0%) participants reported that they first learned of the SIF via police. In multivariate analyses, factors positively associated with being referred to the SIF by local police when injecting in public include: sex work (Adjusted Odds Ratio [AOR] = 1.80, 95%CI 1.28-2.53); daily cocaine injection (AOR = 1.54, 95%CI 1.14-2.08); and unsafe syringe disposal (AOR = 1.46, 95%CI 1.00-2.11). These findings indicate that local police are facilitating use of the SIF by IDU at high risk for various adverse health outcomes. We further found that police may be helping to address public order concerns by referring IDU who are more likely to discard used syringes in public spaces. Our study suggests that the SIF provides an opportunity to coordinate policing and public health efforts and thereby resolve some of the existing tensions between public order and health initiatives.
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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 | 0.017 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".