Policing Practices and Risk of HIV Infection Among People Who Inject Drugs
Bibliographic record
Abstract
Drug-law enforcement constitutes a structural determinant of health among people who inject drugs (PWID). Street encounters between police and PWID (e.g., syringe confiscation, physical assault) have been associated with health harms, but these relationships have not been systematically assessed. We conducted a systematic literature review to evaluate the contribution of policing to risk of human immunodeficiency virus (HIV) infection among PWID. We screened MEDLINE, sociological databases, and gray literature for studies published from 1981 to November 2018 that included estimates of HIV infection/risk behaviors and street policing encounters. We extracted and summarized quantitative findings from all eligible studies. We screened 8,201 abstracts, reviewed 175 full-text articles, and included 27 eligible analyses from 9 countries (Canada, China, India, Malaysia, Mexico, Russia, Thailand, Ukraine, and the United States). Heterogeneity in variable and endpoint selection precluded meta-analyses. In 5 (19%) studies, HIV infection among PWID was significantly associated with syringe confiscation, reluctance to buy/carry syringes for fear of police, rushed injection due to a police presence, fear of arrest, being arrested for planted drugs, and physical abuse. Twenty-one (78%) studies identified policing practices to be associated with HIV risk behaviors related to injection drug use (e.g., syringe-sharing, using a "shooting gallery"). In 9 (33%) studies, policing was associated with PWID avoidance of harm reduction services, including syringe exchange, methadone maintenance, and safe consumption facilities. Evidence suggests that policing shapes HIV risk among PWID, but lower-income settings are underrepresented. Curbing injection-related HIV risk necessitates additional structural interventions. Methodological harmonization could facilitate knowledge generation on the role of police as a determinant of population health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".