Reports of police beating and associated harms among people who inject drugs in Bangkok, Thailand: a serial cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Thailand has for years attempted to address illicit drug use through aggressive drug law enforcement. Despite accounts of widespread violence by police against people who inject drugs (IDU), the impact of police violence has not been well investigated. In the wake of an intensified police crackdown in 2011, we sought to identify the prevalence and correlates of experiencing police beating among IDU in Bangkok. METHODS: Community-recruited samples of IDU in Bangkok were surveyed between June 2009 and October 2011. Multivariate log-binomial regression was used to identify factors associated with reporting police beating. RESULTS: In total, 639 unique IDU participated in this serial cross-sectional study, with 240 (37.6%) participants reporting that they had been beaten by police. In multivariate analyses, reports of police beating were associated with male gender (Adjusted Prevalence Ratio [APR] = 4.43), younger age (APR = 1.69), reporting barriers to accessing healthcare (APR = 1.23), and a history of incarceration (APR = 2.51), compulsory drug detention (APR = 1.22) and syringe sharing (APR = 1.44), and study enrolment in 2011 (APR = 1.27) (all p < 0.05). Participants most commonly reported police beating during the interrogation process. CONCLUSIONS: A high proportion of IDU in Bangkok reported having been beaten by the police. Experiencing police beating was independently associated with various indicators of drug-related harm. These findings suggest that the over-reliance on enforcement-based approaches is contributing to police-perpetrated abuses and the perpetuation of the HIV risk behaviour among Thai IDU.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".