Reports of evidence planting by police among a community-based sample of injection drug users in Bangkok, Thailand
Bibliographic record
Abstract
BACKGROUND: Drug policy in Thailand has relied heavily on law enforcement-based approaches. Qualitative reports indicate that police in Thailand have resorted to planting drugs on suspected drug users to extort money or provide grounds for arrest. The present study sought to describe the prevalence and factors associated with this form of evidence planting by police among injection drug users (IDU) in Bangkok. METHODS: Multivariate logistic regression was used to identify factors associated with evidence planting of drugs by police among a community-based sample of IDU in Bangkok. We also examined the prevalence and average amount of money paid by IDU to police in order to avoid arrest. RESULTS: 252 IDU were recruited between July and August, 2008, among whom 66 (26.2%) were female and the median age was 36.5 years. In total, 122 (48.4%) participants reported having drugs planted on them by police. In multivariate analyses, this form of evidence planting was positively associated with midazolam use (Adjusted Odds Ratio [AOR] = 2.84; 95% Confidence Interval [CI]: 1.58 - 5.11), recent non-fatal overdose (AOR = 2.56; 95%CI: 1.40 - 4.66), syringe lending (AOR = 2.08; 95%CI: 1.19 - 3.66), and forced drug treatment (AOR = 1.88; 95%CI: 1.05 - 3.36). Among those who reported having drugs planted on them, 59 (48.3%) paid police a bribe in order to avoid arrest. CONCLUSION: A high proportion of community-recruited IDU participating in this study reported having drugs planted on them by police. Drug planting was found to be associated with numerous risk factors including syringe sharing and participation in government-run drug treatment programs. Immediate action should be taken to address this form of abuse of power reportedly used by police.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".