Drug use patterns among Thai illicit drug injectors amidst increased police presence
Bibliographic record
Abstract
Thailand has traditionally pursued an aggressive enforcement-based anti-illicit drug policy in an effort to make the country "drug-free." In light of this ongoing approach, we sought to assess impacts of enforcement on drug use behaviors among a cohort of injection drug users (IDU) in Thailand. We examined drug use patterns among IDU participating in a cross-sectional study conducted in Bangkok (n = 252). Participants were asked to provide data regarding patterns of drug use in the previous six months, including types of drugs consumed, method of consumption, frequency of use, and weekly income spent on drugs. We also conducted bivariate analyses to identify a possible effect of a reported increase in police presence on measures of drug use and related risk behaviors among study participants. One hundred fifty-five (61.5%) individuals reported injection heroin use and 132 (52.4%) individuals reported injection midazolam use at least daily in the past six months. Additionally, 86 (34.1%) individuals reported at least daily injection Yaba and Ice (i.e., methamphetamine) use. Participants in our study reported high levels of illicit drug use, including the injection of both illicit and licit drugs. In bivariate analyses, no association between increased police presence and drug use behaviors was observed. These findings demonstrate high ongoing rates of drug injecting in Thailand despite reports of increased levels of strict enforcement and enforcement-related violence, and raise questions regarding the merits of this approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".