Compulsory drug detention and injection drug use cessation and relapse in <scp>B</scp>angkok, <scp>T</scp>hailand
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Strategies to promote the reduction and cessation of injection drug use are central to human immunodeficiency virus prevention and treatment efforts globally. Though drug use cessation is a major focus of drug policy in Thailand, little is known about factors associated with injection cessation and relapse in this setting. DESIGN AND METHODS: A cross-sectional study was conducted between July and October 2011 of a community-recruited sample of people who inject drugs in Bangkok, Thailand. Using multivariate logistic regression, we examined the prevalence and correlates of injection drug use cessation with subsequent relapse. RESULTS: Among 422 participants, 209 (49.5%) reported a period of injection drug use cessation of at least one year. In multivariate analyses, incarceration (adjusted odds ratio [AOR] 13.07), voluntary drug treatment (AOR 2.75), midazolam injection (AOR 2.48) and number of years since first injection (AOR 1.07) were positively associated with injection cessation of duration greater than a year (all P < 0.05). Exposure to compulsory drug detention was positively associated (AOR 2.61) and methadone treatment was negatively associated (AOR 0.38) with short-term cessation only. Injection drug use cessation was most often due to incarceration (74%), and relapse was associated with release from prison (66%). DISCUSSION AND CONCLUSION: Half of the study participants had previously stopped injecting drugs for more than a year, and this was strongly associated with incarceration. Compulsory drug detention was associated with short-term cessation and relapse. A range of evidence-based strategies should be made available to facilitate sustained cessation of injection drug use in Thailand.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".