Assisted injection among people who inject drugs in Thailand
Bibliographic record
Abstract
BACKGROUND: Assisted injection is common among people who inject drugs (IDU), and has been associated with elevated risk for HIV infection and overdose. However, this practice has not been explored in the Asian context, including in Thailand, where HIV prevalence among IDU remains high. METHODS: Using multivariate logistic regression, we examined the prevalence and correlates of assisted injecting among IDU participating in the Mitsampan Community Research Project in Bangkok. We also sought to identify reasons for engaging in assisted injecting and those who provide this form of assistance. RESULTS: In total, 430 IDU participated in this study, including 376 (87.5%) who reported having ever required assistance injecting, and 81 (18.8%) who reported assisted injecting in the previous six months. In multivariate analyses, assisted injecting in the previous six months was independently and positively associated with being female (adjusted odds ratio [AOR] = 2.42; 95% confidence interval [CI]: 1.40 - 4.18), being a weekly heroin injector (AOR = 1.78; 95% CI: 0.99 - 3.20), syringe sharing (AOR = 2.08; 95% CI: 1.18 - 3.68) and soft-tissue infection (AOR = 3.51; 95% CI: 1.43 - 2.53). Having a longer injecting career (AOR = 0.96; 95% CI: 0.94 - 0.99) was negatively associated with assisted injecting. Primary reasons given for engaging in assisted injecting included being new to injecting and lacking knowledge on how to inject. The most common providers of assistance with injecting were close friends. CONCLUSION: We found a high prevalence of assisted injecting among IDU in Bangkok, with females, frequent heroin injectors, those with shorter injecting careers being more likely to engage in this practice. Those who require help with the injecting process are more likely to share syringes, and have skin infections. These findings indicate the need for interventions focused on promoting safer and self-administered injections.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".