Low rates of hepatitis C testing among people who inject drugs in Thailand: implications for peer-based interventions
Bibliographic record
Abstract
BACKGROUND: Regular testing for hepatitis C virus (HCV) provides an opportunity for HCV prevention and treatment efforts. In Thailand, the barriers and facilitators of HCV testing among people who inject drugs (IDU) are not known. METHODS: Using data derived from the Mitsampan Community Research Project between July and October 2011, we assessed the prevalence and factors associated with ever having been tested for HCV antibodies using bivariate statistics and multivariate logistic regression. RESULTS: Among 427 participants, 141 (33.0%) reported a history of HCV antibody testing. In multivariate analyses, factors positively associated with receiving an HCV antibody test included higher than secondary education [adjusted odds ratio (AOR) = 2.20; 95% confidence interval (CI): 1.35-3.64], binge drug use (AOR = 1.81; 95% CI: 1.12-2.93), methadone treatment enrollment (AOR = 3.47; 95% CI: 1.85-6.95) and having received peer-based education on HCV (AOR = 4.22; 95% CI: 2.66-6.77). CONCLUSIONS: We found one-third of Thai IDU in our sample reporting a history of HCV testing. The finding that IDU who received peer-based HCV education were more likely to access HCV testing provides evidence for the value of peer-based interventions for this population.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".