Willingness to Access Peer-Delivered HIV Testing and Counseling Among People Who Inject Drugs in Bangkok, Thailand
Bibliographic record
Abstract
Peer-based models for human immunodeficiency virus (HIV) testing have been implemented to increase access to testing in various settings. However, little is known about the acceptability of peer-delivered testing and counseling among people who inject drugs (IDU). During July and October 2011, data derived from the Mitsampan Community Research Project were used to construct three multivariate logistic regression models identifying factors associated with willingness to receive peer-delivered pre-test counseling, rapid HIV testing, and post-test counseling. Among a total of 348 IDU, 44, 38, and 36 % were willing to receive peer-delivered pre-test counseling, rapid HIV testing, and post-test counseling, respectively. In multivariate analyses, factors associated with willingness to access peer-delivered pre-test counseling included: male gender (adjusted odds ratio (AOR) = 0.48), higher than secondary education (AOR = 1.91), and binge drug use (AOR = 2.29) (all p < 0.05). Factors associated with willingness to access peer-delivered rapid HIV testing included: higher than secondary education (AOR = 2.06), binge drug use (AOR = 2.23), incarceration (AOR = 2.68), avoiding HIV testing (AOR = 0.24), and having been to the Mitsampan Harm Reduction Center (AOR = 1.63) (all p < 0.05). Lastly, binge drug use (AOR = 2.40), incarceration (AOR = 1.94), and avoiding HIV testing (AOR = 0.23) (all p < 0.05) were significantly associated with willingness to access peer-delivered post-test counseling. We found that a substantial proportion of Thai IDU were willing to receive peer-delivered HIV testing and counseling. These findings highlight the potential of peer-delivered testing to complement existing HIV testing programs that serve IDU.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".