‘No test, no disease’: Multilevel barriers to HIV testing among young men who have sex with men and transgender women in three semi-urban areas in Thailand
Bibliographic record
Abstract
Young gay and other men who have sex with men and young transgender women in Thailand continue to be at high risk for HIV infection. We explored multilevel influences on HIV testing in order to inform the design of tailored interventions. We conducted four focus group discussions with 16–20-year-old gay men and transgender persons (n = 25) and 17 key informant interviews with healthcare providers, NGO leaders, and youth advocates. Focus groups and interviews were transcribed and reviewed using thematic analysis in Thai and English language by a bilingual team. We identified intersecting, culturally situated barriers at individual (lack of HIV knowledge, low HIV risk perception, denial), social (intersectional sexual- and HIV-related stigma, lack of family communication), institutional (inadequate and non-LGBT-inclusive sexual health education in schools, lack of youth-friendly clinics) and policy levels (parental consent requirements for HIV testing by minors). Multilevel and multisystem factors coalesce to form extensive barriers to HIV testing access and utilisation and promote disengagement from HIV prevention more broadly. Multicomponent, youth-engaged interventions informed by Thai sociocultural history and practices are needed in renewed approaches to HIV prevention and testing to end the epidemic among young gay and transgender people 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 | 0.001 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".