Comprehensive Analysis and Leveraging Online Innovations to Improve HIV and STI Prevention and Treatment Services in Major Cities of Thailand
Bibliographic record
Abstract
Despite a 50% reduction in new HIV cases since 2010, HIV and sexually transmitted infections (STIs) remain pressing public health challenges in Thailand, particularly among marginalized and high-risk populations. This study examined 8,865 bookings made between October 1, 2023, and September 30, 2024, through Love2Test.org (L2T), a mobile platform launched in December 2020 to provide accessible sexual health services, including HIV testing and STI screening. Applying descriptive analysis, service utilization analysis, and K-means clustering, the study uncovered booking patterns, user demographics, and behavioral trends. L2T attracted a diverse user base, with 32.15% aged 15-24, 47.06% aged 25-34, 2.6% identifying as transgender women, and 16.59% as non-binary individuals, underscoring its inclusivity. HIV testing (38%) and STI screening (28%) were the most requested services, while Bangkok accounted for 74.5% of bookings, highlighting geographic disparities in access. Cluster analysis identified three user groups: older males seeking HIV tests, younger males and middle-aged non-binary individuals utilizing PrEP and STI screening, and a mixed group accessing both services. With 158,639 visitors and 8,865 service bookings, resulting in a visit-to-booking rate of 5.59%, L2T has proven instrumental in delivering confidential, targeted care to vulnerable groups. By addressing service gaps and promoting HIV testing, PrEP education, and STI prevention, L2T strengthens public health efforts. Expanding its outreach and improving service availability beyond Bangkok present opportunities to further reduce health disparities and bolster Thailand's response to HIV and STI challenges.
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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 | 0.001 | 0.000 |
| 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".