Impact of analytical treatment interruptions on sexual behaviors: a mixed-methods longitudinal study of women enrolled in an HIV cure–related trial in Durban, South Africa
Bibliographic record
Abstract
BACKGROUND: Analytical treatment interruptions (ATIs) are increasingly used in HIV cure-related trials to assess intervention efficacy. OBJECTIVE: To assess the impact of participation in ATI-inclusive trials on sexual behaviors and transmission risks among women in Durban, South Africa, and to develop recommendations for safer trial design. METHODS: From 2022-2025, we conducted a mixed-methods socio-behavioral study embedded within a Phase 2A ATI-inclusive HIV cure-related trial. Nineteen participants completed surveys at four trial timepoints, baseline (T1), pre-ATI (T2), post-ATI (T3), and end of trial (T4), and in-depth interviews at the first three (T1-T3). We summarized quantitative data using medians and percentages and used the Theory of Planned Behavior to analyze the qualitative data. RESULTS: Participants had strong intentions toward HIV prevention; however, behaviors were inconsistently enacted during the trial. Condom use was highest during the ATI; however, 40% of participants reported never using condoms. Abstinence was not considered an acceptable option within relationships. Disclosure of HIV and ATI participation facilitated prevention behavior but was undermined by HIV-related stigma, while non-disclosure and new sexual behaviors created relationship instability. Prevention was supported by trial staff's responsiveness and close monitoring, which helped build participants' trust. CONCLUSIONS: For ATI-inclusive trials to reduce HIV transmission risk and social harms, investigators must understand contextual barriers perceived by participants who are expected to enact protective behaviors. Adaptive trial designs that incorporate participant-centered behavioral support, monitor and address emergent harms and facilitate prevention through access to HIV testing and PrEP for partners, must align with participants' evolving realities.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".