Siyaphambili protocol: An evaluation of randomized, nurse‐led adaptive HIV treatment interventions for cisgender female sex workers living with HIV in Durban, South Africa
Bibliographic record
Abstract
In South Africa, 60% of female sex workers are estimated to be living with human immunodeficiency virus (HIV). Many of these women face structural and individual-level barriers to initiating, accessing, and adhering to antiretroviral therapy (ART). While data are limited, it is estimated that less than 40% of sex workers living with HIV achieve viral suppression, leading to suboptimal clinical outcomes and sustained risks of onward sexual and vertical HIV transmission. Siyaphambili, a NINR/NIH-funded study, focuses on studying optimal implementation strategies for meeting HIV treatment needs among cisgender female sex workers living with HIV who are not virally suppressed. Here, we present the study protocol of this sequential multiple assignment randomized trial. In total, 800 viremic female sex workers will be enrolled into an 18-month adaptive implementation study to 1) compare the effectiveness and durability of a nurse-led decentralized ART treatment program versus an individualized case management approach, in isolation or in combination to achieve viral suppression and 2) estimate incremental cost-effectiveness of interventions and combinations of interventions. The primary outcome is a combined intention-to-treat outcome of retention in ART care and viral suppression at 18 months with secondary implementation outcomes. Siyaphambili aims to inform the implementation of and scale-up of HIV treatment services for female sex workers by determining the minimal package of services needed to achieve viral suppression and by characterizing individuals in need of more intensive HIV treatment approaches.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".