Longitudinal Trajectories of Engagement With HIV Treatment Support Strategies Among Female Sex Workers Living With HIV in South Africa
Bibliographic record
Abstract
BACKGROUND: Tailored implementation strategies to promote the uptake and scale-up of antiretroviral therapy (ART) among female sex workers (FSWs) in South Africa are needed, because <50% of FSW living with HIV are on ART and <40% are virally suppressed. SETTING: We conducted a randomized trial testing 2 HIV treatment support strategies (decentralized treatment provision; individualized case management) among 777 FSW living with HIV and not virally suppressed (≥50 copies/mL) in Durban, South Africa, June 2018-January 2022. METHODS: We defined strategy engagement in a 6-month interval if the monthly strategy session was delivered and the FSW participated. Group-based trajectory modeling with logit response function was used to identify engagement trajectories and describe correlates of trajectories. We used Poisson regression analysis with robust variance estimation to assess the association between assigned trajectory group and 18-month retention and viral suppression (<50 copies/mL). RESULTS: We identified 4 trajectories: no engagement (12%), late engagement (10%), engagement corresponding with study visits (53%), and consistent engagement (25%). FSW who were older, unmarried, receiving ART at enrollment, and decentralized treatment provision assignment were more likely to be classified in the consistently engaged trajectory compared with the no engagement trajectory. The prevalence of 18-month retention and viral suppression was higher among FSW assigned to the consistent engagement trajectory than FSW assigned to the no engagement trajectory (prevalence ratio = 3.2, 95% confidence interval: 1.6 to 6.3). CONCLUSIONS: Person-centered HIV services that address unmet treatment needs could improve health, viral suppression, and subsequently reduce population-level HIV transmission. CLINICAL TRIAL REGISTRATION: NCT03500172.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".