Contextual Factors Influencing Implementation of HIV Treatment Support Strategies for Female Sex Workers Living With HIV in South Africa: A Qualitative Analysis Using the Consolidated Framework for Implementation Research
Bibliographic record
Abstract
BACKGROUND: Female sex workers (FSWs) face a confluence of multilevel barriers to HIV care. In South Africa, 63% of FSWs are living with HIV and <40% are virally suppressed. The objective of this analysis was to identify implementation determinants of 2 HIV treatment support strategies. METHODS: The Siyaphambili trial tested a decentralized treatment provision and an individualized case management strategy aimed to support FSWs living with unsuppressed HIV viral loads. We identified a nested sample of trial participants using maximum variation sampling (n = 36) as well as a purposively selected sample of implementors (n = 12). We used semistructured interview guides, developed using the Consolidated Framework for Implementation Research (CFIR) and deductively coded the transcripts using CFIR, systematically assessing the strength and valence of implementation. We compared construct ratings to determine whether any constructs distinguished implementation across strategies. RESULTS: Across 3 CFIR domains (innovation characteristics, inner setting, and outer setting), 12 constructs emerged as facilitating, hindering, or having mixed effects on strategy implementation. The relative advantage, design, adaptability, and complexity constructs of the innovation characteristics and the work infrastructure construct of the inner setting were strongly influential (±2 or +2). While the majority of construct valence and strength rating (9-12) were not distinguishing across strategies, we observed 3 weakly distinguishing CFIR constructs (relative advantage, complexity, and available resources). CONCLUSIONS: Given the potential benefits of differentiated service delivery strategies, identifying the relative importance of implementation determinants facilitates transparency and evaluation, supporting future strategy design and implementation. Optimizing implementation will support addressing inequities in HIV care and treatment services.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".