Understanding Uganda’s early adoption of novel differentiated HIV treatment services: a qualitative exploration of drivers of policy uptake
Bibliographic record
Abstract
BACKGROUND: Although differentiated service delivery (DSD) for HIV treatment was endorsed by the WHO in its landmark 2016 guidelines to lessen patients' need to frequently visit clinics and hence to reduce unnecessary burdens on health systems, uptake has been uneven globally. This paper is prompted by the HIV Policy Lab's annual report of 2022 which reveals substantial variations in programmatic uptake of differentiated HIV treatment services across the globe. We use Uganda as a case study of an 'early adopter' to explore the drivers of programmatic uptake of novel differentiated HIV treatment services. METHODS: We conducted a qualitative case-study in Uganda. In-depth interviews were held with national-level HIV program managers (n = 18), district health team members (n = 24), HIV clinic managers (n = 36) and five focus groups with recipients of HIV care (60 participants) supplemented with documentary reviews. Our thematic analysis of the qualitative data was guided by the Consolidated Framework for Implementation Research (CFIR)'s five domains (inner context, outer setting, individuals, process of implementation). RESULTS: Our analysis reveals that drivers of Uganda's 'early adoption' of DSD include: having a decades-old HIV treatment intervention implementation history; receiving substantial external donor support in policy uptake; the imperatives of having a high HIV burden; accelerated uptake of select DSD models owing to Covid-19 'lockdown' restrictions; and Uganda's participation in clinical trials underpinning WHO guidance on DSD. The identified processes of implementation entailed policy adoption of DSD (such as the role of local Technical Working Groups in domesticating global guidelines, disseminating national DSD implementation guidelines) and implementation strategies (high-level health ministry buy-in, protracted patient engagement to enhance model uptake, devising metrics for measuring DSD uptake progress) for promoting programmatic adoption. CONCLUSION: Our analysis suggests early adoption derives from Uganda's decades-old HIV intervention implementation experience, the imperative of having a high HIV burden which prompted innovations in HIV treatment delivery as well as outer context factors such as receiving substantial external assistance in policy uptake. Our case study of Uganda offers implementation research lessons on pragmatic strategies for promoting programmatic uptake of differentiated treatment HIV services in other countries with a high HIV burden.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".