Planning and sustaining HIV response in the countries of the “risky middle”
Bibliographic record
Abstract
This paper focusses on high-HIV middle-income countries termed the "risky middle", i.e. characterised by a typology based on HIV burden and gross national income (GNI), according to which seven countries - Lesotho, Eswatini, Kenya, Zimbabwe, Tanzania, Namibia and Zambia - are identified. There is particular concern for "people left behind", the factors determining a country's ability to mobilise resources in the context of multiple development needs - including economic disparities; the political economy of fiscal decision-making; levels of health investment; health and community systems; political will; and currency fluctuations. While donors will support lower-income countries and higher-income countries can compensate from domestic resources, there is a risk that some high-burden, lower middle-income countries will be unable to sustain a response. Continued growth means that there are countries transitioning to higher World Bank income classification - an important criterion for allocating development assistance for health. Our concern is that countries may face external funding reduction once their income category improves, and those in the risky middle will be unable to compensate from domestic resources. We conclude, with guidance from UNAIDS, the international community should step up support for "risky middle" countries. In addition these countries need to recognise the threat and develop measures to counter it, including improved accountability. Funding declines should be reversed through funding benchmarks that relate to both GDP and HIV prevalence. Finally, risky middle countries could constitute themselves as a special interest group, to protect their HIV funding and AIDS response.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".