Assessing the impacts of donor support on Nigeria's health system: The global fund in perspective
Bibliographic record
Abstract
Abstract From 2003 to 2019, the Global Fund to Fight AIDS, Tuberculosis, and Malaria (the “Global Fund”) disbursed a total of US$2.3 billion in grants to Nigeria, mainly for the prevention and treatment of HIV/AIDS, tuberculosis, and malaria. This paper examines the impacts of the Global Fund's interventions on Nigeria's health system. Case study evidence shows that while the Global Fund has been successful in achieving its specific performance targets, its impacts on Nigeria's health system has been minimal at best. Major reasons for its negligible impacts on the country's health system include the Global Fund's ambivalent operational structure, little input from the host country in program design, excessive focus on fiduciary matters as opposed to public health interventions, as well as emphasis on parochial performance targets. Policy implications arising from this study include the need for domestic actors in Nigeria's health sector to have significant input in designing the Global Fund's projects in the country. In addition, the Global Fund's board and major donors should work collaboratively to refocus the institution to enhance its public health impacts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".