The impacts of donor transitions on health systems in middle-income countries: a scoping review
Bibliographic record
Abstract
As countries graduate from low-income to middle-income status, many face losses in development assistance for health and must 'transition' to greater domestic funding of their health response. If improperly managed, donor transitions in middle-income countries (MICs) could present significant challenges to global health progress. No prior knowledge synthesis has comprehensively surveyed how donor transitions can affect health systems in MICs. We conducted a scoping review using a structured search strategy across five academic databases and 37 global health donor and think tank websites for literature published between January 1990 and October 2018. We used the World Health Organization health system 'building blocks' framework to thematically synthesize and structure the analysis. Following independent screening, 89 publications out of 11 236 were included for data extraction and synthesis. Most of this evidence examines transitions related to human immunodeficiency virus/Acquired Immune Deficiency Syndrome (AIDS; n = 45, 50%) and immunization programmes (n = 14, 16%), with a focus on donors such as the Global Fund to Fight AIDS, Tuberculosis and Malaria (n = 26, 29%) and Gavi, the Vaccine Alliance (n = 15, 17%). Donor transitions are influenced by the actions of both donors and country governments, with impacts on every component of the health system. Successful transition experiences show that leadership, planning, and pre-transition investments in a country's financial, technical, and logistical capacity are vital to ensuring smooth transition. In the absence of such measures, shortages in financial resources, medical product and supply stock-outs, service disruptions, and shortages in human resources were common, with resulting implications not only for programme continuation, but also for population health. Donor transitions can affect different components of the health system in varying and interconnected ways. More rigorous evaluation of how donor transitions can affect health systems in MICs will create an improved understanding of the risks and opportunities posed by donor exits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".