Designing research funding schemes to promote global health equity: An exploration of current practice in health systems research
Bibliographic record
Abstract
International research is an essential means of reducing health disparities between and within countries and should do so as a matter of global justice. Research funders from high-income countries have an obligation of justice to support health research in low and middle-income countries (LMICs) that furthers such objectives. This paper investigates how their current funding schemes are designed to incentivise health systems research in LMICs that promotes health equity. Semi-structured in-depth interviews were performed with 16 grants officers working for 11 funders and organisations that support health systems research: the Alliance for Health Policy and Systems Research, Comic Relief, Doris Duke Foundation, European Commission, International Development Research Centre, Norwegian Agency for Development Cooperation, Research Council of Norway, Rockefeller Foundation, UK Department of International Development, UK Medical Research Council, and Wellcome Trust. Thematic analysis of the data demonstrates their funding schemes promote health systems research with (up to) five key features that advance health equity: being conducted with worst-off populations, focusing on research topics that advance equitable health systems, having LMIC ownership of the research agenda, strengthening LMIC research capacity, and having an impact on health disparities. The different types of incentives that encouraged proposed projects to have these features are identified and classified by their strength (strong, moderate, weak). It is suggested that research funders ought to create and maintain funding schemes with strong incentives for the features identified above in order to more effectively help reduce global health disparities.
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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 | 0.563 | 0.494 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.017 | 0.064 |
| Scholarly communication | 0.043 | 0.040 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".