Funders: The missing link in equitable global health research?
Bibliographic record
Abstract
Global health research is mired by inequities, some of which are linked to current approaches to research funding. The role of funders and donors in achieving greater equity in global health research needs to be clearly defined. Imbalances of power and resources between high income countries (HICs) and low- and middle-income countries (LMICs) is such that many funding approaches do not centre the role of LMIC researchers in shaping global health research priorities and agenda. Relative to need, there is also disparity in financial investment by LMIC governments in health research. These imbalances put at a disadvantage LMIC health professionals and researchers who are at forefront of global health practice. Whilst many LMICs do not have the means (due to geopolitical, historical, and economic reasons) for direct investment, if those with means were to invest more of their own funds in health research, it may help LMICs become more self-sufficient and shift some of the power imbalances. Funders and donors in HICs should address inequities in their approach to research funding and proactively identify mechanisms that assure greater equity-including via direct funding to LMIC researchers and direct funding to build local LMIC-based, led, and run knowledge infrastructures. To collectively shape a new approach to global health research funding, it is essential that funders and donors are part of the conversation. This article provides a way to bring funders and donors into the conversation on equity in global health research.
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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 | 0.140 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.037 | 0.063 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.027 | 0.033 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".