Funding agencies’ complicity in advancing or impeding equity in and through global health research
Bibliographic record
Abstract
Abstract Background In Global Health Research (GHR), funding policies inform how health research is approached and structured, serving to distribute power and resources in ways that can either reinforce or take action on inequities. In essence, these policies incentivize the focus of GHR and are therefore among the political/structural determinants of health. Despite broad recognition of the need for equity in GHR, the role of funders receives relatively little scrutiny. We sought to better understand the place of equity across the international landscape of GHR funders. Methods Publicly accessible, current strategic (those that set the vision, mission and goals of funding) and operational (those that set the implementation rules and compliance requirements for funded research) policies were harvested from 23 purposely selected Global North and philanthropic funding agencies. Content and discourse analysis were used to examine the portrayal of GHR in inequitable contexts, declarations of equity intentions, and to assess alignment to the six CCGHR equity-centred principles. Results Funding agencies across the Global North made clear declarations on the importance of equity in GHR; however, operationalization was largely absent or incompatible with the best scientific evidence on advancing health equity. Most philanthropic funding agencies failed to make policies public meaning equity action remains unclear. Alignment with equity-centred principles presented in different forms from varying agencies, though no single agency demonstrated good alignment to all six principles. Conclusions Funder discourses suggest equity is a central priority in GHR. Given that funder policies are themselves determinants of equity, stronger alignment between strategic policies, intentions, and action on inequity (e.g., investing in interventional research that acts on upstream determinants) are critically needed for funders of GHR, specifically, and public health research, broadly. Key messages • Funding policy can determine equity possibilities, both in research processes and outcomes. • Coherence between strategic intentions and operational policies is possible, and would promote more equity-advancing 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 | 0.187 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.033 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".