Promoting equitable global health research: a policy analysis of the Canadian funding landscape
Bibliographic record
Abstract
BACKGROUND: Recognising radical shifts in the global health research (GHR) environment, participants in a 2013 deliberative dialogue called for careful consideration of equity-centred principles that should inform Canadian funding polices. This study examined the existing funding structures and policies of Canadian and international funders to inform the future design of a responsive GHR funding landscape. METHODS: We used a three-pronged analytical framework to review the ideas, interests and institutions implicated in publically accessible documents relevant to GHR funding. These data included published literature and organisational documents (e.g. strategic plans, progress reports, granting policies) from Canadian and other comparator funders. We then used a deliberative approach to develop recommendations with the research team, advisors, industry informants and low- and middle-income country (LMIC) partners. RESULTS: In Canada, major GHR funders invest an estimated CA$90 M per annum; however, the post-2008 re-organization of funding structures and policies resulted in an uncoordinated and inefficient Canadian strategy. Australia, Denmark, the European Union, Norway, Sweden, the United Kingdom and the United States of America invest proportionately more in GHR than Canada. Each of these countries has a national strategic plan for global health, some of which have dedicated benchmarks for GHR funding and policy to allow funds to be held by partners outside of Canada. Key constraints to equitable GHR funding included (1) funding policies that restrict financial and cost burden aspects of partnering for GHR in LMICs; and (2) challenges associated with the development of effective governance mechanisms. There were, however, some Canadian innovations in funding research that demonstrated both unconventional and equitable approaches to supporting GHR in Canada and abroad. Among the most promising were found in the International Development Research Centre and the (no longer active) Global Health Research Initiative. CONCLUSION: Promoting equitable GHR funding policies and practices in Canada requires cooperation and actions by multiple stakeholders, including government, funding agencies, academic institutions and researchers. Greater cooperation and collaboration among these stakeholders in the context of recent political shifts present important opportunities for advancing funding policies that enable and encourage more equitable investments in GHR.
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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 | 0.060 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.040 | 0.018 |
| Scholarly communication | 0.027 | 0.007 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".