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Record W4280633444 · doi:10.1186/s12961-022-00859-6

Evaluating official development assistance-funded granting mechanisms for global health and development research that is initiated in high-income countries

2022· article· en· W4280633444 on OpenAlexafffund
Adèle Cassola, Prativa Baral, John‐Arne Røttingen, Steven J. Hoffman

Bibliographic record

VenueHealth Research Policy and Systems · 2022
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsCentre for Global Health ResearchMcMaster UniversityYork University
FundersCanadian Institutes of Health ResearchNorges ForskningsrådNational Science FoundationVetenskapsrådetNederlandse Organisatie voor Wetenschappelijk OnderzoekSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUK Research and Innovation
KeywordsGlobal healthContext (archaeology)Health services researchEconomic growthPublic healthDeveloping countrySustainable developmentInternational developmentGlobal public goodHealth policyBusinessPolitical scienceHealth carePublic goodMedicineEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Several countries allocate official development assistance (ODA) for research on global health and development issues that is initiated in the donor country. The integration of such research within domestic research systems aligns with efforts to coordinate ODA investments with science, technology and innovation policies towards achieving the Sustainable Development Goals (SDGs). METHODS: Through a document synthesis and interviews with research funders in ODA donor and recipient countries, we evaluated the performance of this funding approach across seven donor-country programmes from five donor countries and examined the institutional design elements that increase its chances of advancing development goals and addressing global challenges. RESULTS: We found that carefully designed programmes provide a promising pathway to producing valuable and contextually relevant knowledge on global health and development issues. To achieve these outcomes and ensure they benefit ODA-receiving countries, programmes should focus on recipient-country priorities and absorptive capacity; translate research on global public goods into context-appropriate technologies; plan and monitor pathways to impact; structure equitable partnerships; strengthen individual and institutional capacity; and emphasize knowledge mobilization. CONCLUSIONS: Global health and development research programmes and partnerships have an important role to play in achieving the SDGs and addressing global challenges. Governments should consider the potential of ODA-funded research programmes to address gaps in their global health and development frameworks. In the absence of concrete evidence of development impact, donor countries should consider making increases in ODA allocations for research additional to more direct investments that have demonstrated effectiveness in ODA-receiving countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.581
metaresearch head score (Gemma)0.610
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.517

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5810.610
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.012
Science and technology studies0.0060.007
Scholarly communication0.0160.011
Open science0.0070.017
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.553
GPT teacher head0.577
Teacher spread0.023 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainIncentives
GenreEmpirical

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".

Quick stats

Citations9
Published2022
Admission routes2
Has abstractyes

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