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Record W2555416681 · doi:10.1016/s2214-109x(16)30331-x

Making health systems research work: time to shift funding to locally-led research in the South

2016· article· en· W2555416681 on OpenAlexaboutno aff
Amalia Hasnida, Robert Borst, Anneke M Johnson, Nada R Rahmani, Sabine van Elsland, Maarten Kok

Bibliographic record

VenueThe Lancet Global Health · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal healthEconomic growthHealth equityHealth policyPolitical scienceEquity (law)Developing countryHealth careEconomics

Abstract

fetched live from OpenAlex

This week, the global health systems research community is gathered in Vancouver, Canada, for the Fourth Global Symposium on Health Systems Research. The current movement for health systems research developed out of a need to strengthen health systems in low-income and middle-income countries. More than 25 years ago, the Commission on Health Research for Development published a report that represented a pivotal change in thinking about health research for development.1 The main argument of the report was that research contributed little to health in low-income and middle-income countries, because it matched poorly with needs in the global South, was dominated by researchers from the North, and had a narrow biomedical focus.

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)0.147
metaresearch head score (Gemma)0.117
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.853
Threshold uncertainty score0.777

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1470.117
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0050.006
Science and technology studies0.0200.028
Scholarly communication0.0400.044
Open science0.0070.047
Research integrity0.0330.053
Insufficient payload (model declined to judge)0.0620.020

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 head0.314
GPT teacher head0.524
Teacher spread0.210 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainIncentives
GenreCommentary

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

Citations28
Published2016
Admission routes1
Has abstractyes

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