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Record W2034064038 · doi:10.1186/1472-698x-10-s1-s1

Science-based health innovation in sub-Saharan Africa

2010· article· en· W2034064038 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC International Health and Human Rights · 2010
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersGenome CanadaOntario GenomicsUniversity of TorontoUniversity Health NetworkOntario Genomics InstituteBill and Melinda Gates Foundation
KeywordsKenyaEconomic growthTanzaniaPublic healthGross domestic productBusinessPolitical scienceMedicineSocioeconomicsEconomics

Abstract

fetched live from OpenAlex

In recent years emerging markets such as India, China, and Brazil have developed appropriate business models and lower-cost technological innovations to address health challenges locally and internationally. But it is not well understood what capabilities African countries, with their high disease burden, have in science-based health innovation.This gap in knowledge is addressed by this series in BMC International Health and Human Rights. The series presents the results of extensive on-the-ground research in the form of four country case studies of health and biotechnology innovation, six studies of institutions within Africa involved in health product development, and one study of health venture funds in Africa. To the best of our knowledge it is the first extensive collection of empirical work on African science-based health innovation.The four country cases are Ghana, Rwanda, Tanzania and Uganda. The six case studies of institutions are A to Z Textiles (Tanzania), Acorn Technologies (South Africa), Bioventures venture capital fund (South Africa), the Malagasy Institute of Applied Research (IMRA; Madagascar), the Kenyan Medical Research Institute (KEMRI; Kenya), and Niprisan's development by Nigeria's National Institute for Pharmaceutical Research and Development and Xechem (Nigeria).All of the examples highlight pioneering attempts to build technological capacity, create economic opportunities, and retain talent on a continent significantly affected by brain drain. They point to the practical challenges for innovators on the ground, and suggest potentially helpful policies, funding streams, and other support systems.For African nations, health innovation represents an opportunity to increase domestic capacity to solve health challenges; for international funders, it is an opportunity to move beyond foreign aid and dependency. The shared goal is creating self-sustaining innovation that has both health and development impacts. While this is a long-term strategy, this series shows the potential of African-led innovation, and indicates how it might balance realism against opportunity. There is ample scope to learn lessons more systematically from cases like those we discuss; to link entrepreneurs, scientists, funders, and policy-makers into a network to share opportunities and challenges; and ultimately to better support and stimulate African-led health innovation.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.334
Teacher spread0.306 · 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