Science-based health innovation in sub-Saharan Africa
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".