Science-based Health Innovation in Sub-Saharan Africa
Bibliographic record
Abstract
Policy making bodies are increasingly highlighting the important role innovation can play in African development─not only to spur economic growth but also to deliver locally relevant, affordable products and services to African populations. The health sector is one area where innovation is most needed; however, we know very little about the capacity of African countries to innovate in this area. At the same time, a range of conceptual questions have arisen in the academic literature as to the very definition of innovation in an African context, and specifically, the applicability of the National Innovation System (NIS) to African countries. Through detailed case study research of science-based health firms in South Africa, of the NIS health system of Ghana, and by comparing these data with data collected in Uganda and Tanzania, I shed light on these questions from an empirical perspective. I find that science-based health innovation is a complex field, and whilst institutions can help or hinder its viability, the current state of health innovation in SSA can be attributed primarily to individual entrepreneurs with strong networks, who are taking risks in a largely non-enabling environment. I find that, more important for innovation, is the ability to access global knowledge–through appropriate policies and strong partnerships–and the capacity to apply it locally. For this, tacit knowledge, or “learning-by-doing,”’ to respond to consumer demand and achieve regional product penetration, is vital. My results show that the traditional focus on knowledge - or science-heavy innovation - will simply not capture the true extent of health innovation in SSA countries. Furthermore, science-based health innovation is clearly not one thing, and it is, for example, important to understand how plant medicine innovation fit in. The aims, intentions, and impacts of African health research on the countries themselves are rather vague, which constrains innovation at all levels.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".