Commercialization of Health Products from Sub-Saharan Africa: Challenges and Opportunities
Bibliographic record
Abstract
Despite the global progress made in improving health of people and increasing the life expectancy, Sub-Saharan Africa continues to be plagued by many health problems. Commercialization of health products from Sub-Saharan Africa presents opportunities to solve some of these health problems as well as generate economic returns. This thesis explored science based health product commercialization in sub-Saharan Africa through three studies. The objective was to identify opportunities and challenges facing health product commercialization in Sub-Saharan Africa. A qualitative case study approach was used and data collected using interviews. The first study involved looking at science based health product commercialization at a national level. Rwanda was chosen for this study. Thirty eight key informants selected from various institutions that form the health innovation system in Rwanda were interviewed. The results of the study show that opportunities exist in Rwanda for health product commercialization mainly because of the strong political will to support health innovation. However the main challenge is that there are no linkages between the actors involved in health innovation in Rwanda. The second study looked at health innovation at the level of a research institution. The Kenya Medical Research Institute (KEMRI) was studied where eight key informants were interviewed. The results show that KEMRI faced many challenges in its attempt at health product development, including shifting markets, lack of infrastructure, inadequate financing, and weak human capital with respect to innovation. However, it overcame them through diversification, partnerships and changes in culture. The third study looked at health technologies that are being developed in sub-Saharan Africa but have stagnated in laboratories. Thirty nine key informants were interviewed. A total of 25 technologies were identified, the majority being traditional plant medicines; other technologies identified included diagnostic tests and medical devices. Many of these technologies require further validation. Other key challenges to commercialization of these technologies that were identified included a lack of innovative culture amoung scientists and policy makers and lack of proof of concept funds including venture capital. Overall, this thesis identified opportunities for science based health commercialization in Africa, and also provides recommendations on how to overcome major challenges.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".