Commercialization of Health Products from Sub-Saharan Africa: Challenges and Opportunities
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».