Science-based health innovation in Ghana: health entrepreneurs point the way to a new development path
Bibliographic record
Abstract
BACKGROUND: Science, technology and innovation have long played a role in Ghana's vision for development, including in improving its health outcomes. However, so far little research has been conducted on Ghana's capacity for health innovation to address local diseases. This research aims to fill that gap, mapping out the key actors involved, highlighting examples of indigenous innovation, setting out the challenges ahead and outlining recommendations for strengthening Ghana's health innovation system. METHODS: Case study research methodology was used. Data were collected through reviews of academic literature and policy documents and through open-ended, face-to-face interviews with 48 people from across the science-based health innovation system. Data was collected over three visits to Ghana from February 2007 to August 2008, and stakeholders engaged subsequently. RESULTS: Ghana has strengths which could underpin science-based health innovation in the future, including health and biosciences research institutions with strong foreign linkages and donor support; a relatively strong regulatory system which is building capacity in other West African countries; the beginnings of new funding forms such as venture capital; and the return of professionals from the diaspora, bringing expertise and contacts. Some health products and services are already being developed in Ghana by individual entrepreneurs, which are innovative in the sense of being new to the country and, in some cases, the continent. They include essential medicines, raw pharmaceutical materials, new formulations for pediatric use and plant medicines at various stages of development. CONCLUSIONS: While Ghana has many institutions concerned with health research and its commercialization, their ability to work together to address clear health goals is low. If Ghana is to capitalize on its assets, including political and macroeconomic stability which underpin investment in health enterprises, it needs to improve the health innovation environment through increasing support for its small firms; coordinating policies; and beginning a dialogue with donors on how health research can create locally-owned knowledge and be more demand-driven. Mobilizing stakeholders around health product development areas, such as traditional medicines and diagnostics, would help to create trust between groups and build a stronger health innovation system.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".