Science-based health innovation in Rwanda: unlocking the potential of a late bloomer
Bibliographic record
Abstract
BACKGROUND: This paper describes and analyses Rwanda's science-based health product 'innovation system', highlighting examples of indigenous innovation and good practice. We use an innovation systems framework, which takes into account the wide variety of stakeholders and knowledge flows contributing to the innovation process. The study takes into account the destruction of the country's scientific infrastructure and human capital that occurred during the 1994 genocide, and describes government policy, research institutes and universities, the private sector, and NGOs that are involved in health product innovation in Rwanda. 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 38 people from across the science-based health innovation system. Data was collected over two visits to Rwanda between November - December 2007 and in May 2008. A workshop was held in Kigali on May 23rd and May 24th 2009 to validate the findings. A business plan was then developed to operationalize the findings. RESULTS AND DISCUSSION: The results of the study show that Rwanda has strong government will to support health innovation both through its political leadership and through government policy documents. However, it has a very weak scientific base as most of its scientific infrastructure as well as human capital were destroyed during the 1994 genocide. The regulatory agency is weak and its nascent private sector is ill-equipped to drive health innovation. In addition, there are no linkages between the various actors in the country's health innovation system i.e between research institutions, universities, the private sector, and government bureaucrats. CONCLUSIONS: Despite the fact that the 1994 genocide destroyed most of the scientific infrastructure and human capital, the country has made remarkable progress towards developing its health innovation system, mainly due to political goodwill. The areas of greatest potential for Rwanda are in traditional plant technologies. However, there is need for investments in domestic skill development as well as infrastructure that will enhance innovation. Of foremost importance is the establishment of a platform to link the various actors in the 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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".