Science-based health innovation in Tanzania: bednets and a base for invention
Bibliographic record
Abstract
BACKGROUND: Tanzania is East Africa's largest country. Although it is socially diverse, it has experienced general political stability since independence in 1964. Despite gradual economic development and Tanzania's status as one of the biggest recipients of aid in Africa, health status remains poor. This paper explores Tanzania's science-based health innovation system, and highlights areas which can be strengthened. METHODS: Qualitative 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 52 people from across the science-based health innovation system over two visits to Tanzania from July to October 2007. RESULTS AND DISCUSSION: Tanzania has a rich but complex S&T governance landscape, with the public sector driving the innovation agenda through a series of different bodies which are not well-coordinated. It has some of the leading health research on the continent at the University of Dar es Salaam, Muhimbili University of Health and Applied Sciences, the National Institute for Medical Research and the Ifakara Medical Institute, with strong donor support. Tanzania has found developing an entrepreneurial culture difficult; nevertheless projects such as the clusters initiative at the University of Dar es Salaam are encouraging low-tech innovation and overcoming knowledge-sharing barriers. In the private sector, one generics company has developed a South-South collaboration to enable technology transfer and hence the local production of anti-retrovirals. Local textile company A to Z Textiles is now manufacturing 30 million insecticide impregnated bednets a year. CONCLUSIONS: To have a coherent vision for innovation, Tanzania may wish to address some key issues: coordination across stakeholders involved with health research, increasing graduates in health-related disciplines, and building capabilities in biological testing, preclinical testing, formulation and standardization, and related areas important to moving from basic research to applications. The private sector can be encouraged to innovate through improved access to financing, and incentives for R&D. The diaspora community represents an untapped source for partnerships and access to other developing world markets and technology. The government may wish to set up mechanisms to encourage south-south collaborations, and to bring the public and private sector together around specific projects to help realize the country's innovation potential.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".