Getting a GRiPP on everyday schistosomiasis: experience from Zimbabwe
Bibliographic record
Abstract
Schistosomiasis, commonly known as bilharzia, is a parasitic disease prevalent in Africa, Asia and South America. The majority of the cases occur in Sub-Saharan Africa where schistosomiasis is a major public health problem impacting on child health and development as well as adult health when infections become chronic. Control of schistosomiasis is by treatment of infected people with the antihelminthic drug praziquantel. Current schistosome control programmes advocated by the World Health Assembly in 2001 are aimed at regular school-based integrated deworming strategies in order to reduce development of severe morbidity, promote school health and to improve cognitive potential of children. Several countries in Africa have now embarked on national scale deworming programmes treating millions of children exposed to schistosomiasis in endemic areas without prior diagnosis of infection through mass drug administration programmes. Implementing such control programmes requires a concerted effort between scientists, policy makers, health practitioners and several other stake holders and of course a receptive community. This paper considers the contributions to global schistosome control efforts made by research conducted in Zimbabwe and the historical context and developments leading to the national schistosomiasis control programme in Zimbabwe giving an example of Getting Research into Policy and Practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".