The use of non-pharmaceutical interventions for the prevention and control of schistosomiasis in sub-Saharan Africa: A systematic review
Bibliographic record
Abstract
Schistosomiasis remains a major cause of global parasitic morbidity. Current control strategies focus on pharmaceutical approaches using Mass Drug Administration (MDA) to distribute praziquantel in endemic areas of sub-Saharan Africa. Our paper systematically reviewed the literature on non-pharmaceutical interventions for enhanced schistosomiasis control. We conducted a systematic review of peer-reviewed English language literature using PubMed, Embase and Web of Science. Our search terms were limited to the year 2000 to March 2019 to reflect the period of the Millennium and Sustainable Development Goals. We initially identified 1733 publications, which were reduced to 1324 after screening by title and abstract. After the inclusion and exclusion criteria, a total of 1312 studies were excluded. Following this, we had a total of 12 articles, which we later screened by full text. Out of the twelve articles, seven were excluded for being systematic reviews or examining clinical and nutritional aspects of schistosomiasis control. We finally remained with five studies that met our inclusion criteria. Our paper indicates a gap in non-pharmaceutical based interventions for schistosomiasis control. We propose that future research addresses this gap by engaging communities in participatory approaches such as environmental sanitation, Water, Sanitation and Hygiene (WASH), health education and economic empowerment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".