Cross-Sectoral Engagement in the Eradication of Schistosomiasis in Indonesia
Bibliographic record
Abstract
BACKGROUND: Indonesia has planned a roadmap to eradicate schistosomiasis and achieved the elimination of schistosomiasis by 2025. Through cooperation between the Ministry of Health and the Ministry of National Development Planning or the National Development Planning Agency (Bappenas). The roadmap is a reference to plan the shared action multiple sectors, central-regional and communal coordinated by the National Development Planning Agency (Bappenas) and Development Planning Agency at Sub-national Level (Bappeda). OBJECTIVE: analyzing cross-sectoral involvement in 2019 in efforts to eradicate schistosomiasis. The research method is to analyze data and information regarding the schistosomiasis control program in 2019. MATERIAL AND METHODS: The data and information in the study came from six Regional Apparatus Organizations (OPD) in Poso and seven OPDs in Sigi Central Sulawesi. RESULT: This study reveals that, based on the roadmap to eradicate schistosomiasis, mass treatment regarding schistosomiasis for humans is 70-94%; mass medication for livestock is 50%; surveillance on intermediate snails, humans, and animals is 70-94%; 6,000 animals and 49%; the campaigns for behavioral changes and an increase in community participation in 18 villages and multi-sector coordination and intensive integrated supervision is 50%. Meanwhile, public toilets in the focus areas and livestock management have not proceeded. The prevalence of schistosomiasis in humans showed yields of 0.13%, 0%, and 0.0% in the Napu, Bada, and Lindu Plateaus. In addition, the prevalence of schistosomiasis in animals was 3.4% and 2.3% in buffalo and horses. CONCLUSION: Schistosomiasis control in terms of health can reduce the prevalence of schistosomiasis in humans. And schistosomiasis control is not a priority program in terms of agriculture. Who did not build schistosomiasis control programs in 2019 upon good coordination between the central and local governments?
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".