Community-Led Total Sanitation: Conceptual Approach to Intestinal Parasites Control in Rural Areas, Cote d’Ivoire
Bibliographic record
Abstract
Lack of appropriate sanitation, with poor hygiene and unsafe water, are sources of the spread of diseases. Ongoing efforts to control neglected tropical diseases, including helminth and intestinal protozoan infections, must be maintained and strengthened with new approaches. The aim of this study was to test the adherence of communities to the Community-Led Total Sanitation (CLTS) approach. The study was conducted in three (3) departments in south- central Côte d'Ivoire. In practice, the process of implementing CLTS involves 5 major steps: i) Mapping of defecation areas, ii) Calculating of human fecal matter quantity and medical costs, iii) Walk of shame, iv) Analysis of contamination pathways, v) Community decision making and latrine construction. Overall, latrine coverage and usage rates have increased considerably in the intervention localities. In particular, out of the 26 localities where the CLTS was applied, 11 reached a latrine coverage rate higher than 80%, 6 of which reached a 100% coverage rate. The results of this work should be used to raise awareness in rural communities about the importance of building and using latrines. Furthermore, CLTS implemented on a large scale can contribute to achieving Goal 3 and 6 of the Sustainable Development Goals (SDGs).
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".