Water, sanitation, and hygiene practices among rural households and related health impacts: a case study from some North Indian villages
Bibliographic record
Abstract
Abstract The present study was carried out to assess the water availability, hygiene practices, and sanitary conditions in the households of open defecation-free (ODF) villages after achieving ODF status. Monitoring was conducted from 360 households of 9 ODF villages from 3 blocks of the Kurukshetra district of north India, using a questionnaire. The results interpreted that 78.33% of the surveyed population were using water supplied from government borewells and 65.55% of respondents agreed that their water is safe for drinking as they did not have any water-related diseases. Many respondents (57.2%) replied that someone in their family had suffered from waterborne disease in the preceding year. About 42.8% of households were treating their drinking water at the house level through methods like boiling, chlorination, and reverse osmosis systems. 90.8% of respondents said that they have access to a functional latrine facility. However, 5% population responded that they still prefer fields for open defecation (OD). Logistic regression results showed that the presence of garbage or litter and stagnant water near the household were associated with an increased risk of disease occurrence among the households. The findings suggested that people should maintain good sanitation and hygiene in their household surroundings to avoid health problems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".