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Record W4380987917 · doi:10.2166/aqua.2023.199

Water, sanitation, and hygiene practices among rural households and related health impacts: a case study from some North Indian villages

2023· article· en· W4380987917 on OpenAlexaff
Anjali Malan, Meenakshi Suhag, Pankaj Kumar Gupta, Hardeep Rai Sharma

Bibliographic record

VenueAQUA - Water Infrastructure Ecosystems and Society · 2023
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Waterloo
FundersKurukshetra University
KeywordsOpen defecationSanitationHygieneLatrineEnvironmental healthPopulationGarbageHand washingSocioeconomicsMedicineToiletGeographyWaste management

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.265
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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