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Record W4400128093 · doi:10.2166/washdev.2024.092

Determinants of diarrhea prevalence among children under 5 years in semi-arid Ghana

2024· article· en· W4400128093 on OpenAlexaff
Cornelius K. A. Pienaah, Yoko Yoshida, Sulemana Ansumah Saaka, Frank Nyongnaah Ategeeng, Isaac Luginaah

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2024
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsWestern University
Fundersnot available
KeywordsDiarrheaAridEnvironmental healthPrevalenceMedicineGeographyBiologyInternal medicineEcologyPopulation

Abstract

fetched live from OpenAlex

ABSTRACT Despite the Sustainable Development Goal (SDG6) of achieving universal access to clean water and sanitation by 2030, many developing countries still face water, sanitation, and hygiene (WASH)-related health issues such as child mortality caused by diarrhea. This study investigated the factors contributing to diarrhea prevalence in rural children, utilizing a cross-sectional survey (n = 517) of smallholder household representatives from a Risk, Attitudes, Norms, Abilities, and Self-Regulation (RANAS) perspective. Using binary logistic regression, the study found that a high prevalence of diarrhea among children was associated with unsafe/open disposal of child feces, living in the poorest households, poor self-rated health, and residing in the Wa East district. Conversely, children from the Brifo ethnicity and those from larger households were less likely to have a high prevalence of diarrhea. These findings underscore the influence of behavioral, socio-cultural, and socioeconomic factors on the prevalence of diarrhea in rural areas. To achieve SDG6, child-friendly sanitation infrastructure, behavior change communication strategies, and incentivizing WASH infrastructure in Ghana and other regions in Sub-Saharan Africa facing similar conditions are recommended.

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 score0.045
Threshold uncertainty score0.172

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.317
Teacher spread0.293 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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