Determinants of diarrhea prevalence among children under 5 years in semi-arid Ghana
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".