Spatial variations and determinants of childhood diarrhea management in Uganda
Bibliographic record
Abstract
The study examines the variability of community-based and determinants of childhood diarrhea management including rehydration and feeding therapies using the 2016 Uganda Demographic and Health Survey, UDHS (N = 2,923). The study utilized the Bayesian model and geo-statistical techniques with location (district) and nonlinear metrical attributes (mother’s and child’s age) to gain a better understanding of childhood diarrhea management. The results show that 45% and 58% of under-5 children received less than the usual amount of fluid and food, respectively, during diarrheal episodes. However, the findings indicate that the prevalence of diarrhea among under-5 children does vary spatially within and between subregions and districts of Uganda. The fixed effects show that the covariates have no significant influence on rehydration therapy. However, the wealth index, family size, and number of under-5 children in a household have a significant impact on feeding therapy for children with diarrhea. In general, the results indicate that geography has a significant effect on the rehydration therapy, while both geography and socioeconomic variables have a significant influence on feeding therapy on under-5 children with diarrhea. These findings can support policymakers to identify subregions and districts with ineffective practices and policy strategies to better address the spatial variations and determinants of diarrhea management in Uganda.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".