Risk Factors and Trends in Childhood Stunting in a District in Western Uganda
Bibliographic record
Abstract
OBJECTIVES: This representative, cross-sectional study conducted in Kabarole District, Western Uganda, determined the nutritional status of children 6-59 months of age and established a trend in the childhood stunting rates. METHODS: A multi-stage random cluster sampling was performed to select 322 children and their principal caregivers. Anthropometric measurements were taken from the children and compared with a reference population and the children's principle caregivers were interviewed. RESULTS: Childhood stunting was high with 43.0% of all children having a z-score of less than or equal to -2. Predictive factors for stunting were a low economic status of the household, poor health of the child's caregiver, residence located at a long distance from a health unit and use of a contaminated water source. The comparison of our study results with an earlier nutritional study in Kabarole District revealed that there is an increasing trend of childhood stunting over the years of 28.0% [95% confidence interval (CI) 22.1-33.1%] in 1989 vs 43% (95% CI 37.6-48.8%) in 2006 and that stunting rates in Kabarole District were much higher compared to national data. CONCLUSION: The high stunting rates in children and the increasing trend in stunting needs further investigations. It should also be determined why stunting rates in children in Kabarole District are much higher than the national average. There is a need for better nutritional interventions as part of the district's public health programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".