Factors Associated with Underweight in Children Aged 6-59 Months in the Kibuye Health District, Gitega Health Province, Burundi
Bibliographic record
Abstract
The study, conducted in the Kibuye health district in Burundi, aimed to investigate the factors associated with underweight children aged 6-59 months. An analytical cross-sectional study was conducted on a sample of 273 households with at least one randomly selected child aged 6-59 months in the Kibuye, Burundi health district. Socio-demographic, socio-economic, child morbidity, behavioral, and environmental data were collected using a structured questionnaire. Children's weight was measured using a standard procedure (seca scale), height was measured using a UNICEF standard height board, and age was obtained from the birth certificate. Anthropometric data were analyzed using Emergency Nutrition Assessment (ENA for Smart) software. Modeling was performed using logistic regression to eliminate confounding factors, and all independent variables whose significance level was less than or equal to 20% in the bivariate analysis were included to explore factors associated with underweight children aged 6-59 months. In this study, the prevalence of underweight was 32.9%. After multivariate analysis, child age (OR=7.82, 95% CI = [2.21-27.6]), child gender (OR=2.61, 95% CI = [1.31-5.23]), maternal education level (OR=0, 32, 95% CI = [0.15-0.65]), exclusive breastfeeding (OR=0.28, 95% CI = [0.13-0.60]), latrine type (OR=8.08, 95% CI = [1.06-61.38]), water source (OR=2.76, 95% CI = [1.33-5.74]), mothers' knowledge of a balanced diet (OR=0.23, 95% CI = [0.08-0.64]) and knowledge of the consequences of malnutrition (OR= 0.21, 95% CI = [0.07-0.61]) were identified as factors significantly associated with underweight children aged 6-59 months in the Kibuye health district.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".