Predictive Model of Stunting in Children 6-59 Months of Age in Kirundo Health District, Burundi
Bibliographic record
Abstract
An analytical cross-sectional study was conducted among a randomly selected sample of 374 households with at least one child aged 6 to 59 months in the Kirundo health district, Burundi. Sociodemographic, socioeconomic, socio-sanitary factors, food insecurity, behavioral, and environmental data were collected using a structured questionnaire. Children's weight was measured using a standard procedure (SECA scale), their height using a standard UNICEF height rod, and their 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 with a significance level less than or equal to 20% in the bivariate analysis were included to explore factors associated with stunting in children aged 6 to 59 months. In this study, the prevalence of stunting is estimated at 61.5%. According to multivariate logistic regression, sex (AOR = 2.83; 95% CI:1.40-5.75), age (AOR= 10.40; 95% CI: 1.21-88.30), food insecurity (AOR = 10.47;95% CI: 3.58-30.61), latrine type (AOR = 6.83; 95% CI: 3.12-14.94), diarrhea (AOR = 2.56; 95% CI: 1.19-5.48), water source (AOR = 3.17; 95% CI: 1.54-6.52), media exposure (AOR = 0.24, 95% CI: 0.11-0.51), nutritional knowledge (AOR = 0.11; 95% CI: 0.05-0.25), birth spacing (AOR = 0.39, 95% CI: 0.16-0.93), complete vaccination (AOR = 0.06; 95% CI: 0.02-0.21), father's occupation (AOR = 0.25; 95% CI: 0.09-0.72), and mother's education (AOR = 0.21; 95% CI: 0.07-0.64) were significantly associated with stunting. The predictive model showed an area under the curve (AUC) of 0.95, indicating excellent discrimination ability. The high prevalence of stunting in this study highlights the importance of urgent action to end this problem.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".