CHILD NUTRITIONAL STATUS IN EGYPT: A COMPREHENSIVE ANALYSIS OF SOCIOECONOMIC DETERMINANTS USING A QUANTILE REGRESSION APPROACH
Bibliographic record
Abstract
This study examined the underlying demographic and socioeconomic determinants of child nutritional status in Egypt using data from the most recent round of the Demographic and Health Survey. The height-for-age Z-score (HAZ) was used as a measure of child growth. A quantile regression approach was used to allow for a heterogeneous effect of each determinant along different percentiles of the conditional distribution of the HAZ. A nationally representative sample of 13,682 children aged 0-4 years was drawn from the 2014 Egypt DHS. The multivariate analyses included a set of HAZ determinants commonly used in the literature. The conditional and unconditional analyses revealed a socioeconomic gradient in child nutritional status, in which children of low income/education households have a worse HAZ than those from high income/education households. The results also showed significant disparities in child nutritional status by demographic and social characteristics. The quantile regression results showed that the association between the demographic and socioeconomic factors and HAZ differed along the conditional HAZ distribution. Intervention measures need to consider the heterogeneous effect of the determinants of child nutritional status along the different percentiles of the HAZ distribution. There is no one-size-fits-all policy to combat child malnutrition; a multifaceted approach and targeted policy interventions are required to address this problem effectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".