Demographic and Social-Economic Determinants of Malnutrition among Children (0-23 Months Old) in Kenya
Bibliographic record
Abstract
Objective: To identify the demographic, social, and economic determinants of malnutrition in Kenya's children aged 0-23 months. Methods: Data from the Kenya Demographic and Health Survey (KDHS), a nationally representative cross-sectional study conducted in 2014/2015, were used in this study. Data from children 0-23 months old with complete information on weight, height, age, and sex were used for analysis. Height for Age Z scores (HAZ), Weight for Height Z scores (WHZ), and BMI for age Z scores (BAZ) was determined using WHO guidelines to determine the nutritional status of the children. Chi-square statistics were used to determine the relationship between social-economic status and place of residence indicators and the nutritional status of the children. Significance was set at p <0.05. Results: Among all participating (n=7578), 22.7% were stunted (HAZ < -2), 6.2% were wasted (WHZ < -2), and 6.1% were either overweight or obese (BAZ > 2). Wasting and stunting were significantly higher in children from rural areas, poorer wealth index, and mothers with no education. In contrast, children from urban areas, the richest wealth index category, and mothers with secondary or higher education were significantly more likely to be overweight or obese. Conclusion: Current and future policies and programs to curb malnutrition in Kenya need to target specific needs of children based on their social-economic status, area of residence, and other demographic characteristics that were identified as determinants of child malnutrition instead of using a general approach.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".