Patterns in the Risk Factors of Stunting Among Children 0 to 24 and 25 to 59 Month Old in Ethiopia: Evidence from the 2016 National Survey (P10-004-19)
Bibliographic record
Abstract
The objective of this study was to examine the prevalence and patterns of individual and household level factors on stunting among two groups of under five children in Ethiopia. The study employed a quantitative cross-sectional design based on a nationally representative data. It included 4199 young children (age 0–24 months) and 5497 children (age 25–59 months), giving a total of 9696 children. Prevalence of stunting was 29% among the younger age group (age 0–24 months) and 47% among the older group (age 25–59 months). Being female, intake of multiple micronutrients, households having piped source of drinking water, high maternal Body Mass Index (BMI), household non-monetary wealth, and maternal education were associated with decreased likelihood of stunting in both groups. On the other hand, children who were anemic, small birth weight, drank from bottle, and children of stunted and working mother resulted in higher likelihood of stunting in both groups (P < 0.05). While most predictors and/or risk factors of stunting followed similar pattern across the two groups, child factors had higher leverage in the younger than the older groups. Multiple set of factors predicted childhood stunting among the young and older children in Ethiopia. The study underscores the importance of intervening in the first 1000 days through promoting maternal education, maternal-child health services, mother’s nutrition, and improving the intra household food distribution and good eating habits. No source of funding.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".