Multilevel Analysis of Determinants of Stunting Prevalence among Children under Age Five in Ethiopia
Bibliographic record
Abstract
Background: Stunting is a well-established child health indicator of chronic malnutrition related to environmental and socio-economic circumstances. In Ethiopia, childhood stunting is the most widely prevalent among children under the age of five years. Objective: To estimate the prevalence of stunting and model the determinants of stunting prevalence among children under age five in Ethiopia. Methods: Data were extracted from 2016 EDHS, and samples of 8487 children under age five were used in this study. The sample was selected using a two-stage stratified sampling process, and a multilevel logistic regression model was used to determine the factors associated with childhood stunting in Ethiopia. Results: This study revealed that the prevalence of stunting among children under age five years in Ethiopia was around 39.39%. The multilevel binary logistic regression analysis was performed to investigate the variation of predictor variables of stunting prevalence among children under age five. Accordingly, it has been identified that the ages of the child above 12 months, male gender, children from poor households, and no mother education significantly affect the prevalence of stunting in Ethiopia. It is found that variances related to the random term were statistically significant, implying a variation in the prevalence of stunting across Ethiopia's regional states. Conclusion: The current study confirmed that the prevalence of stunting among children under aged five years in Ethiopia was a severe public health problem. Therefore, governmental or stakeholders should pay attention to all the significant factors mentioned in this study's analysis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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".