Disparities in Child Survival in Ethiopia: Evidence From Nationally Represented Data
Bibliographic record
Abstract
Abstract Background: Even though Ethiopia has made considerable progress in improving child health and survival, the country is experiencing one of the highest infant and under 5 mortality rates. The purpose of this study was to examine the disparities in child health and survival in Ethiopia.Method: Data were drawn from the 2016 Ethiopian Demographic and Health Surveys (EDHS). Proportional odds regression was used to identify the determinants of poor child health and survival outcomes. The Mosley and Cohen’s child health framework was used to measure child survival. Results: The proportion of both poor health and mortality were high. The likelihood of falling into the poor health and survival category increases for: male children; children born with preceding birth interval of <18 months; those never breastfed; born to mothers having higher deprivation index or having poor health service utilization score; poor diet diversity score; living in a household with non-improved toilet facility; having a father with low education level; and those living in a community where mothers’ education is low (p<0.05). Conclusion: Inequalities hamper Ethiopia’s true progress in improving child health and survival. Given the fact that nearly two-thirds of Ethiopian women have no education and half live in financially disadvantaged households, this study recommends aggressive intervention in promoting women’s status at the grassroots level through community education and behavioral communication strategies that will eventually help to significantly reduce huge disparity in early mortality in the population.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".