Severe Acute Malnutrition and Feeding Practice of Children Aged 6-59 Months in Pastoral Community, Afar, Ethiopia: Descriptive Cross-Sectional Study
Bibliographic record
Abstract
Background: Severe acute malnutrition remains one of the most common causes of morbidity and mortality among children in developing countries, including Ethiopia. Knowing the local burden of SAM has huge importance for public health interventions. Therefore this study aimed to assess the level of severe acute malnutrition and feeding practice of children aged 6–59 months in Abaa’la district, Afar, Northeast, Ethiopia. Methods: Community-based descriptive cross-sectional study was conducted on 422 mother-child pairs of children aged 6–59 months. Kebeles were selected randomly after stratifying the district in to urban and rural, and study participants were selected using a cluster sampling technique. Data were collected using an interviewer-administered questionnaire, and child nutritional status was measured using WHO Mid upper arm circumference measuring tape. Data were entered into Epi data version 3.1 and exported to SPSS version 22 for analysis. The result was presented using Descriptive statistics. Results: The prevalence of severe acute malnutrition (SAM) was found to be 4.3% (95% CI, 2.3-6.1%) and that of moderate acute malnutrition (MAM) was 21.1 %. Almost all (98.8%) of children were ever breastfed. Prelacteal feeding and bottle feeding was practiced by 31% and 33.9% of children, respectively. Only 68.5% of children were feed colostrum. Around 45.5% of children were exclusively breastfed for the first six months, and 70.4% of children wean breastfeeding before the age of two years. Conclusion: The prevalence of severe acute malnutrition in the study area was lower than the regional figures, but still, it is a public health priority. There are improper child care and feeding practices. Therefore, public health interventions that can improve those practices should be strengthened.
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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 | 0.001 | 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".