The Prevalence and Associated Factors of Protein Energy Malnutrition among under Five Year Children in Pediatric OPDs of Public Health Institution in Yirgalem Town, Southern Ethiopia, 2017
Bibliographic record
Abstract
Introduction: Protein energy malnutrition is one of the leading causes of morbidity and mortality in children under the age of five in developing countries. Ethiopia being one of these countries malnutrition is an important public health problem and among the highest in the world Objectives: The main objective of this study is to assess the prevalence and associated factors of protein energy malnutrition among under-five children in pediatric OPDs of public health institutions at Yirgalem town, Dale woreda, Sidama zone, SNNPR, Ethiopia from September 18-26, 2017. Methodology: Institutional based cross sectional study was conducted in pediatric OPDs of public health institutions in Yirgalem town, on a total of 204 children age less than five years from September 18-26, 2017. Study subjects were selected by using quota sampling technique. Interviewer administered structured questionnaire and anthropometric measurements were used to collect data. Data was analysed using SPSS version 20.0statistical software. Result: This study indicated that the prevalence of stunting wasting and Under-weight was 41.2%, 1.7% and 25% respectively. Factors showed statistically significant association in multivariate analysis were family planning use by mother, children eat together with older siblings and exclusive breast feeding with P-value (0.02), (0.036) and (0.002) respectively. Conclusion: This study reviled that the prevalence of stunting, wasting and under-weight were higher among under-five children participated in this study. Thus children are at a higher risk of under nutrition related morbidity and mortality. Further progress in under nutrition prevention can be achieved by specifically targeting children at their early age and conducting tailored public education to improve the nutritional status of the study subjects.
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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 | 0.000 | 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 | 0.000 | 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".