Trends in the Prevalence and Associated Contributing Factors of Stunting in Children Under Five Years of Age. Secondary Data Analysis of 2005, 2010 and 2014-2015 Rwanda Demographic and Health Surveys
Bibliographic record
Abstract
Background Stunting affects more than 161 million children under five years of age worldwide. Rwanda has a high prevalence of stunted children under five years of age (~38%) according to the 2014-2015 Rwanda Demographic and Health Survey. Objectives The aim of this study is to compare the prevalence rates of stunting in Rwanda using the Rwanda Demographic and Health Survey data of 2005, 2010 and 2014-2015. Methods The three Rwanda Demographic and Health Survey cross-sectional studies into consideration were conducted in 2005, 2010 and in 2014-2015. Stunting prevalence rates from those surveys were compared using Pearson's chi-squared tests and Marascuilo procedure using STATA (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP.). Results The Pearson's chi-squared tests and Marascuilo procedure used in this research confirmed a significant difference between the reported three RDHS stunting prevalence rates. The trends in the stunting prevalence rates among children under five years of age showed a decrease of 13% in stunting prevalence rate, falling from 51%in 2005 to 38%in 2014-15. Conclusion A statistical analysis based on2005, 2010 and 2014-15 RDHS surveys datasets confirmed that there is a statistically significant reduction in stunting prevalence rates over that decade(from 51% in 2005 to 38%in 2014-2015). The main persistent associated factors with stunting were the age, sex, size at birth, residence place of the child, and the mother’s educational level and household wealth index. Keywords: Stunting; children under five years; demographic and health survey; nutrition; Rwanda
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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) | 0.001 | 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 | 0.001 |
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".