Risk factors for stunting among children under five years: a cross-sectional population-based study in Rwanda using the 2015 Demographic and Health Survey
Bibliographic record
Abstract
BACKGROUND: Child growth stunting remains a challenge in sub-Saharan Africa, where 34% of children under 5 years are stunted, and causing detrimental impact at individual and societal levels. Identifying risk factors to stunting is key to developing proper interventions. This study aimed at identifying risk factors of stunting in Rwanda. METHODS: We used data from the Rwanda Demographic and Health Survey (DHS) 2014-2015. Association between children's characteristics and stunting was assessed using logistic regression analysis. RESULTS: A total of 3594 under 5 years were included; where 51% of them were boys. The prevalence of stunting was 38% (95% CI: 35.92-39.52) for all children. In adjusted analysis, the following factors were significant: boys (OR 1.51; 95% CI 1.25-1.82), children ages 6-23 months (OR 4.91; 95% CI 3.16-7.62) and children ages 24-59 months (OR 6.34; 95% CI 4.07-9.89) compared to ages 0-6 months, low birth weight (OR 2.12; 95% CI 1.39-3.23), low maternal height (OR 3.27; 95% CI 1.89-5.64), primary education for mothers (OR 1.71; 95% CI 1.25-2.34), illiterate mothers (OR 2.00; 95% CI 1.37-2.92), history of not taking deworming medicine during pregnancy (OR 1.29; 95%CI 1.09-1.53), poorest households (OR 1.45; 95% CI 1.12-1.86; and OR 1.82; 95%CI 1.45-2.29 respectively). CONCLUSION: Family-level factors are major drivers of children's growth stunting in Rwanda. Interventions to improve the nutrition of pregnant and lactating women so as to prevent low birth weight babies, reduce poverty, promote girls' education and intervene early in cases of malnutrition are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".