Basic determinants of child linear growth outcomes in sub-Saharan Africa: a cross-sectional survey analysis of positive deviants in poor households
Bibliographic record
Abstract
Abstract Childhood malnutrition is a significant public health problem confronting countries across the globe. Although there is evidence of a downward trend in undernutrition globally, sub-Saharan Africa did not experience significant improvement in the past decades. This study investigated the basic determinants of linear growth among children living in poor households. We analysed a nationally representative sample of children aged 0–59 months (N = 24,264). The study countries were Ghana, Kenya, the Democratic Republic of Congo (DRC), Nigeria, and Mozambique. The child’s height-for-age Z-scores (HAZ), categorised into HAZ > − 2 standard deviations (SD) (not stunted) and HAZ < − 2 SD (stunted) was the outcome variable of interest. We used logistic regression as our analytical strategy. In DRC, Ghana, Kenya and Nigeria, maternal years of schooling was associated with positive linear growth among children living in poor households. In Ghana and DRC, four antenatal visits had a positive effect on better linear growth, while in Nigeria, healthy maternal body mass index (kg/m2) had a positive effect on child's linear growth. The putative socio-demographic determinants investigated in our study can promote the linear growth of children living in poor households. Interventions aimed at fostering linear growth among children living in poverty should focus on enhancing these factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".