The association of parental education with childhood undernutrition in low- and middle-income countries: comparing the role of paternal and maternal education
Bibliographic record
Abstract
Background: Most existing research on the association of parental education with childhood undernutrition focuses on maternal education and often ignores paternal education. We systematically investigate differences in maternal and paternal education and their association with childhood undernutrition. Methods: One hundred and eighty Demographic and Health Surveys from 62 countries performed between 1990 and 2014 were analysed. We used linear-probability models to predict childhood undernutrition prevalences, measured as stunting, underweight and wasting, for all combinations of maternal and paternal attainment in school. Models were adjusted for demographic and socio-economic covariates for the child, mother and household, country-level fixed effects and clustering. Additional specifications adjust for local area characteristics instead of country fixed effects. Results: Both higher maternal and paternal education levels are associated with lower childhood undernutrition. In regressions adjusted for child age and sex as well as country-level fixed effects, the association is stronger for maternal education than for paternal education when their combined level of education is held constant. In the fully adjusted models, the observed differences in predicted undernutrition prevalences are strongly attenuated, suggesting a similar importance of maternal and paternal education. These findings are confirmed by the analysis of composite schooling indicators. Conclusions: We find that paternal education is similarly important for reducing childhood undernutrition as maternal education and should therefore receive increased attention in the literature.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".