Women’s decision-making power and undernutrition in their children under age five in the Democratic Republic of the Congo: A cross-sectional study
Bibliographic record
Abstract
Undernutrition in children remains a major global health issue and the prevalence of undernutrition in children under age five in the Democratic Republic of the Congo (DRC) is among the highest in the world. Both biological and socioeconomic factors contribute to undernutrition, and the literature reports an association between women's empowerment and lower rates of child undernutrition in sub-Saharan Africa. However, the relationship between women's decision-making power and child undernutrition is less understood. The objective of this study was to evaluate the association between women's decision-making power and stunting/wasting in their children under age five in the DRC. This study used cross-sectional data from the 2013-2014 DRC Demographic and Health Survey, from which a sample of 3,721 woman-child pairs were identified. Women were classified as having decision-making power in five decision-making dimensions if they participated in the decision either alone or jointly with their husband or partner or someone else. Child height-for-age and weight-for-height Z-scores were used to determine stunting and wasting, respectively, according to the World Health Organization Child Growth Standards. Multivariate regression analyses demonstrated that none of the five dimensions of decision-making power were associated with stunting or wasting in children. Further research that evaluates women's decision-making power with more detailed, relevant and context-specific measures is warranted to more accurately investigate women's decision-making power and undernutrition in children.
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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 | 0.001 |
| 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".