Association between maternal experiences of intimate partner violence and child stunting: a secondary analysis of the Demographic Health Surveys of four South Asian countries
Bibliographic record
Abstract
OBJECTIVES: To determine the association between maternal exposure to intimate partner violence (IPV) and child stunting using the Demographic Health Survey (DHS) data comparing four South Asian countries. DESIGN: A secondary analysis. SETTING: Data from the seventh round of the DHS data of four South Asian countries; Pakistan, Nepal, India and Maldives. PARTICIPANTS: Married women of reproductive age (15-49 years) from each household were randomly selected, having at least one child less than 5 years of age for whom all anthropometric measures were available. OUTCOME MEASURE: The exposure variable was maternal IPV including, sexual violence, physical violence or both. The outcome variable was moderate or severe stunting, measured based on the height-for-age Z-score of children aged 6-59 months old . Multiple Cox proportional regression analyses were used separately on each country's data to determine the association between maternal IPV and child stunting. RESULTS: The prevalence of IPV among women ranged from 10.17% in the Maldives to 31% in India. The burden of child stunting was the lowest in the Maldives at 14.04% and the highest in Pakistan at 35.86%. The number of severely stunted children was the highest in Pakistan (16.60%), followed by India (14.79%). In India, children whose mothers were exposed to IPV showed a 7% increase in the prevalence of moderate to severe child stunting (OR 1.07; 95% CI 1.01 to 1.14). Additionally, in Nepal, severe stunting was strongly associated with the prevalence of physical IPV (OR 1.66; 95% CI 1.01 to 2.87). CONCLUSION: Our study findings suggest that maternal exposure to IPV is associated with child stunting. Further research investigating the relationship between IPV and child outcomes using improved and advanced statistical analyses can provide substantial evidence to enhance public awareness and potentially reduce the burden of child stunting in South Asian countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".