Intimate Partner Violence and Women’s Dietary Diversity: A Population-Based Investigation in 8 Low- and Middle-Income Countries
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) poses a significant threat to the well-being of women and girls and is a highly prevalent form of gender-based violence. Evidence regarding the nutritional implications of IPV has focused primarily on intergenerational relationships with child nutrition and growth. There remains a knowledge gap regarding the association with women's own dietary intake. OBJECTIVES: We investigated relationships between past-year IPV (physical, emotional, and sexual) and women's dietary habits, using the Minimum Dietary Diversity for Women tool. METHODS: The data sources analyzed were the cross-sectional Demographic and Health Surveys conducted in Cambodia (2021, N = 5618), Nepal (2022, N = 4155), Sierra Leone (2019, N = 3808), Nigeria (2018, N = 8313), Tajikistan (2017, N = 4792), Cote D'Ivoire (2022, N = 3654), Kenya (2022, N = 10,717), and the Philippines (2022, N = 12,240). Utilizing multivariable generalized linear models, we assessed the overall relationship between women's exposure to IPV and 1) the number of food groups consumed and 2) minimum dietary diversity. RESULTS: Our results reveal heterogeneous relationship patterns between IPV and women's diet. Although none of the pooled estimates were significant and there were a large number of nonsignificant associations, IPV was associated with consuming a lower number of total food groups and reduced consumption of a diverse diet in Nigeria, Kenya, and the Philippines. There is evidence that in Tajikistan, physical violence relates to an increased number of food groups consumed. CONCLUSIONS: IPV is associated with altered dietary intake patterns within certain low- and middle-income countries. The directionality of associations may depend on local food environments and food access. Further research is needed to clarify the pathways underlying these findings. These pathways may involve impacts of IPV that influence diet and food access, for example, mental health symptoms and disorders and related coping mechanisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".