Current evidence around key underrepresented women's health topics in pregnancy and postpartum nutrition: a narrative review
Bibliographic record
Abstract
Adequate nutrition during pregnancy and postpartum is critical to maternal and child health, but there is often a missing focus around health outcomes specifically for women. Women's health includes sex-specific biological attributes and socially constructed gender roles framing behaviours and practices. This narrative review aims to highlight key areas where women's health has been underrepresented in pregnancy and postpartum nutrition research. Current evidence and research gaps are discussed for nutritional requirements during pregnancy and lactation, maternal mortality and morbidity nutritional risk factors, preconception and postpartum nutrition, and gendered cultural norms and inequities in access to nutritious foods during pregnancy and postpartum. Important areas for future research include strengthening empirical evidence for nutritional requirements in pregnant and lactating populations, the relationship between maternal iron status, anaemia and maternal morbidities, linkages between nutrient status among women and adolescent girls to maternal health outcomes, postpartum nutrition for recovery, lactation, and long-term women's health outcomes, and strength-based cultural practices that can support adequate maternal nutrition. There is an ongoing need to include women in nutritional requirements research, and measure health outcomes for women to ensure creation of an evidence base on both sex and gender-based datasets.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".