Influence of Nutrition Knowledge on the Association between Maternal Nutrition and Birth Outcomes
Bibliographic record
Abstract
Maternal undernutrition is a complex condition that arises from various factors, including social, cultural, psycho-social, biological, and ecological factors. The intergenerational consequences of chronic malnutrition, starting with mothers and their children, account for a significant proportion of infant deaths, cognitive disability, and reduced productivity in adulthood. Therefore, having nutritional knowledge throughout pregnancy is crucial for better risk assessment of undernutrition and healthy pregnancy outcomes. The study examined whether trimester-specific nutrition education and awareness could significantly impact the relationship between maternal nutrition and birth outcomes and found that this association was stronger in women with higher levels of nutrition knowledge. As a result, a food-based approach that is both low-cost and high-nutrition can help meet the specific nutritional requirements of pregnancy, alter certain nutrients that target fetal metabolic vulnerabilities, or enhance fetal growth and development in the migrant population. The review explores recent research and discusses how nutrition literacy and knowledge influence pregnancy and birth outcomes, providing an overview of the current understanding of maternal nutritional trimester-specific needs and highlighting areas that still require further study. The findings emphasize the importance of considering diet diversity and peer support during pregnancy, considering the impact that nutrition knowledge has on pregnancy outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".