Maternal diet during early childhood, but not pregnancy, predicts diet quality and fruit and vegetable acceptance in offspring
Bibliographic record
Abstract
Studies have identified prenatal flavour exposure as a determinant of taste preferences in infants; however, these studies have focused on relatively small samples and limited flavours. As many parents struggle with getting children to accept a variety of nutritious foods, a study of the factors influencing food acceptance is warranted. The objective of this study was to determine whether exposure to a wider variety of fruit and vegetables and overall higher diet quality in utero results in acceptance of a greater variety of these foods and better diet quality for offspring during childhood. This study is a secondary data analysis of pregnant women (n = 52) and their resulting offspring recruited for the Women and Their Children's Health study in NSW, Australia. Dietary intake of mothers and children was measured using food frequency questionnaires. Diet quality and vegetable and fruit variety were calculated using the Australian Recommended Food Score and the Australian Child and Adolescent Recommended Food Score. Associations between maternal and child diet quality and variety were assessed using Pearson's correlations and the total effect of in utero maternal pregnancy diet on childhood diet was decomposed into direct and indirect effect using mediation analysis. Maternal pregnancy and post-natal diet were both correlated with child diet for overall diet quality and fruit and vegetable variety (P < 0.001). Mediation analyses showed that the indirect effect of maternal pregnancy diet on child diet was mediated through maternal post-natal diet, particularly for fruit (P = 0.045) and vegetables (P = 0.055). Nutrition intervention should therefore be aimed at improving diet quality and variety in mothers with young children, in order to subsequently improve eating habits of offspring.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".