Early Determinants of Fruit and Vegetable Acceptance
Bibliographic record
Abstract
OBJECTIVE: Our goal was to evaluate the effects of breastfeeding and dietary experiences on acceptance of a fruit and a green vegetable by 4- to 8-month-old infants. METHODS: Forty-five infants, 44% of whom were breastfed, were assigned randomly to 1 of 2 treatment groups. One group was fed green beans, and the other was fed green beans and then peaches at the same time of day for 8 consecutive days. Acceptance of both foods, as determined by a variety of measures, was assessed before and after the home-exposure period. RESULTS: During the initial exposure, infants ate more calories from peaches than from green beans. Breastfed infants showed greater liking of peaches, as did their mothers, who ate more fruits in general than did mothers who formula fed. Although formula-feeding mothers ate more green beans, there was no difference in their infants' acceptance of this vegetable. For breastfed and formula-fed infants, repeated dietary exposure to green beans, with or without peaches, resulted in greater consumption of green beans (56.8 vs 93.6 g). Only infants who experienced green beans with peaches displayed fewer facial expressions of distaste during feeding. Mothers were apparently unaware of these changes in acceptance. CONCLUSIONS: Breastfeeding confers an advantage in initial acceptance of a food, but only if mothers eat the food regularly. Once weaned, infants who receive repeated dietary exposure to a food eat more of it and may learn to like its flavor. However, because infants innately display facial expressions of distaste in response to certain flavors, caregivers may hesitate to continue offering these foods. Mothers should be encouraged to provide their infants with repeated opportunities to taste fruits and vegetables and should focus not only on their infants' facial expressions but also on their willingness to continue feeding.
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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 | 0.003 |
| 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) | 0.003 | 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".