Young Children’s Food-Related Receptive Language and Acceptance of a Novel Food
Bibliographic record
Abstract
Little inquiry has been undertaken regarding the interface of children's development of eating behaviors and their understanding of food-related (FR) words. Thus, we explored the relationship between young children's understanding of FR vocabulary (FR receptive language) and their acceptance of a novel food. Caregivers (n = 35) and children ages 7–24 mo (n = 12 infants age <12 mo; n = 23 toddlers age ≥12 mo) participated in a study in which caregivers offered a novel food (nutrition supplement added to infant oatmeal) to their children. Novel food acceptance was measured as g consumed. The Communicative Development Inventory assessed caregiver perceptions of infants’ understanding of 154 words related to food and eating (FR receptive language). A linear regression model with FR receptive language, age (toddler vs. infant) and a FR receptive language-by-age interaction was used to predict acceptance of the novel food. Covariates included infant sex and BMI z-score and a p < 0.1 was chosen for this exploratory study. As expected, caregivers reported that infants understood fewer words as compared to toddlers (median [IQR]; 7 [13] vs. 33 [46], respectively). The relationship between FR receptive language and novel food acceptance differed by age (F = 8.08, p = 0.01). Among toddlers, greater FR receptive language (more food-related words understood) was associated with greater novel food acceptance (β [95% CI], 0.22g [−0.04, 0.49], p = 0.09). In younger infants, greater FR receptive language was associated with lower novel food acceptance (−0.80g [−1.53, −0.07], p = 0.03). Receptive language facilitates children's understanding of their environment and contributes to shaping their behavior. Our preliminary findings suggest that greater FR receptive language may facilitate acceptance of novel food in toddlers. Younger infants were perceived to understand few words and may not have enough receptive language to positively influence food acceptance. This work was supported by the Government of Canada, as part of the Business Platform for Nutrition Research (BPNR) hosted by the Global Alliance for Improved Nutrition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".