Food Preparation Practices for Infants Aged From 7 to 13 Months
Bibliographic record
Abstract
OBJECTIVE: To examine infant food preparation practices at age 7, 9, 11, and 13 months overall and by sociodemographic characteristics. DESIGN: Data from a longitudinal study from the US Department of Agriculture's Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Infant and Toddler Feeding Practices Study-2 (ITFPS-2) were used. PARTICIPANTS: A sample of 1,904 infants (970 males and 934 females) enrolled in WIC who had been introduced to solid foods and were consuming food prepared at home. MAIN OUTCOME MEASURES: Food preparation practices included pureeing, mashing, chopping/dicing, and prechewing. Estimates were provided overall and by sociodemographics. ANALYSIS: Prevalence estimates were calculated for each survey month overall and by sociodemographics. Chi-square tests for independence were used to test for differences. RESULTS: Food preparation practices changed as infants aged. Pureeing and mashing were common in month 7 (57.8% and 59.6%, respectively), but chopping/dicing were the most prevalent by month 13 (85.4%). Food preparation practices did not vary by education status, but statistical differences were consistently observed by race and ethnicity and inconsistently observed by maternal age at birth. CONCLUSIONS AND IMPLICATIONS: Exposing children to a range of food textures at an appropriate age is important for developmental progress. Continued culturally relevant efforts by WIC educators and health care providers can emphasize the importance of early experiences with food textures.
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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 | 0.001 | 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".