Western, Healthful, and Low-Preparation Diet Patterns in Preschoolers of the STRONG Kids2 Program
Bibliographic record
Abstract
OBJECTIVE: Identify and describe diet patterns of children during early childhood using confirmatory factor analysis (CFA). DESIGN: Longitudinal data were drawn from the STRONG Kids 2 program. PARTICIPANTS: Mothers were surveyed about their child's diet at 24 (n = 337), 36 (n = 317), and 48 (n = 289) months old. VARIABLES MEASURED: The Block Food Frequency Questionnaire for children aged 2-7 years was used to derive diet patterns; 23 food groups were created for analyses. ANALYSIS: Principal component analysis was used to obtain preliminary factor loadings, and loadings were used to form a priori hypotheses for CFA-derived diet patterns. Independent samples t tests were used to compare food groups, nutrient intakes, and child and family characteristics by CFA pattern scores above vs at/below the median. RESULTS: Three diet patterns consistently emerged: (1) processed meats, sweets, and fried foods; (2) vegetables, legumes, and starchy vegetables; and (3) grains, nuts/seeds, and condiments (only 24 and 36 months). Patterns were related to differences in added sugars, dietary fiber and potassium intakes, maternal education, and household income. CONCLUSIONS AND IMPLICATIONS: Opposing healthful vs Western patterns, extant in child and adult literature, were observed across all ages. The third pattern differed between 24/36 and 48 months, representing a potential shift in food choices or offerings as children age.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".