Ready‐to‐Eat Cereal Consumption and the School Breakfast Program: Relationship to Nutrient Intake and Weight
Bibliographic record
Abstract
BACKGROUND: Children in the United States exhibit high rates of nutrient inadequacy and overweight/obesity; other international studies report similar associations. The School Breakfast Program (SBP) may help alleviate these conditions for youth in the United States. This research examined the association of ready-to-eat (RTE) breakfast cereal consumption with students' (SBP participants and nonparticipants) dietary nutrient intake and weight status. METHODS: Data were from the third School Nutrition Dietary Assessment Study (SNDA-III), conducted in 2004-2005. Analyses included all 2298 SNDA-III participants (ages 5-18 years), who completed a dietary recall and whose parents completed an interview. Students were categorized as skipping versus eating breakfast; students eating breakfast were categorized by SBP participation (SBP participant vs nonparticipant) and breakfast type (with vs without RTE cereal). Analyses estimated the association of these categorizations with total daily nutrient intake and weight-related measures, after adjusting for covariates. RESULTS: RTE cereal breakfast consumers (regardless of SBP participation) had greater intake of vitamin A, iron, and whole grain, compared with noncereal breakfast eaters. Breakfast consumers (regardless of SBP participation and type) had greater intake of vitamins A and C, calcium, iron, dietary fiber, and whole grain, relative to breakfast skippers. CONCLUSIONS: The SBP increases opportunity for improved nutrient intake and healthier body weight through breakfast consumption, especially whole grain RTE cereals, as recommended by the Dietary Guidelines 2010.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".