Breakfast Cereal Consumption Moderates the Association Between Body Composition and Body Esteem in Young Women but not in Young Men
Bibliographic record
Abstract
PURPOSE: To explore the role of breakfast cereal consumption on the relationships among BMI, percent fat mass (%FM), and body esteem in young adults. METHODS: Weight, height, and %FM (by air displacement plethysmography) were measured in 29 males (aged 25.1 ± 4.0 years) and 28 females (aged 24.6 ± 4.0 years). Body esteem was measured using the Body Esteem Scale for Adolescents and Adults (BESAA). Three-day food records classified participants as breakfast cereal consumers (n = 27, any amount of ready-to-eat or cooked cereal consumed at breakfast) versus nonconsumers (n = 30, no cereal consumed at breakfast). RESULTS: The %FM was significantly (P ≤ 0.05) inversely correlated with weight esteem (r = -0.769), appearance esteem (r = -0.723), and external attribution (r = -0.620) in female noncereal consumers. BMI was similarly correlated with BESAA scores. These relationships were not significant in female cereal consumers (all r < 0.426), despite no difference in confounding variables between female cereal consumers and nonconsumers. Neither BMI nor %FM were correlated with measures of body esteem (all r < 0.466, NS) in either male cereal consumers or nonconsumers. CONCLUSIONS: Breakfast cereal consumption may moderate the relationship among BMI, %FM, and body esteem in young adult women and may be useful for improving body esteem without focusing on weight loss.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".