Assessment of eating attitudes and dieting behaviors in healthy children: Confirmatory factor analysis of the Children's Eating Attitudes Test
Bibliographic record
Abstract
OBJECTIVE: The Children's Eating Attitudes Test (ChEAT) is a self-report questionnaire that is conventionally summarized with a single score to identify "problematic" eating attitudes, masking informative variability in different eating attitude domains. This study evaluated the empirical support for single- versus multifactor models of the ChEAT. For validation, we compared how well the single- versus multifactor-based scores predicted body mass index (BMI). METHOD: Using data from 13,674 participants of the 11.5 year-follow-up of the Promotion of Breastfeeding Intervention Trial (PROBIT) in the Republic of Belarus, we conducted confirmatory factor analysis to evaluate the performance of 3- and 5-factor models, which were based on past studies, to a single-factor model representing the conventional summary of the ChEAT. We used cross-validated linear regression models and the reduction in mean squared error (MSE) to compare the prediction of BMI at 11.5 and 16 years by the conventional and confirmed factor-based ChEAT scores. RESULTS: The 5-factor model, based on 14 of the original 26 ChEAT items, had good fit to the data whereas the 3- and single-factor models did not. The MSE for concurrent (11.5 years) BMI regressed on the 5-factor ChEAT summary was 35% lower than that of the single-score models, which reduced the MSE from the null model by only 1%-5%. The MSE for BMI at 16 years was 20% lower. DISCUSSION: We found that a parsimonious 5-factor model of the ChEAT explained the data collected from healthy Belarusian children better than the conventional summary score and thus provides a more discriminating measure of eating attitudes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".