Psychometric Properties of the Dutch Version of the Eating Competence Satter Inventory (ecSI 2.0TM) in Community Adolescents
Bibliographic record
Abstract
Eating competence can help adolescents navigate their food choices and attitudes toward eating in a healthy and balanced way. In the present study, we investigated the psychometric properties of the Dutch translation of the Eating Competence Satter Inventory 2.0TM (ecSI 2.0TM), which was developed to assess eating attitudes and behaviors. A sample of 900 Flemish adolescents completed the ecSI 2.0TM DUTCH and two self-report measures on eating disorder symptoms and identity functioning (i.e., confusion and synthesis). Confirmatory factor analysis confirmed the four-factor structure of the ecSI 2.0TM DUTCH, and the resulting four subscales (i.e., Eating Attitudes, Food Acceptance, Internal Regulation, and Contextual Skills) showed acceptable-to-excellent reliability (αs ranging from 0.69 to 0.91). The ecSI 2.0TM DUTCH also demonstrated scalar invariance across sex and age (<17 years, ≥17 years). Males reported significantly higher ecSI 2.0TM DUTCH scores than females on the four subscales and the total scale. The two age groups did not significantly differ on the ecSI 2.0TM DUTCH scales. Finally, scores on the ecSI 2.0TM DUTCH subscales showed non-significant or small negative correlations with adolescents’ Body Mass Index (BMI), large negative correlations with eating disorder symptoms and identity confusion, and large positive associations with identity synthesis. The Dutch translation of the ecSI 2.0TM is a valid and reliable instrument to assess eating competence skills in male and female adolescents.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".