Intuitive eating in light of other eating styles and motives: Experiences with construct validity and the Hungarian adaptation of the Intuitive Eating Scale-2
Bibliographic record
Abstract
Intuitive eating (IE), an adaptive eating approach, has been identified as a plausible positive determinant of physical and mental well-being. This cross-sectional survey study aimed to examine the construct validity of IE measured by the Intuitive Eating Scale-2 (IES-2). The conceptual network of different adaptive and maladaptive eating behaviors was also explored. Confirmatory factor analysis (CFA), measurement invariance testing, and correlation analyses were applied on the data from a sample of 732 Hungarian university students. Validating variables included several eating behavior styles (i.e., obesogenic eating behaviors, mindful eating [ME], and dieting practices) and motivational factors (i.e., autonomous, controlled, and amotivational sources of healthy eating, and internalization of the thin and muscular body ideals). The results of the CFA supported the original four-factor structure of the IES-2 without a global second-order factor. The scale exhibited measurement invariance on the scalar level across sexes and BMI categories. The IES-2 subscales showed adequate reliability. IE and ME were found to be positively related but conceptually distinct constructs. Autonomous motivation for healthy eating, restrained eating, and current dieting exhibited different yet meaningful associations to IE on the subscale level. Undertaking subscale-level analyses is recommended when using the IES-2.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".