From Restriction to Intuition: Evaluating Intuitive Eating in a Sample of the General Population
Bibliographic record
Abstract
Intuitive eating (IE) is a non-dieting approach that promotes listening to internal cues of hunger and satiety, rather than adhering to external dietary restrictions aimed at weight loss. However, the role of IE in dieting behaviors related to weight-loss approaches is still unclear. To address this issue, the aim of this study was to compare IE levels between dieting and non-dieting individuals, exploring the relationship between IE and dieting-related psychological and physical factors. A sample of 2059 females was recruited via social media and self-reported questionnaires were administered to measure IE, eating psychopathology, self-efficacy, and quality of life. Individuals with a history of dieting exhibited lower IE levels, a higher BMI, and a greater eating psychopathology, as well as a reduced self-efficacy and quality of life, compared to non-dieters. IE showed a protective effect against dieting behaviors, with higher IE levels being associated with a lower likelihood of dieting. Additionally, higher BMI and eating psychopathology were predictors of dieting. Promoting IE could represent a relevant clinical target strategy to address disordered eating and enhance overall well-being, underscoring the need for interventions that foster a healthier relationship with food and bodily internal sensations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".