Motivation to regulate eating behaviors, intuitive eating, and well-being: A dyadic study with mothers and adult daughters
Bibliographic record
Abstract
Intuitive eating, defined as relying on physiological cues to determine when, what, and how much to eat while maintaining a positive relationship with food (Tribole & Resch, 1995), has gained a lot of research attention in the last two decades. The present study sought to determine how motivation for regulating eating behaviors is related to intuitive eating and well-being outcomes in dyads of mothers and their adult daughters (n = 214). Structural equation modelling revealed that controlling for dieting and desire to lose weight, both mothers' and daughters' autonomous motivation was positively associated with their own intuitive eating while their controlled motivation was negatively associated with intuitive eating. In turn, intuitive eating was positively associated with well-being in both mothers and daughters. Interestingly, mothers' intuitive eating was also positively related to their daughters' well-being. The analysis of indirect effects suggests that mothers' motivation to regulate eating behaviors has an indirect (mediating) relationship with daughters' well-being through mothers' intuitive eating. The implications for women's health and well-being are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".