The relationships between body-related emotion intolerance and restrictive eating as a function of multidimensional perfectionism
Bibliographic record
Abstract
Objective: Emotion intolerance and perfectionism are two maintaining mechanisms to eating disorder psychopathology. However, it is unclear how these mechanisms relate to one another. This study explored whether perfectionism is a vulnerability factor for facets of restrictive eating in the context of body-related emotions. Methods: Female undergraduate students ( N =148) completed questionnaires assessing baseline levels of self-critical perfectionism and personal standards perfectionism. Participants then engaged in an ecological momentary assessment protocol where body-related emotion intolerance and restrictive eating facets (cognitive restraint and behavioural restriction) were assessed over 10 consecutive days. Multi-level modeling and simple slopes analysis were used to explore these moderated relationships. Results: Based on the analyses, both self-critical and personal standards perfectionism dimensions interacted with body-related emotion intolerance to predict increases in restrictive eating facets. Conclusion: These findings indicate that personal standards perfectionism, though conceptualized as the less maladaptive dimension of perfectionism, should not be ignored in eating disorder conceptualizations and treatments. Recommendations are provided on how to refine treatment targets to be more attuned with situations that elicit body-related emotion intolerance.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".