Attachment Anxiety is Associated with Restrictive Eating via Low Global Self-Esteem and Appearance Overvaluation
Bibliographic record
Abstract
This research explored the relationship between attachment, appearance overvaluation, global self-esteem, and restrictive eating in a secondary analysis of a community sample of undergraduate women.Participants (N = 527) completed the Experiences in Close Relationships questionnaire (Lafontaine et al., 2016), Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965), Beliefs About Appearance Scale (Spangler & Stice, 2001), and the restriction subscale of the Eating Disorders Examination-Questionnaire (EDE-Q; Fairburn & Beglin, 1994).A serial mediation analysis was conducted to examine associations between these variables.Women higher in attachment anxiety reported greater appearance overvaluation, via lower global selfesteem, and reported more restrictive eating through lower global self-esteem and higher appearance overvaluation.Attachment avoidance was not related to appearance overvaluation or restrictive eating but was associated with lower global self-esteem.These results may inform prevention efforts, by identifying individuals with attachment anxiety, who may be more vulnerable to low global self-esteem, appearance overvaluation, and restrictive eating.Keywords: Attachment styles, disordered eating, overvalued ideation, self-concept Dr. Cheryl Harasymchuk, for her time and invaluable input, which served to strengthen my thesis and broaden my perspectives
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".