Attachment and eating disorders: A review of current research
Bibliographic record
Abstract
OBJECTIVE: Attachment insecurity may confer risk for developing an eating disorder. We describe domains of attachment functioning that are relevant to eating disorders including: affect regulation, interpersonal style, coherence of mind, and reflective functioning. Research since 2000 on attachment and eating disorders related to these domains is reviewed. METHOD: We searched MedLine/Pubmed and PsycINFO from January 2000 to February 2014 and kept articles that: were empirical, included adults with a diagnosed eating disorder, and used a standard attachment measure. We retained 50 relevant studies. RESULTS: Compared to controls, those with eating disorders had higher levels of attachment insecurity and disorganized mental states. Lower reflective functioning was specifically associated with anorexia nervosa. Attachment anxiety was associated with eating disorder symptom severity, and this relationship may be mediated by perfectionism and affect regulation strategies. Type of attachment insecurity had specific negative impacts on psychotherapy processes and outcomes, such that higher attachment avoidance may lead to dropping out and higher attachment anxiety may lead to poorer treatment outcomes. DISCUSSION: Research to date suggests a possible relationship between attachment insecurity and risk for an eating disorder. More research is needed that uses attachment interviews, and longitudinal and case control designs. Clinicians can assess attachment insecurity to help inform therapeutic stances and interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".