Insecure attachment and eating disorder symptoms: Intolerance of uncertainty and emotion regulation as mediators
Bibliographic record
Abstract
OBJECTIVE: Literature on eating disorder (ED) symptoms of Black, Indigenous, and People of Color (BIPOC) group is extremely scarce. This study aimed to understand the mechanisms underlying the associations between insecure attachment and ED symptoms and examine whether these mechanisms differed between White and BIPOC groups. METHOD: The study investigated direct and indirect relationship between attachment anxiety/avoidance and ED symptoms via intolerance of uncertainty (IU) and emotion regulation strategies of suppression and reappraisal. Further, we examined whether the proposed mechanisms equally represented White versus BIPOC using Multigroup Structural Equation Model (MG-SEM). A total of 1227 college students (48.50% BIPOC and 51.50% White) completed research questionnaires. RESULTS: Results showed that IU and suppression mediated the relations between insecure attachment and ED symptoms for both White and BIPOC groups. Uniquely, reappraisal mediated the relations between insecure attachment and ED symptoms for the White group, but not for the BIPOC group. DISCUSSION: The implications of the findings for culturally informed practice are discussed, including targeting increasing tolerability of uncertainties and improving emotion regulation to mitigate ED symptoms for those with insecure attachment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".