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Record W4393932955 · doi:10.1002/jclp.23685

Insecure attachment and eating disorder symptoms: Intolerance of uncertainty and emotion regulation as mediators

2024· article· en· W4393932955 on OpenAlexaff
Ling Jin, Gabriel Zamudio, Chiachih D. C. Wang, Stacy L. Lin

Bibliographic record

VenueJournal of Clinical Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyInsecure attachmentAnxietyAttachment theoryClinical psychologyEating disordersStructural equation modelingWhite (mutation)Developmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.475
Teacher spread0.423 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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