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Record W4402730842 · doi:10.1037/abn0000953

Capturing the experience of borderline personality disorder symptoms in the daily lives of women with eating disorders.

2024· article· en· W4402730842 on OpenAlexafffund
Alexia E. Miller, Ege Biçaker, Vittoria Trolio, Carl F. Falk, Chloe White, Lisa Y Zhu, Sarah E. Racine

Bibliographic record

VenueJournal of Psychopathology and Clinical Science · 2024
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of CanadaFonds de recherche du Québec
KeywordsBorderline personality disorderEating disordersPsychologyPersonalityClinical psychologyPsychiatryPsychotherapistPsychoanalysis

Abstract

fetched live from OpenAlex

= 47) completed 14 days of ecological momentary assessment. All BPD symptoms except affective instability were more common in individuals with comorbid ED-BPD than those with only an ED. Affective instability and paranoia/dissociation had the largest effect sizes, indicating the greatest differences across groups. Individuals with more frequent abandonment avoidance, anger, identity disturbance, paranoia/dissociation, and self-harm over the 14 days engaged in more frequent binge eating, while those with greater emptiness engaged in more frequent restriction and maladaptive exercise. Momentary affective instability predicted an increased likelihood of binge eating, while momentary interpersonal difficulties predicted a decreased likelihood of binge eating, at the next prompt. This study highlights the importance of considering BPD symptoms in the treatment of individuals with EDs to improve their clinical outcomes and quality of life. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.024
GPT teacher head0.381
Teacher spread0.357 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2024
Admission routes2
Has abstractyes

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