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Record W4410003399 · doi:10.1037/per0000726

Interpersonal emotion regulation in personality disorders: Introduction to a special section.

2025· article· en· W4410003399 on OpenAlexafffund
Katherine L. Dixon–Gordon, Skye Fitzpatrick

Bibliographic record

VenuePersonality Disorders Theory Research and Treatment · 2025
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsYork University
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsPsychologySpecial sectionPersonality disordersSection (typography)Interpersonal communicationPersonalityInterpersonal relationshipClinical psychologyPsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

With joint interpersonal and affective impairments associated with personality disorders, understanding the intersection of these processes in these disorders is a critical emerging trajectory for research. An emerging line of research has been devoted to understanding interpersonal emotion regulation processes in personality disorders. This relatively nascent research area has recently gained traction internationally. This special section summarizes some of the recent innovations in this area of research. These investigations have harnessed a diverse range of methods, including dyadic approaches, intensive longitudinal assessments, and information processing perspectives. Moreover, this section suggests that interpersonal emotion regulation is relevant to personality disorders beyond borderline personality disorder. This summary of innovative approaches is timely and can propel future clinically relevant and impactful research in this area. We provide recommendations for important next steps in this area of research. (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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.383
Teacher spread0.345 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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