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Record W2007682880 · doi:10.1521/pedi.2008.22.2.148

Impulsivity and Emotion Dysregulation in Borderline Personality Disorder

2008· article· en· W2007682880 on OpenAlexaff
Alexander L. Chapman, Debbie W. Leung, Thomas R. Lynch

Bibliographic record

VenueJournal of Personality Disorders · 2008
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBorderline personality disorderImpulsivityPsychologyEmotional dysregulationClinical psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

This study examined the association of borderline personality disorder (BPD) and negative emotional states with impulsivity in the laboratory. Undergraduate participants who were high in BPD features (high-BPD; n = 39) and controls who were low in BPD features (low-BPD; n = 56) completed measures of negative emotional state before a laboratory measure of impulsivity--a passive avoidance learning task. Controlling for psychopathology, high-BPD participants committed a greater number of impulsive responses than did low-BPD participants. Negative emotional state moderated the effect of BPD on impulsive responses. High-BPD participants who were in a negative emotional state committed fewer impulsive responses than high-BPD participants who were low in negative emotional state. Fear, nervousness, and shame negatively correlated with impulsivity among high-BPD participants but not among low-BPD participants. In addition, high-BPD participants reported greater emotion dysregulation in a variety of domains, compared with low-BPD participants.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.311
Teacher spread0.290 · 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 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

Citations147
Published2008
Admission routes1
Has abstractyes

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