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Record W2495309536 · doi:10.1080/09515070.2016.1211509

Examining the relationship between emotion regulation deficits and borderline personality disorder features: A daily diary study

2016· article· en· W2495309536 on OpenAlexaff
Skye Fitzpatrick, Jennifer E. Khoury, Janice R. Kuo

Bibliographic record

VenueCounselling Psychology Quarterly · 2016
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBorderline personality disorderDysfunctional familyPsychologyClinical psychologyPersonalityEmotional regulationExpressive SuppressionCognitive reappraisalDevelopmental psychologyCognitionPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

This study used a six-day daily diary methodology to precisely specify the nature of emotion regulation deficits associated with borderline personality disorder (BPD) features. Three possibilities were explored: that BPD features are associated with (1) the overall underuse of emotion regulation strategies; (2) the overuse of dysfunctional and the underuse of functional strategies; and (3) the lower perceived effectiveness of emotion regulation strategies. One hundred and fifty-four undergraduate participants completed self-report measures of BPD feature severity, and then reported their daily negative emotional intensity, whether or not they used various emotion regulation strategies, and whether or not the strategies that they used were effective across a six-day period. Higher BPD features were associated with (a) higher total frequency use of emotion regulation strategies; (b) higher frequency use of dysfunctional and functional emotion regulation strategies; and (c) less self-reported effectiveness of functional strategies. BPD features may be characterized by increased attempts to regulate emotions, without corresponding increases in perceived effectiveness.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.067
GPT teacher head0.346
Teacher spread0.279 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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