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Record W2890284945 · doi:10.1521/pedi_2018_32_390

Cognitive Reappraisal of Negative Emotional Images in Borderline Personality Disorder: Content Analysis, Perceived Effectiveness, and Diagnostic Specificity

2018· article· en· W2890284945 on OpenAlexaff
Alexander R. Daros, Achala H. Rodrigo, Nikoo Norouzian, Bri Darboh, Kateri McRae, Anthony C. Ruocco

Bibliographic record

VenueJournal of Personality Disorders · 2018
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsCognitive reappraisalPsychologyBorderline personality disorderExpressive SuppressionCognitionAnxietyClinical psychologyStimulus (psychology)Negative emotionDevelopmental psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Individuals with borderline personality disorder (BPD) report using cognitive reappraisal less often than healthy individuals despite the long-term benefits of the emotion regulation strategy on emotional stability. Individuals with BPD, mixed anxiety and/or depressive disorders (MAD), and healthy controls (HC) completed an experimental task to investigate the tactics contained in cognitive reappraisal statements vocalized for high and low emotional intensity photographs. Self-reported effectiveness after using cognitive reappraisal to decrease negative emotions was also evaluated. Although BPD and MAD used a similar number of cognitive reappraisal tactics, they perceived themselves as less effective at reducing their negative emotions compared to HC. During cognitive reappraisal, BPD and MAD uttered fewer words versus HC, while BPD uttered fewer words versus MAD. Results suggest that individuals with BPD and MAD are less fluent and perceive themselves as less effective than HC when using cognitive reappraisal to lower negative emotions regardless of stimulus intensity.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.349
Teacher spread0.316 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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