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Record W3033749228 · doi:10.1097/nmd.0000000000001196

Empathy, Alexithymia, and Theory of Mind in Borderline Personality Disorder

2020· article· en· W3033749228 on OpenAlexaboutno aff
Faruk Kılıç, Arif Demirdaş, Ümit Işık, Merve Akkuş, İnci Meltem Atay, Duru Kuzugüdenlioğlu

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2020
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaEmpathyPsychologyBorderline personality disorderToronto Alexithymia ScaleBeck Depression InventoryClinical psychologyTheory of mindBarratt Impulsiveness ScaleDepression (economics)ImpulsivityDevelopmental psychologyPsychiatryCognitionAnxiety

Abstract

fetched live from OpenAlex

The purpose of this research was to determine the differences in empathy, alexithymia features, and theory of mind between healthy controls and patients with borderline personality disorder (BPD). Thirty-five patients with BPD and 35 healthy controls were included in the study. To measure the clinical variables, the Empathy Quotient (EQ), Reading the Mind in the Eyes Test (RMET), Toronto Alexithymia Scale (TAS-20), Barratt Impulsivity Scale-11 (BIS-11), and Beck Depression Inventory (BDI) were applied. We found that the BPD group had significantly worse total RMET and neutral RMET scores than the control group. There were no differences in the EQ scores between the BPD and control groups. The patients with BPD were more alexithymic than the controls, and alexithymia and depression scores predicted BPD status. Patients with BPD who have difficulty identifying their own emotions tend to display deficits in perceptions of facial emotions, which, in turn, may lead to misperceptions of social signals and thus contribute to excessive emotional intensity and tension in social situations. The study results reveal that alexithymia and depression are important variables in predicting BPD traits.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.207
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.303
Teacher spread0.277 · 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.

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

Citations33
Published2020
Admission routes1
Has abstractyes

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