Self-disgust in patients with borderline personality disorder. The associations with alexithymia, emotion dysregulation, and comorbid psychopathology
Bibliographic record
Abstract
BACKGROUND: Self-disgust is a negative self-conscious emotion, which has been linked with borderline personality disorder (BPD). However, it has not yet been investigated in relation to both emotion dysregulation and alexithymia, which are recognized as crucial to BPD. Therefore, the aim of our study was to measure these variables and examine the possible mediational role of emotional alterations and comorbid anxiety and depression symptoms in shaping self-disgust in patients with BPD and healthy controls (HCs). METHODS: In total, the study included 100 inpatients with BPD and 104 HCs. Participants completed: the Self-Disgust Scale (SDS), Disgust Scale - Revised (DS-R), Toronto Alexithymia Scale (TAS-20), Emotion Dysregulation Scale short version (EDS-short), Borderline Personality Disorder Checklist (BPD Checklist), State-Trait Anxiety Inventory (STAI), and Center for Epidemiologic Studies Depression Scale (CESD-R). RESULTS: Inpatients with BPD showed higher self-disgust, alexithymia, emotion dysregulation, core and comorbid symptoms levels, and lower disgust sensitivity. Alexithymia, emotion dysregulation, and trait anxiety partially mediated between BPD diagnosis and self-disgust. The relationship between the severity of BPD symptoms and self-disgust was fully mediated by alexithymia, emotion dysregulation, depressive symptoms, and trait anxiety. CONCLUSIONS: The results of our study may imply the contribution of emotion dysregulation, alexithymia, and comorbid psychopathology to self-referenced disgust in BPD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".