The moderating role of interpersonal problems on baseline emotional intensity and emotional reactivity in individuals with borderline personality disorder and healthy controls
Bibliographic record
Abstract
Emotion dysregulation, including higher baseline emotional intensity and emotional reactivity (i.e., increased magnitude of change in emotional responding) is theoretically central to Borderline Personality Disorder (BPD). However, little research has examined which specific emotions individuals with BPD experience emotion dysregulation in. Interpersonal problems also theoretically drive emotion dysregulation in BPD. However, whether interpersonal problems elicit emotion dysregulation for some specific emotions but not others is unclear. This study aimed to assess whether interpersonal problems moderate the relationship between (1) baseline emotional intensity and (2) emotional reactivity in BPD across six specific emotions (i.e., sadness, disgust, fear, shame, guilt, and anger). Borderline Personality Disorder ( n = 30) and healthy control (HC; n = 30) groups reported their interpersonal problems at baseline and their emotions before and after listening to a laboratory stressor. For the BPD (but not HC) group, higher interpersonal problems were associated with greater baseline sadness, disgust, fear, shame, and guilt. Across groups, higher interpersonal problems were associated with greater sadness, fear, guilt, and anger, but not disgust, reactivity. Higher interpersonal problems were associated with higher shame reactivity specifically for those with BPD. Targeting interpersonal problems may reduce heightened baseline emotional intensity and emotional reactivity for those with BPD, particularly for shame reactivity 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".