Predicting the effectiveness of engagement and disengagement emotion regulation based on emotional reactivity in borderline personality disorder
Bibliographic record
Abstract
Improving emotion regulation is central to borderline personality disorder (BPD) treatment, but little research indicates which emotion regulation strategies are optimally effective and when. Basic emotion science suggests that engagement emotion regulation strategies that process emotional content become less effective as emotional intensity increases, whereas disengagement strategies that disengage from it do not. This study examined whether emotional reactivity to emotional stimuli predicts the effectiveness of engagement and disengagement emotion regulation across self-report, general physiologic (heart rate), sympathetic (skin conductance responses), and parasympathetic (respiratory sinus arrythmia) emotion in BPD, healthy, and clinical control (i.e. generalized anxiety disorder; GAD) groups. 120 participants (40 per group) were exposed to emotion inductions and then instructed to implement engagement (mindful awareness) and disengagement (distraction) strategies while self-report and physiological emotion measurements were taken. In the BPD and GAD groups, higher heart rate or respiratory sinus arrythmia reactivity, respectively, predicted improved mindful awareness effectiveness. Higher skin conductance reactivity predicted worsened distraction effectiveness in BPD. Higher reactivity may potentiate engagement emotion regulation, and exacerbate disengagement from emotional content, in BPD. Future research should examine other domains of emotion regulation that may be influenced by emotional intensity, and other forms of emotional intensity that may influence them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".