Optimizing Emotion Regulation in Borderline Personality Disorder: Why and When Strategies Do and Do Not Work
Bibliographic record
Abstract
This dissertation aimed to delineate ways to optimize emotion regulation in borderline personality disorder (BPD) by 1) identifying factors that influence general emotion regulation effectiveness and 2) examining whether these factors predict differential effectiveness of two classes of emotion regulation strategies: engagement (i.e., engaging with emotional content) versus disengagement (i.e., shifting attention away from emotional content) strategies. Factors that occur before (i.e., antecedent-focused) and after (i.e., response-focused) emotion provocation were examined. Specifically, four predictors of general and differential emotion regulation effectiveness were identified: antecedent-focused sleep quality (impaired sleep efficiency and rated sleep quality), antecedent-focused biology (basal vagal tone), antecedent-focused emotion (baseline emotional intensity), and response-focused emotion (emotional reactivity). Secondary analyses also investigated whether the relationships of these factors to general and differential emotion regulation effectiveness varied across BPD and healthy control (HC) groups. A sample of individuals with BPD (n = 40) and matched HCs (n = 40) completed a weeklong assessment of sleep efficiency and quality and then participated in an experimental procedure. First, basal vagal tone and baseline emotional intensity data were collected. Following, participants were trained to use two BPD-relevant emotion regulation strategies, mindful awareness (engagement strategy) and distraction (disengagement strategy), in response to negative emotion inductions. Emotional reactivity in response to the inductions, and the extent to which emotion was decreased using the strategies following the inductions (i.e., emotion regulation effectiveness), was examined. Emotion was measured comprehensively across self-report, sympathetic, parasympathetic, and behavioural/expressive domains. Results indicated that sleep efficiency and rated sleep quality predicted differential emotion regulation effectiveness as they improved distraction but not mindful awareness effectiveness across groups. As well, higher basal vagal tone and emotional reactivity predicted improved emotion regulation effectiveness across strategies and groups. Findings suggest that targeting sleep quality may specifically facilitate the attention mechanisms required for effective use of distraction in BPD. They also suggest that identifying ways to increase vagal tone may potentiate the emotion regulation capacity of individuals with BPD. Finally, results indicate that high emotional reactions may not necessarily be problematic and, in fact, may mark a particularly fluid emotional system that is responsive to emotion regulation attempts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".