A Meta-analysis and Systematic Review of Emotion-Regulation Strategies in Borderline Personality Disorder
Bibliographic record
Abstract
Emotion dysregulation is often considered a core characteristic of individuals with borderline personality disorder (BPD). With the development and strength of a contemporary affective-science model that encompasses both healthy emotion regulation (ER) and emotion dysregulation, this model has increasingly been used to understand the affective experiences of people with BPD. In this meta-analysis and review, we systematically review six of the most commonly studied ER strategies and determine their relative endorsement in individuals with elevated symptoms of BPD compared to individuals with low symptoms of BPD and healthy controls, as well as to individuals with other mental disorders. Results from 93 unique studies and 213 different effect-size estimates indicated that symptoms of BPD were associated with less frequent use of ER strategies that would be considered more effective at reducing negative affect (i.e., cognitive reappraisal, problem solving, and acceptance) and more frequent use of ER strategies considered less effective at reducing negative affect (i.e., suppression, rumination, and avoidance). When compared to individuals with other mental disorders, people with BPD endorsed higher rates of rumination and avoidance, and lower rates of problem solving and acceptance. We also review important contributions from studies of ER in BPD that we were unable to incorporate into our meta-analysis. We conclude by discussing how the pattern of using ER strategies in BPD contributes to emotion dysregulation and also the potential reasons for this pattern, integrating both Gross's extended process model of ER and Linehan's updated theoretical account on the development of emotion dysregulation.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".