Cognitive Emotion Regulation Strategies in Borderline Personality Disorder: Diagnostic Comparisons and Associations with Potentially Harmful Behaviors
Bibliographic record
Abstract
BACKGROUND: Although difficulties in emotion regulation (ER) are considered a core feature of borderline personality disorder (BPD), the specific strategies that individuals with BPD most commonly use, their diagnostic specificity, and their associations with harmful behaviors have not been firmly established. SAMPLING AND METHODS: Individuals with BPD (n = 30), mixed anxiety and/or depressive disorders (MAD; n = 30), and healthy controls (HC; n = 32) completed questionnaires assessing both cognitive ER strategies (e.g., cognitive reappraisal) and potentially harmful behaviors that individuals might use to regulate their emotions (e.g., self-injury). RESULTS: BPD subjects endorsed more maladaptive cognitive ER strategies and fewer adaptive strategies compared to HC. Compared to MAD subjects, BPD individuals endorsed more maladaptive cognitive ER strategies, but only when those with subthreshold symptoms in the MAD group were excluded. BPD also endorsed engaging in potentially harmful behaviors more often than both HC and MAD. Discriminant analysis revealed that MAD endorsed lower rates of problem-solving and cognitive reappraisal compared to both HC and BPD. Higher maladaptive and lower adaptive ER strategies were associated with higher rates of potentially harmful behaviors, although the specific strategies differed for MAD versus BPD. CONCLUSIONS: BPD and MAD endorse cognitive ER strategies with a comparable frequency, although BPD subjects engage in potentially harmful behaviors more often. Subthreshold BPD symptoms may also affect rates of ER strategy use in individuals with other mental disorders.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".