Dialectical behavior therapy skills training affects defense mechanisms in borderline personality disorder: An integrative approach of mechanisms in psychotherapy
Bibliographic record
Abstract
Objective: Borderline personality disorder (BPD) is characterized by immature defense mechanisms. Dialectical behavior therapy (DBT) is an effective treatment for BPD. However, understanding the underlying mechanisms of change is still limited. Using a transtheoretical framework, we investigated the effect of DBT skills training on defense mechanisms. Method: In this randomized controlled trial, 16 of 31 BPD outpatients received DBT skills training adjunctive to individual treatment as usual (TAU), while the remaining 15 received only individual TAU. Pre–post changes of defense mechanisms, assessed with the Defense Mechanism Rating Scale, were compared between treatment conditions using ANCOVAs. Partial correlations and linear regressions were conducted to explore associations between defenses and symptom outcome. Results: Overall defense function improved significantly more in the skills training condition (F(1, 28) = 4.57, p = .041). Borderline defenses decreased throughout skills training, but not throughout TAU only (F(1, 28) = 5.09, p = .032). In the skills training condition, an increase in narcissistic defenses was associated with higher symptom scores at discharge (β = 0.58, p = .02). Conclusions: Although DBT does not explicitly target defense mechanisms, skills training may have favorable effects on defense function in BPD. Our findings contribute to an integrative understanding of mechanisms of change in BPD psychotherapy.
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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 | 0.001 |
| 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 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".