A Preliminary Pilot Study Comparing Dialectical Behavior Therapy Emotion Regulation Skills with Interpersonal Effectiveness Skills and a Control Group Treatment
Bibliographic record
Abstract
This pilot study examined the effects of dialectical behavior therapy (DBT) emotion regulation (ER) skills training for borderline personality disorder (BPD). To date, studies have yet to reveal whether specific DBT skill modules uniquely improve ER problems - one of the putative factors considered to underlie clinical problems in BPD. This preliminary examination aimed to characterize the effect sizes of DBT-ER compared to DBT interpersonal effectiveness (DBT-IE) skills training. Women with BPD (N = 19) were randomized to 6-weeks of DBT-ER, DBT-IE, or a control group. BPD symptoms, emotion regulation, and other BPD-relevant outcomes were assessed using self-report and laboratory-based measures. The DBT-ER group demonstrated large effect sizes for improved self-reported reactivity to an emotional stressor, generation of active rather than passive solutions to interpersonal problems, as well as self-reported distress tolerance, mindfulness, and BPD symptoms following treatment. At follow-up, DBT-ER training was also associated with large improvements in ER, social problem solving, and depression. Similarly large effect sizes, however, were sometimes also achieved in the DBT-IE and control condition. Significant condition x time interactions were detected for non-suicidal self-injury and mindfulness, with significant improvements occurring only in the DBT-ER group, whereas such interactions were not detected for other outcomes. These findings, although preliminary, can stimulate future research examining the specificity of DBT skills.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".