Target validation in dialectical behavior therapy skills group: Emotion regulation, mindfulness, and distress tolerance as mediators of outcome
Bibliographic record
Abstract
OBJECTIVE: There is increasing interest in identifying mediators in dialectical behavior therapy (DBT), particularly within the broader DBT framework and the specific skills taught in group-based interventions. This study examined whether three core DBT skills-emotion regulation, mindfulness, and distress tolerance- mediate the relationship between group therapy participation and borderline personality disorder (BPD) symptoms in university students. It was hypothesized that improvements in these skills would mediate treatment outcomes, reflecting their role in driving therapeutic change. METHODS: Fifty-four participants were randomly assigned to a 12-week DBT or positive psychotherapy (PPT) group and completed baseline and posttreatment assessments of treatment-specific factors. Data were analyzed using mediation models to examine the relationship between treatment group and BPD symptoms, with emotion regulation, distress tolerance, and mindfulness as mediators. RESULTS: Impulse control and access to emotion regulation strategies were significant mediators of the relationship between treatment group and BPD symptoms, with stronger effects observed in the DBT group compared to the PPT group. Additionally, acting with awareness, a mindfulness skill, was found to mediate treatment outcomes, whereas other mindfulness aspects and distress tolerance were not significant mediators. CONCLUSION: Results highlight the importance of targeting impulse control, emotion regulation, and acting with awareness in the treatment of BPD symptoms.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".