Trauma-focused dialectical behavior therapy: study protocol for a randomized controlled multi-center trial in online and face-to-face formats
Bibliographic record
Abstract
BACKGROUND: Borderline Personality Disorder (BPD) is a severe mental health condition that requires intensive psychotherapeutic treatment. Dialectical Behavior Therapy (DBT) is a specialized treatment approach for BPD with broad empirical evidence. However, as with other disorder-specific treatments, the effect sizes of the standard DBT approach are only modest and access to treatment is limited. To enhance the efficacy of DBT, we developed an adaptation called Trauma-Focused DBT (TF-DBT), which is based on the principles, treatment modes, and functions of DBT. The goal was to (i) condense and accelerate the core therapeutic processes of DBT and (ii) expand therapeutic strategies for addressing BPD symptoms beyond Stage I of DBT (i.e., focusing on behavioral dyscontrol). TF-DBT adopts an accelerated experiential approach that is phase-based in its delivery. It emphasizes the processing of maladaptive emotions related to a wide range of developmental relational trauma (i.e., experiences of traumatic invalidation, emotional abuse, bullying, sexual abuse, or neglect in childhood or adolescence). AIM: The primary aim of this study is to investigate the efficacy of this novel DBT adaptation (TF-DBT) compared to standard DBT (S-DBT) as developed by M. Linehan. We hypothesize that TF-DBT is superior to S-DBT on all BPD symptom measures. A second aim of the study is to investigate the efficacy of the delivery format of both treatments (i.e., online vs. face-to-face), with the hypothesis that online therapy is non-inferior to face-to-face treatment. METHODS: This study will enroll N = 260 individuals diagnosed with BPD according to DSM-5. Participants will be randomly assigned to 12 months of outpatient TF-DBT or S-DBT in an online or face-to-face format. DISCUSSION: The expected results might help to improve psychotherapy efficacy for BPD. Additionally, they will improve our understanding of the efficacy of online-delivered DBT treatments which might contribute to facilitating access to treatment. TRIAL REGISTRATION: German Clinical Trials Register: registration number DRKS00031808, date of registration 04 July 2023. WHO Universal Trial Number: U1111-1273-3381.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.012 |
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".