Emotions observed during sessions of dialectical behavior therapy predict outcome for borderline personality disorder.
Bibliographic record
Abstract
OBJECTIVE: We examined whether the emotions that clients experience within session are associated with treatment outcome in dialectical behavior therapy (DBT) for borderline personality disorder (BPD). METHOD: Participants were 52 adults who met criteria for BPD and were enrolled in a 12-month DBT treatment. The Classification of Affective-Meaning States, an observer-rated measure of discrete emotions, was used to code videos of individual DBT sessions. Raters coded three psychotherapy sessions for each participant: one session from each of the early, working, and late phases of psychotherapy. Self-report measures of BPD symptoms were used to assess treatment outcome. RESULTS: More emotional experience overall during the early phase predicted fewer BPD symptoms at 12-month treatment outcome, explaining 19% of the variance in symptoms. However, increases across treatment in global distress predicted higher levels of BPD (24% of the variance explained) and depression symptoms (15% explained) at termination. Increases in emotional flexibility (i.e., variation between states) from the early to working phase predicted fewer depressive symptoms at termination (14% explained). Self-compassion coded during the working phase also predicted a better treatment outcome (explaining 19%-34%). CONCLUSIONS: Clients' in-session emotional experiences predict treatment outcome 8-10 months later. Clients with BPD may benefit from more overall exploration of their emotional experiences early in DBT, as well as expression of self-compassion. Increases in nonspecific, intense negative affect anticipates poor prognosis, whereas increases in emotional flexibility during early treatment anticipates better prognosis. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.006 |
| 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 | 0.001 |
| 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".