Emotion profiles in Dialectical Behaviour Therapy: Early Observations Anticipate Treatment Outcome
Bibliographic record
Abstract
Borderline Personality Disorder (BPD) can have various clinical presentations and is also difficult to treat. Researchers have investigated whether subtypes of BPD could explain variability in clinical presentations and outcomes after treatment. Previous research has identified subtypes of BPD based on temperament, which explain some variation in symptoms and outcomes. However, subtypes have typically been created using extensive self-report or structured-interview data. Instead, creating identifiable emotion profiles based on observational data could have a wider range of clinical and research applications, while helping to explain heterogeneity in BPD presentations and outcomes. This thesis is designed to look at emotion profiles in clients with BPD undergoing Dialectical Behaviour Therapy (DBT). Session video recordings were coded and analyzed for 54 clients with BPD, treated in a 12-month randomized controlled trial of DBT at Toronto’s Center for Addiction and Mental Health. Thus, a secondary data set was generated based on clients’ within-session expression of various emotional states, as defined by the coding of affective meaning states (CAMS). This observational measure has been used to analyze within-session therapy processes and to predict outcome data for a wide range of disorders and therapeutic modalities. Across a range of emotion codes, three unique profiles were found using cluster analysis: a Distressed profile, an Ashamed profile, and an Angry/Flexible profile. Additionally, these early observations of emotion profiles were associated with differential treatment outcomes between groups at 6- and 12-months. Most critically, the group with a primarily Ashamed profile showed a lack of reduction in their rates of self-harm at the end of treatment when compared to the other two groups. The main implication of the present study is that early observations of within-session emotion can be prognostic indicators for assessment and treatment planning in DBT.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".