Examining Therapeutic Alliance as a Mechanism of Change in Dialectical Behavior Therapy for Subgroups of Individuals With Borderline Personality Disorder
Bibliographic record
Abstract
Millions of people worldwide are diagnosed with borderline personality disorder (BPD), a complex mental disorder characterized by significant affect lability, an unstable sense of identity, tumultuous interpersonal relationships, and difficulties managing impulsive behaviours. According to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders, to be diagnosed with BPD, individuals need to present with a minimum of 5 of 9 possible psychiatric symptoms, resulting in 256 possible combinations of symptoms. Given the significant symptom heterogeneity, recent research has examined whether reliable, homogenous subgroups of BPD symptoms exist. If so, it may be that individuals within each subgroup would benefit from tailored modifications to current interventions (namely, dialectical behavior therapy, or DBT) for the treatment of their unique subset of symptoms. In a recent study in our laboratory at the BPD Clinic, Antoine and colleagues (2022) applied a latent class analysis to existing data on the symptoms of individuals diagnosed with BPD and identified three BPD symptom subgroups, or "classes" of BPD symptoms, which are termed the interpersonally unstable, nonaffective labile, and dissociative/paranoid classes, respectively. In the current dissertation, these classes are examined as predictors of psychiatric functioning after individuals with BPD completed 12 months of standard DBT in outpatient mental health hospitals. Moreover, therapeutic alliance, which is defined as the strength of the relationship between the therapist and their patient, is examined as a potential mediator of the relationship between class membership and psychiatric functioning posttreatment. Despite hypotheses that class membership would be predictive of treatment outcome after 12 months of DBT, there were no significant relationships between class membership and psychiatric functioning, deliberate self-harm, or days in treatment, and mediation with the therapeutic alliance did not occur. Nonetheless, DBT was effective across the symptom classes and therapeutic alliance predicted better outcomes. Specific reasons as to why I failed to find meaningful relations between symptom classes and outcomes, clinical implications, and future directions are explored. This dissertation is the first to examine how symptom class inclusion relates to DBT treatment outcomes as mediated by therapeutic alliance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".