Contrasting Clients in Dialectical Behavior Therapy for Borderline Personality Disorder: "Marie" and "Dean," Two Cases with Different Alliance Trajectories & Outcomes
Bibliographic record
Abstract
Dialectical Behavior Therapy (DBT; Linehan, 1993a) has garnered a strong evidence base to support its efficacy in treating borderline personality disorder (BPD). Despite this, some clients do not benefit from evidenced-based approaches. There is a recent emphasis on identifying the processes and mechanisms of DBT in order to improve treatment outcomes. This report describes the course of treatment for two individuals who were treated with one-year of standard, outpatient DBT in the context of a randomized control trial. The two clients were selected because (1) both reported poor initial alliances, and (2) they had different outcomes. The first case, "Marie," showed considerable change across a broad range of outcomes whereas the second case, "Dean," made only limited treatment gains. The two cases are contrasted in order to highlight potential factors that may have contributed to the different alliance trajectories and outcomes. We explore several hypotheses to help to explain the relationship between treatment outcome and client characteristics, the therapeutic alliance, the consultation team, and the research context.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".