Premature Termination of Psychotherapy in Patients With Borderline Personality Disorder
Bibliographic record
Abstract
The goal of the present study was to establish profiles of patients with borderline personality disorder (BPD) who dropped out early from an outpatient psychotherapy program. From a sample of 56 BPD patients who dropped out after the first of a three-year program, a TwoStep cluster analysis procedure was performed, using the five factors of the Treatment Attrition-Retention Scale for Personality Disorders (Gamache et al., J Pers Disord 1-21, 2017) and the Global Assessment of Functioning score (Spitzer et al., Global Assessment of Functioning [GAF] Scale. In Sederer LI, Dickey B [Eds], Outcomes assessment in clinical practice [pp 76-78]. Baltimore, MD: Walter and Williams) as clustering variables. Four clusters emerged: Higher-functioning, Narcissistic features/entitlement, Pseudo-normality, and Highly dysfunctional. Differences between the clusters were found on sex, occupational status, and presence of antisocial features. These findings could help both identify BPD patients at potential risk of dropping out of psychotherapy and adjust interventions accordingly to reduce premature termination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".