Predictors of Dropout From a 20-Week Dialectical Behavior Therapy Skills Group for Suicidal Behaviors and Borderline Personality Disorder
Bibliographic record
Abstract
Treatment dropout among individuals with borderline personality disorder (BPD) is associated with negative psychosocial outcomes. Identifying predictors of dropout among this population is critical to understanding how to improve treatment retention. The present study extends the current literature by examining both static and dynamic predictors of dropout. Chronically suicidal outpatients diagnosed with BPD (N = 42) were randomly assigned to a 20-week dialectical behavior therapy (DBT) skills training group. Static and dynamic predictors were assessed at baseline, 5, 10, 15, 20 weeks, and 3 months post-intervention. A post-hoc two-stage logistic regression analysis was conducted to predict dropout propensity. Receiving disability benefits at baseline and decreases in mindfulness were associated with significantly increased probability of dropout. Clinicians working with chronically self-harming outpatients diagnosed with BPD would benefit from prioritizing clinical interventions that enhance mindfulness in order to decrease dropout propensity.
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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 | 0.006 |
| 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".