Predictors of Premature Termination of Day Treatment for Personality Disorder
Bibliographic record
Abstract
BACKGROUND: Premature termination is a common problem in the treatment of personality disorder. Efforts to improve compliance should begin by recognising risk factors for premature termination. This prospective study identified predictors of premature termination from a day treatment program for personality disorder. METHODS: Consecutively admitted patients with a personality disorder (n = 197) were assessed with self-report and interview measures. Patient personality characteristics were the primary predictors. Others were demographic, initial disturbance, and personality disorder variables. Cox proportional hazards regression was used. RESULTS: Risk of terminating prematurely significantly increased if the patient had been previously hospitalised for psychiatric difficulties, was younger, had fewer prior contacts with health and social services, and had more severe borderline personality disorder traits. CONCLUSIONS: Information about which patients are at high risk for premature termination can help clinicians take measures to modify the risk. This might involve selection decisions, pre-treatment preparation, close monitoring during treatment, or addition of adjunctive interventions.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".