Psychotherapy for Personality Disorders in a Natural Setting
Bibliographic record
Abstract
Long-term assessment of the effects of psychotherapy for personality disorders (PDs) in a natural environment is an important task. Such research contributes to enlarge the practice-based evidence, embedded in broad collaborations between clinicians and researchers in psychotherapy for PDs. The present pilot study used rigorous assessment procedures and incorporated feedback loops of outcome information to the therapists in demonstrating the effects of psychotherapy for PD in a natural setting. The number of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), criteria for any PD was the primary outcome (along with psychological distress, depression, impulsiveness, and quality of life as secondary measures), assessed at intake, 6, 12, 18, and 24 months of psychotherapy for N = 13 patients with PD. Data were analyzed using hierarchical linear modeling. Results demonstrated a large pre-post effect (d = 2.22) for the observer-rated measure (primary outcome), and small to medium effects for the secondary outcomes; these results were corroborated by a steady decrease of symptoms over all five time points, which was significant for several outcomes. These results add a piece to the literature by demonstrating the effects of long-term psychotherapy for PDs in increasingly diverse contexts and suggest that practice-oriented research can be carried out in a collaborative and systematic manner.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".