Promising Psychotherapies for Personality Disorders
Bibliographic record
Abstract
OBJECTIVE: To provide a narrative review of recent research on the psychotherapeutic treatment of patients with personality disorders (PDs). METHOD: We conducted PubMed and PsycINFO searches of recently published articles that reported on the treatment outcomes of psychotherapies for PDs. Our focus was on studies that used randomized controlled trial (RCT) methodology. The search period was from January 2006 to June 2009. RESULTS: The effectiveness of various psychotherapy treatment packages for PDs is well supported by favourable results from RCTs. Beneficial effects of psychotherapy included reduced symptomatology, improved social and interpersonal functioning, reduced frequency of maladaptive behaviours, and decreased hospitalization. Equivalent effects among the interventions we compared were common. Many of the treatments studied required only limited training by therapists. Most studies were focused on treating patients with borderline personality disorder (BPD). Some findings were suggestive of psychotherapy being cost-effective; however, few studies actually included formal cost analyses. Only one study included follow-up of treated patients beyond 1-year posttreatment. CONCLUSIONS: There is strong support for the use of psychotherapy to treat patients with PDs. However, most of the evidence is limited to BPD. The findings of recent studies hold promise for training and practice. Future research should attend to identification of appropriate patient-treatment matches, elucidation of active treatment ingredients, and illumination of factors that are common among treatments that account for their equivalent effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".