Skills Training as an Adjunctive Treatment for Personality Disorders
Bibliographic record
Abstract
Personality disorders are usually treated with either psychodynamic or supportive psychotherapy, with psychotropic medication often used as an adjunctive treatment. However, patients with personality disorders pose special treatment issues, because their problems are pervasive and long-standing and they have entrenched deficits in many areas of functioning. In this article, the authors consider the role of skills training in the treatment of personality disorders. They describe a two-pronged approach to the treatment of personality disorders based on a model of deficit compensation. Because skills training has only been investigated in a very limited way in personality disorders, the authors first review research on the use of skills training in Axis I disorders as background for a discussion of ways in which skills training may be applicable to the treatment of Axis II disorders. They describe a number of skills training approaches (social skills training, anger management, and conversational skills) that can be used for a variety of different Axis I disorders. Skills training approaches that are targeted to specific types of disorders (substance use disorders, schizophrenia, anxiety disorders, adolescent problems) are also described. The authors then review what is known about the use of skills training in borderline personality disorder and avoidant personality disorder, and propose skills training approaches that may be useful for patients with paranoid personality disorder and antisocial personality disorder. Case examples are provided to demonstrate how skills training for personality disorders can be applied in clinical practice.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".