Future Challenges in Psychotherapy Research for Personality Disorders
Bibliographic record
Abstract
PURPOSE OF REVIEW: Individuals with personality disorders are frequently seen in mental health settings. Their symptoms typically reflect a high level of suffering and burden of disease, with potentially harmful societal consequences, including costs related to absenteeism at work, high use of health services, ineffective or harmful parenting, substance use, suicidal and non-suicidal self-harming behavior, and aggressiveness with legal consequences. Psychotherapy is currently the first-line treatment for patients with personality disorders, but the study of psychotherapy in the domain of personality disorders faces specific challenges. RECENT FINDINGS: Challenges include knowing what works for whom, identifying which putative mechanisms of change explain therapeutic effects, and including the social interaction context of patients with a personality disorder. By following a dimensional approach, psychotherapy research on personality disorders may serve as a model for the development and study of innovative psychotherapeutic interventions. We recommend developing the following: (a) an evidence base to make treatment decisions based on individual features; (b) a data-driven approach to predictors, moderators, and mechanisms of change in psychotherapy; (c) methods for studying the interaction between social context and psychotherapy.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".