Future Challenges in Psychotherapy Research for Personality Disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it