Evolutionary Systems Therapy for Obsessive-Compulsive Personality Disorder: A Five-Case Series
Bibliographic record
Abstract
Obsessive-compulsive personality disorder is the most common personality disorder, with a prevalence of approximately 6.5% in the general population. However, little is known about the effective psychotherapy for this disorder. In this case series, we employed evolutionary systems therapy to support five adults diagnosed with obsessive-compulsive personality disorder. Evolutionary systems therapy is a novel form of psychotherapy that integrates evolutionary psychopathology, compassion-focused therapy, and metacognitively oriented treatments. Primary outcome (personality pathology) was assessed monthly from baseline to follow-up. Secondary outcomes (anxiety, depression, perfectionism, self-criticism, overcontrol) were assessed at initial and final sessions. Moreover, we evaluated the feasibility of intervention (completion, attendance, adverse events). At the end of 6 months of treatment, all the participants reported reliable changes and remitted from diagnosis. These outcomes were maintained at 1-month and 3-month follow-ups. Further research is needed to provide evidence about the acceptability of evolutionary systems therapy in the treatment of obsessive-compulsive personality disorder.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".