Integrated Treatment: A Conceptual Framework for an Evidence-Based Approach to the Treatment of Personality Disorder
Bibliographic record
Abstract
Evidence that various therapies are effective in treating personality disorder and that outcome does not differ substantially across treatments suggests that it is time replace concerns about the efficacy of specific therapies and which form of therapy to use with an evidence-based approach that combines methods that work from all therapies. A framework is proposed for selecting and combining eclectic treatment methods and delivering them in a coordinated way. The framework has two components: (1). a system for conceptualizing personality disorder based on empirical knowledge about the structure, etiology, development, and stability of personality pathology to use as a guide to selecting interventions and planning the sequence in which they will be used; and (2), a model of therapeutic change based on the general literature on psychotherapy outcome and specific studies of PD treatments. The framework proposes that integrated treatment be organized around general principles of therapeutic change common to all effective therapies supplemented with more specific treatment methods taken from the different therapies as needed to tailor treatment to individual patients and treat specific problems and psychopathology. The coordinated delivery of such a diverse array of interventions is achieved by using a phases of treatment scheme that proposes that treatment focus on specific symptoms and problems is a systematic and orderly way according to their stability and potential for change.
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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 | 0.082 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.008 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.013 | 0.013 |
| Research integrity | 0.012 | 0.015 |
| 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".