Self-Ratings of Personality Pathology: Insights Regarding Their Validity and Treatment Utility
Bibliographic record
Abstract
Purpose of Review: The validity of self-ratings of personality pathology often are questioned because personality disorders (PD) historically have been viewed as being characterized by poor insight. However, recent research indicates that PD self-ratings are valid in many ways and have significant clinical utility. Building upon this growing literature, our goal here is to provide practical discussion of how incorporating dimensional PD ratings into assessment protocols can benefit diagnosis and treatment. Recent findings: We first review evidence suggesting that PD self-ratings are particularly useful for assessing constructs related to individuals’ own subjective experiences (e.g., propensities for experiencing negative mood states). We then highlight research indicating that PD self-ratings (a) change positively with intervention and (b) meaningfully inform diagnosis, treatment planning, and treatment outcome. Finally, we illustrate how freely available, well-validated self-report PD measures can be used to efficiently obtain clinically useful information in a manner comprehensible to both practitioners and patients. Summary: Self-ratings of personality pathology are valid and useful in many ways and can be efficiently incorporated into assessment protocols. Key future directions for advancing knowledge of self-report PD assessment include examining the extent to which self-ratings of antagonism—a core PD trait—are accurate across contexts.
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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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".