An integrative dimensional classification of personality disorder.
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
Psychological assessment research concerns how to describe psychological dysfunction in ways that are both valid and useful. Recent advances in assessment research hold the promise of facilitating significant improvements in description and diagnosis. One such contribution is in the classification of personality disorder symptomatology. The American Psychiatric Association's diagnostic manual considers personality disorders to be categorically distinct entities. However, research assessing personality disorders has consistently supported a dimensional perspective. Recognition of the many limitations of categorical models of personality disorder classification has led to the development of a variety of alternative proposals, which further research has indicated can be integrated within a common hierarchical structure. This article offers an alternative integrated dimensional model of normal and abnormal personality structure, and it illustrates how such a model could be used clinically to describe patients' normal adaptive personality traits as well as their maladaptive personality traits that could provide the basis for future assessments of personality disorder. The empirical support, feasibility, and clinical utility of the proposal are discussed. Points of ambiguity and dispute are highlighted, and suggestions for future research are provided.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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