A Framework for Integrating Dimensional and Categorical Classifications of Personality Disorder
Bibliographic record
Abstract
Although empirical evidence strongly supports a dimensional representation of personality disorder, there is strong resistance to dimensional classification due in part to concerns about clinical utility. Acceptance of an evidence-based dimensional classification would be facilitated by information on how such a system would map onto existing diagnoses. With this objective in mind, an integrated framework is proposed that combines categorical and dimensional diagnoses. A two-component classification is adopted that distinguishes between the diagnosis of general personality disorder and the assessment of individual differences in the form the disorder takes. Then, the DSM definition of personality disorders is extended by defining individual disorders as categories of trait dimensions. This makes it possible to develop an integrated classification organized around a set of empirically derived primary traits. Assessments of these traits may then be combined to generate categorical and dimensional diagnoses. It is argued that this approach would introduce an etiological perspective into the classification of personality disorder and improve categorical classification by providing an explicit definition of each diagnosis. The clinical utility of incorporating a dimensional classification is discussed in terms of convenience and acceptability, value in predicting outcomes and treatment planning, and usefulness in organizing and selecting interventions.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".