DSM‐IV personality disorders and the Five‐Factor Model of personality: a multi‐method examination of domain‐ and facet‐level predictions
Bibliographic record
Abstract
The personality disorder classification system (Axis II) in the various versions of the Diagnostic and Statistical Manuals of Mental Disorders (DSM) has been the target of repeated criticism, with conceptual analysis and empirical evidence documenting its flaws. In response, many have proposed alternative approaches for the assessment of personality psychopathology, including the application of the Five‐Factor Model of personality (FFM). Many remain sceptical, however, as to whether domain and facet traits from a model of general personality functioning can be successfully applied to clinical patients with personality disorders (PDs). In this study, with a sample of psychiatric patients (n = 115), personality disorder symptoms corresponding to each of the 10 PDs were successfully predicted by the facet and domain traits of the FFM, as measured by a semi‐structured interview, the Structured Interview for the Five Factor Model (SIFFM; Trull & Widiger, 1997) and a self‐report questionnaire, the Revised NEO Personality Inventory (NEO PI‐R; Costa and McCrae, 1992). These results provide support for the perspective that personality psychopathology can be captured by general personality dimensions. The FFM has the potential to provide a valid and scientifically sound framework from which to assess personality psychopathology, in a way that covers most of the domains conceptualized in DSM while transcending the limitations of the current categorical approach to these disorders. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.035 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".