Predicting <i>Diagnostic and Statistical Manual of Mental Disorders‐IV</i> personality disorders with the five‐factor model of personality and the personality psychopathology five
Bibliographic record
Abstract
Abstract The five‐factor model of personality (FFM), derived from personality trait psychology, is increasingly used to describe personality disorders (PDs). Critics have argued, however, that the personality traits of the FFM fail to capture adequately the full range of personality psychopathology. In this investigation, the personality domains of the personality psychopathology five (PSY‐5), an alternative model designed specifically to assess pathological traits, were compared to the domain traits from the FFM in the prediction of the PD symptom counts. The personality traits from both dimensional models were assessed in a sample of 138 psychiatric patients with the Minnesota Multiphasic Personality Inventory‐2 (MMPI‐2) and the revised NEO personality inventory (NEO PI‐R), respectively. Both instruments significantly predicted all 10 PD symptoms and contributed on average an additional 10% of the variance beyond that predicted by the other instrument. Whereas the MMPI‐2 PSY‐5 scales were comparatively better predictors of paranoid, schizotypal, narcissistic and antisocial PD symptom counts, the NEO PI‐R domain scales outperformed the MMPI‐2 PSY‐5 scales in the prediction of borderline, avoidant and dependent PD symptom counts. Copyright © 2008 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".