Dimensional Personality Traits and the Prediction of DSM-IV Personality Disorder Symptom Counts in a Nonclinical Sample
Bibliographic record
Abstract
The third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III; APA, 1980) set forth a categorical system of personality psychopathology that is composed of discrete personality disorders (PDs), each with a distinct set of diagnostic criteria. Although this system is widely accepted and highly influential, alternative dimensional approaches to capturing personality psychopathology have been proposed. Three dimensional models of personality have garnered particular attention-the Five-Factor Model (FFM; Costa & McCrae, 1992), the Seven-Factor Psychobiological Model of Temperament and Character (Seven-Factor Model; Cloninger, Svrakic, & Przybeck, 1993); and the 18-factor model of personality pathology (18-factor model; Livesley, 1986). Although the personality traits from each of these models has been examined in relation to the ten personality disorders in the DSM-IV, no study has examined the comparative and incremental validity of these models in predicting PD symptoms for these ten disorders. Using self-report instruments that measure these models and the ten DSM-IV PDs, correlation and linear regression analyses indicate that traits from all three models had statistically significant associations with PD symptom counts. Hierarchical regressions revealed that the 18-factor model had incremental predictive validity over the FFM and Seven-Fac-tor Model in predicting symptom counts for all ten DSM-IV PDs. The FFM had incremental predictive validity over the Seven-Factor Model model for all ten disorders and the Seven-Factor was able to add incremental predictive validity over the 18-factor model for five of the ten PDs and for eight of the ten disorders relative to the FFM.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".