The Psychometric Properties of the Personality Inventory for <i>DSM-5</i> in an APA <i>DSM-5</i> Field Trial Sample
Bibliographic record
Abstract
Section 3 of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) includes a hybrid model of personality pathology, in which dimensional personality traits are used to derive one of seven categorical personality disorder diagnoses. The Personality Inventory for DSM-5 (PID-5) was developed by the DSM-5 Personality and Personality Disorders workgroup and their consultants to produce a freely available instrument to assess the personality traits within this new system. To date, the psychometric properties of the PID-5 have been evaluated primarily in undergraduate student and community adult samples. In the current investigation, we extend this line of research to a psychiatric patient sample who participated in the APA DSM-5 Field Trial (Centre for Addiction and Mental Health site). A total of 201 psychiatric patients (102 men, 99 women) completed the PID-5 and the Revised NEO Personality Inventory (NEO PI-R). The internal consistencies of the PID-5 domain and facet trait scales were acceptable. Results supported the unidimensional structure of all trait scales but one, and the convergence between the PID-5 and analogous NEO PI-R scales. Evidence for discriminant validity was mixed. Overall, the current investigation provides support for the psychometric properties of this diagnostic instrument in psychiatric samples.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".