Validation of Personality Inventory for DSM–5 (PID-5) algorithms to assess ICD-11 personality trait domains in a psychiatric sample.
Bibliographic record
Abstract
The International Classification of Disease (11th ed.; ICD-11) personality disorder (PD) proposal characterizes personality psychopathology using an overall impairment severity dimension as well as dysfunctional personality style on the basis of five trait domain qualifiers: Negative Affectivity, Detachment, Dissociality, Disinhibition, and Anankastia. Recent research has indicated that trait facet scales from the Personality Inventory for DSM-5 (PID-5) can be used to index these five broad domains with promising construct validity. Our goal in the current study was to validate the PID-5 algorithms for the five ICD-11 trait domains with some minor adjustments based on the updated ICD-11 text. To this end, we used 343 psychiatric outpatients from a large Canadian metropolitan area, who had completed the PID-5, the Structured Clinical Interview for DSM-IV Axis II Disorders-Personality Questionnaire, the Minnesota Multiphasic Personality Inventory-2 Restructured Form, and the Revised NEO Personality Inventory. The factor structure of the ICD-11 domains was upheld, as expected, and associations with external measures of five-factor model and Personality Psychopathology Five personality traits as well as PD symptom counts adhered to a conceptually expected pattern. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".