The Personality Inventory for ICD-11: Investigating Reliability, Structural and Concurrent Validity, and Method Variance
Bibliographic record
Abstract
The eleventh edition of the International Classification of Diseases and Related Health Problems (ICD-11), recently approved by the World Health Organization, contains a new diagnostic approach for personality disorders. This approach partly involves the consideration of five dimensional trait domain qualifiers – Negative Affectivity, Detachment, Dissocial, Disinhibition, and Anankastia. Oltmanns and Widiger (2018) recently developed a self-report measure, the Personality Inventory for ICD-11 (PiCD), to assess the five domains; however, further examination of the psychometric properties of the PiCD is warranted due to its limited research base. The present study aimed to further examine the reliability, structural and concurrent validity, and method variance of the PiCD in an ethnically-diverse undergraduate sample (N = 518), who were also administered the Minnesota Multiphasic Personality Inventory–2–Restructured Form (MMPI-2-RF). First, results suggested that the PiCD domain scales exhibited adequate internal consistency reliability via coefficient categorical omega (range = .77 - .87). Next, exploratory structural equation modeling results suggested support for a four-factor solution, with the fourth factor thought to represent a bipolar continuum of Anankastia to Disinhibition severity. Random-intercept factor analysis results suggested a small amount of variance in items (4.88%) attributable to idiosyncratic scale usage. Lastly, relations between PiCD domains and MMPI-2-RF scales (PSY-5 and Higher-Order scales) provided support for the validity of the Negative Affectivity, Detachment, and Dissocial domains, though relatively less support for Disinhibition and Anankastia. Further examination of other psychometric properties and the nomological network of the PiCD is recommended.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".