Validating the German Version of the Personality Disorder Severity-ICD-11 Scale Using Nominal Response Models
Bibliographic record
Abstract
The ICD-11 features a new classification of personality disorders (PD), focusing on the severity of PD. Although there are numerous self-report measures that assess PD severity, to date only the Personality Disorder Severity ICD-11 (PDS-ICD-11) is based on ICD-11’s operationalization of PD. Initial results indicated that the PDS-ICD-11 measures a unidimensional construct, but the assumptions made for scoring its bipolar items had not been fully examined. The aim of this study is to fill this gap and investigate the latent structure of the German version of the PDS-ICD-11 using nominal response models (NRM), which allow for testing these assumptions. We applied the PDS-ICD-11 together with other self-report measures in a sample of 1,228 individuals from the general population. NRM indicated an acceptable fit of a unidimensional model, with only few deviations from the theoretically imposed scoring scheme. The total score was sufficiently reliable and correlated meaningfully with other self-report measures of PD severity. Regarding DSM-5 and ICD-11 maladaptive trait domains, the total score was found to be most strongly associated with negative affectivity, whereas associations with antagonism and anankastia were small or non-significant. We conclude that the proposed scoring scheme of the PDS-ICD-11 items is acceptable, and the examined psychometric properties of the German version largely correspond to the results from the English-language development study. The total score, however, depicts more internalizing than externalizing personality pathology. Future studies should investigate the diagnostic efficiency of the PDS-ICD-11 scale using multiple methods and time points as well as clinical and forensic 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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".