Comparing two domain scoring methods for the Personality Inventory for DSM–5.
Bibliographic record
Abstract
The Personality Inventory for Diagnostic and Statistical Manual of Mental Disorders-5 (PID-5) has become a popular measure of personality pathology, with widespread usage extending beyond its original purpose to aid in the diagnosis of personality disorders. There are 2 methods for scoring the 5 higher order domain scales (Negative Affect, Detachment, Antagonism, Disinhibition, Psychoticism) of this instrument, both of which are used with similar frequency. Krueger, Derringer, Markon, Watson, and Skodol (2012) initially used a scoring method for the 5 domains that included all 25 of the lower order facets. In contrast, the American Psychiatric Association (2013) copyright and publicly available version instructs users to score the domain scales using only 15 of the 25 facets. Our aim in the current study was to compare these 2 scoring methods across various analyses by quantifying the magnitude of any differences in results. The results from both clinical (N = 388) and undergraduate (N = 492) samples supported that the results produced by the 2 domain scoring methods are more similar than different with respect to mean differences, convergent and discriminant correlations with external criteria, and intraclass correlations comparing the consistency between profiles of correlations produced by each scoring method. In contrast, the domain scale profiles for 2 individuals with a borderline personality diagnosis revealed substantive differences for 3 of the 5 domain scales across scoring methods, which has implications for clinical utility. Given these results, we recommend using the 15-facet domain scoring method for research contexts and that more research is needed to determine the optimal scoring method for clinical contexts. (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 | 0.045 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".