Associations between the Personality Inventory for DSM-5 trait facets and aggression among outpatients with personality disorder: A multimethod study
Bibliographic record
Abstract
BACKGROUND: Most research on the Personality Inventory for DSM-5 (PID-5) was conducted with self-reports. One of the specific areas for which a multimethod design has yet to be implemented is for the PID-5's associations with aggression. The main objectives of this study were to (a) compare the PID-5 associations with self-reported and file-rated aggression, (b) compare these associations between women and men, and (c) identify the relative importance of PID-5 facet predictors. METHODS: A sample of outpatients with personality disorder (N = 285) was recruited in a specialized public clinic to complete questionnaires, and a subsample was assessed for file-rated aggression (n = 227). Multiple regression analyses were performed with PID-5 facets as statistical predictors but using distinct operationalizations of aggression (self-reported vs. file-rated). Moderation analyses were performed to identify the moderating effect of biological sex. Dominance analyses were computed to identify the relative importance of predictors. RESULTS: PID-5 facet predictors of self-reported and file-rated aggression were very consistent in both conditions. However, the amount of explained variance was reduced in the latter case (from 39% to 14%), especially for women (from 40% to 2%). The most important predictors were Hostility, Risk Taking, and Callousness. CONCLUSION: Pertaining to the statistically significant facets associated with aggression, strong evidence of multimethod replication was found. The women-men discrepancies were not most obvious in their specific associations with aggression, but rather in their amount of explained variance, maybe reflecting examiners' or patients' implicit biases, and/or different manifestations of aggression between women and men.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".