Analysis of the interaction between personality dysfunction and traits in the statistical prediction of physical aggression: Results from outpatient and community samples
Bibliographic record
Abstract
The Alternative Model for Personality Disorders (AMPD), included in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.) and the World Health Organization's International Classification of Diseases (11th ed.; ICD-11) are, respectively, hybrid categorical-dimensional and dimensional frameworks for personality disorders (PDs). Both models emphasize personality dysfunction and personality traits. Previous studies investigating the links between the AMPD and ICD-11, and self-reported physical aggression have mostly focused on traits and did not take into account the potential interaction between personality dysfunction and traits. Thus, the aim of this study is to identify dysfunction*trait interactions using regression-based analysis. Outpatients with personality disorder from a specialized public clinic (N = 285) and community participants (N = 995) were recruited to complete self-report questionnaires. Some small-size, albeit significant and clinically/conceptually meaningful personality dysfunction*trait interactions were found to predict physical aggression in both samples. Interaction analyses might further inform, to some degree, about the current discussion pertaining to the potential redundancy between dysfunction and traits, the optimal personality dysfunction structure (in the case of the AMPD), as well as clinical assessment based on AMPD/ICD-11 PD frameworks.
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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 | 0.007 | 0.018 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".