A Hierarchical Integration of Normal and Abnormal Personality Dimensions: Structure and Predictive Validity in a Heterogeneous Sample of Psychiatric Outpatients
Bibliographic record
Abstract
Hierarchical, quantitative models of psychopathology focus primarily on higher-order constructs, whereas less is known about the structure and content comprising lower-order dimensions of psychopathology. Here, we address this gap in the literature by using targeted factor analysis to integrate the 25 maladaptive facet-level traits of the Personality Inventory for Diagnostic and Statistical Manual of Mental Disorder–Fifth edition and the 10 aspect-level traits of the normal personality hierarchy within a sample of 198 psychiatric outpatients. A 10-factor solution replicated previous work, with each of the 10 aspects primarily characterizing only one factor. In addition, the 10 factors differentially predicted a range of diagnoses, including alcohol use disorder, major depression, panic disorder, social anxiety, and borderline and avoidant personality disorders. Our results suggest that research on the development, causes, and structure of lower-order traits within the normal personality hierarchy may serve as an important guide to research on the causes and structure of maladaptive personality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".