The association of pathological and “normal-range” personality traits with psychotic-like experiences in a general population sample.
Bibliographic record
Abstract
Transient expressions of positive psychotic symptoms, referred to as "psychotic-like experiences," can be measured in the general population. With an increase in attention to investigate underlying causes of psychotic-like experiences (PLEs), such as personality traits, we investigated pathological personality traits (i.e., using the Personality Inventory for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition) in predicting PLEs, beyond normal-range personality traits. Study results suggest that pathological personality traits have unique prediction power and improve prediction of PLEs, beyond normal-range personality traits. Pathological personality traits significantly improved prediction of PLEs by 18.9% beyond normal-range traits in the general population. When we conducted a multivariate linear regression in reverse order, pathological personality traits explained 62.9% of the variance in predicting PLEs, with normal-range traits explaining ∼2% of the remaining variance. Specifically, pathological personality trait domains of Detachment and Psychoticism improved prediction power beyond normal-range traits in PLEs. The identification of associations between pathological personality traits and PLEs might contribute to "warning signs" for future psychotic-related disorders, beyond prediction power of normal-range personality traits. (PsycInfo Database Record (c) 2021 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".