Assessing the overlap of personality traits and internalizing psychopathology using multi-informant data: Two sides of the same coin?
Bibliographic record
Abstract
Personality and psychopathology share a hierarchical dimensional structure, developmental trajectories and correlations with varied outcomes. However, quantifying the extent and details of their direct empirical overlap has been hindered by over-reliance on self-reports and broad construct domains. Using multi-method data, we estimated the Big Five personality domains’ and nuances’ (items’) “true” correlations (rtrue) with, and true predictive accuracy (rtruePRED) for, various psychopathology state domains, free of single-method and occasion-specific biases, random error, and direct content overlap. Our sample included Estonian Biobank participants (N = 16,226) who completed psychopathology and comprehensive personality questionnaires, and whose personality traits were also rated by close informants. Personality nuances out-predicted the Big Five domains for psychopathology, with items’ rtruePRED = 0.31…0.58 for specific psychopathology domains of distress, fear, inattention, hyperactivity, insomnia and fatigue, and rtruePRED = 0.52 for the general p-factor. Individual items had various meaningful rtrues with the psychopathology domains. Among the Big Five, neuroticism was the strongest correlate of distress (rtrue = 0.29) and fear (rtrue = 0.13), while inattention was most correlated with conscientiousness (rtrue = –0.56), hyperactivity with extraversion (rtrue = 0.25), fatigue with openness (rtrue = 0.12), and insomnia with conscientiousness (rtrue = 0.12). Associations based on self-reports alone were weaker. We argue for multi-rater and finer-grained assessments of both personality and psychopathology to fully reveal the extent and details of their overlap. This association is likely stronger than typical self-report data suggest, yet psychopathology is not empirically redundant with personality traits.
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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 | 0.092 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".