Big Five aspects of personality interact to predict depression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Research has shown that three personality traits-Neuroticism, Extraversion, and Conscientiousness-moderate one another in a three-way interaction that predicts depressive symptoms in healthy populations. We test the hypothesis that this effect is driven by three lower-order traits: withdrawal, industriousness, and enthusiasm. We then replicate this interaction within a clinical population for the first time. METHOD: Sample 1 included 376 healthy adults. Sample 2 included 354 patients diagnosed with current major depressive disorder. Personality and depressive tendencies were assessed via the Big Five Aspect Scales and Personality Inventory for DSM-5 in Sample 1, respectively, and by the NEO-PI-R and Beck Depression Inventory-II in Sample 2. RESULTS: Withdrawal, industriousness, and enthusiasm interacted to predict depressive tendencies in both samples. The pattern of the interaction supported a "best two out of three" principle, in which low risk scores on two trait dimensions protects against a high risk score on the third trait. Evidence was also present for a "worst two out of three" principle, in which high risk scores on two traits are associated with equivalent depressive severity as high risk scores on all three traits. CONCLUSIONS: These results highlight the importance of examining interactive effects of personality traits on psychopathology.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it