Big Five aspects of personality interact to predict depression
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".