Mortality Risk Associated With Personality Facets of the Big Five and Interpersonal Circumplex Across Three Aging Cohorts
Bibliographic record
Abstract
OBJECTIVE: To address the common reliance on the global Big Five domains in the personality and longevity literature, the present study examined mortality risk associated with subdimensions of Big Five domains as well as specific traits within the interpersonal circumplex (IPC) model of personality. METHODS: Data were drawn from three major longitudinal studies of aging that administered the NEO Personality Inventory-Revised, a comprehensive measure of the Big Five, and comprised a total of 4223 participants. Item Response Theory models were used to generate latent trait scores for each of the 30 Big Five facets and eight scales from the IPC. Pooled mortality risk estimates were obtained from demographic-adjusted Cox regression models within each study. RESULTS: With a high degree of consistency, the vulnerability facet of neuroticism was associated with higher mortality risk and the activity facet of extraversion, with lower risk. None of the openness or agreeableness facets were associated with mortality, although the IPC scales submissiveness and hostile submissiveness were linked with elevated risk. All but one of the facets in the conscientiousness domain were robustly and consistently associated with lower mortality risk. CONCLUSIONS: Findings indicate that specific facets of neuroticism and extraversion carry greater or lesser mortality risk. Broad composite scales averaging across all facets mask important personality risk factors. In contrast, nearly all facets within the conscientiousness domain confer protection against mortality. Finally, the IPC model may capture more nuanced interpersonal risk factors than the facets of Big Five agreeableness or extraversion. Understanding of the role of personality in longevity requires a more precise approach to conceptualization and measurement than broad, composite constructs usually provide.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".