THE IMPORTANCE OF PERSONALITY TRAITS FOR COGNITIVE HEALTHSPAN
Bibliographic record
Abstract
Abstract The existing literature consistently finds that personality traits are associated with dementia and with risk of mortality. However, few studies have simultaneously examined the impact of traits on dementia while accounting for death as a competing risk, which is critical as dementia and mortality are dependent outcomes and the likelihood of each increases alongside older age. In response, data were drawn from the Rush Memory and Aging Project (N=1954; baseline mean age=80 years; 74% female; up to 23 annual assessments). We used multi-state survival models to investigate whether traits (conscientiousness, extraversion, and neuroticism) were associated with transitions between cognitive status categories (No Cognitive Impairment, NCI; Mild Cognitive Impairment, MCI; dementia) and death. Further, multinomial regression models estimated cognitive healthspan (i.e., years without cognitive impairment) and total longevity. Adjusting for demographics, depressive symptoms, and APOEε4, personality traits were most important in the transition from NCI to MCI; higher conscientiousness was associated with a ~22% decreased risk of transitioning from NCI to MCI (HR=0.78, 95%CI=0.72, 0.85), whereas higher neuroticism was associated with a ~12% increased risk of transitioning from NCI to MCI (HR=1.12, 95%CI=1.04, 1.21). Life expectancy analyses suggested that participants who were higher in conscientiousness were estimated to live longer without cognitive impairment compared to individuals lower in conscientiousness. Findings were similar, though less pronounced, for individuals higher in extraversion or lower in neuroticism. Importantly, none of the traits appeared to be important for total longevity. Together, these results highlight the importance of personality traits, particularly conscientiousness, for maximizing cognitive healthspan.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".