Personality traits, cognitive states, and mortality in older adulthood.
Bibliographic record
Abstract
= 80 years; 74% female) over up to 23 annual assessments. Multistate survival modeling examined the extent to which conscientiousness, neuroticism, and extraversion, assessed using the NEO Five Factor Inventory, were associated with transitions between cognitive status categories and death. Additionally, multinomial regression models estimated cognitive health span and total survival based on standard deviation units of personality traits. Adjusting for demographics, depressive symptoms, and apolipoprotein (APOE) ε4, personality traits were most important in the transition from no cognitive impairment (NCI) to MCI. For instance, higher conscientiousness was associated with a decreased risk of transitioning from NCI to MCI, hazard ratio (HR) = 0.78, 95% CI [0.72, 0.85] and higher neuroticism was associated with an increased risk of transitioning from NCI to MCI, HR = 1.12, 95% CI [1.04, 1.21]. Additional significant and nonsignificant results are discussed in the context of the existing literature. While personality traits were not associated with total longevity, individuals higher in conscientiousness and extraversion, and lower in neuroticism, had more years of cognitive health span, particularly female participants. These findings provide novel understanding of the simultaneous associations between personality traits and transitions between cognitive status categories and death, as well as cognitive health span and total longevity. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".