INVESTIGATION OF PERSONALITY USING DIFFERENT TIME MATRICES, CONTROL VARIABLES, AND INCLUSION GROUPS
Bibliographic record
Abstract
Two studies suggest that personality change may be an early indicator of dementia (Balsis et al, 2005; Smith-Gamble et al, 2001); however, these studies did not assess personality trait change. Although Yoneda et al (2015) prospectively examined personality traits, the nature of the analyses did not allow comparison between trajectories in normal and abnormal aging. The current study includes comparison of trajectories of extraversion and neuroticism personality traits in individuals who did and did not receive a dementia diagnosis. This study used data from the OCTO-Twin Study, Longitudinal Aging Study Amsterdam, Swedish Adoption Twin Study of Aging, and Einstein Aging Study. For each dataset, a series of latent growth curve models were run examining each personality trait, first including a subsample of individuals eventually diagnosed with dementia and time-to-dementia metric, and second including the entire dataset, dementia diagnosis as a variable, and time-in-study metric. Controlling for sex, age, education, depressive symptoms, and the interaction between age and education, the first series of analyses revealed a consistent pattern of personality change preceding dementia diagnosis across datasets, specifically linear increases in neuroticism and stability in extraversion. The second series of analyses revealed a less stable pattern of results: dementia diagnosis was only a significant predictor of neuroticism trajectories in some datasets. These findings will be discussed. Identification of early indicators of dementia, specifically how personality changes differ for healthy individuals compared to individuals eventually diagnosed with dementia, may aid in early care strategies and facilitate development of screening assessments.
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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 | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".