A COORDINATED ANALYSIS EXAMINING PERSONALITY CHANGE IN OLDER ADULTS: CONSISTENT RESULTS DESPITE HETEROGENEITY BETWEEN DATASETS
Bibliographic record
Abstract
Three high-quality longitudinal studies were selected to examine personality trait changes in the context of different cognitive outcome (i.e., dementia, Mild Cognitive Impairment, and no diagnosis). The datasets varied in several characteristics, including intervals between measurement occasions, number of occasions, assessment of personality, and criteria for diagnosis of dementia. A series of latent growth curve models were fitted independently for each dataset to estimate trajectories of personality traits in: 1) individuals with incident dementia (N=295), 2) individuals with incident MCI (N=135), and 3) individuals who did not receive a diagnosis during the observation period (N=2109). Linear increases in neuroticism were consistently observed in individuals with MCI and dementia diagnoses across datasets. Analyses examining individuals without a diagnosis consistently revealed non-significant change over time. Discussion will focus on the implications of consistent results across datasets when applying conceptual replication, despite heterogeneity in key features of datasets.
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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 | 0.057 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".