COGNITIVE RESERVE, PHYSICAL HEALTH, AND COGNITIVE FUNCTIONING IN OLDER ADULTS
Bibliographic record
Abstract
Abstract Prior research has shown positive relationships between cognitive reserve (CR), physical health, and cognition, meaning that higher levels of physical health and CR are associated with higher cognitive functioning and vice versa. A group of community-dwelling older adults (N = 45, mean age = 70.5 years) completed a measure of CR (Life Experiences Questionnaire; LEQ), as well as cognitive tests, with number of physician diagnosed health conditions and number of medications measuring physical health. Initially, we ran correlations with the intention of running a mediation model (physical health factor as the independent variable, LEQ as the mediator, and cognitive test scores as dependent variables). Significant correlations were found between physical health and CR (r = -0.44, p = .01) with a medium effect size, and between CR and some test scores. However, there were no correlations between physical health and cognitive scores. Therefore, using linear regression analyses, the LEQ significantly predicted scores on some tests of executive functioning (DKEFS: Colour-Word Interference Test; Trial 3: F(1,39) = 7.42, p = .010), and processing speed (DKEFS: combined colour naming/reading: F(1,32) = 4.32, p = .046). However, the LEQ did not significantly predict verbal fluency, any set-switching tests, or a set-switching and inhibition test. Additionally, when physical health was added to the model, there was no significant improvement. The results suggest that CR may predict some types of executive functioning test scores, but not other executive functioning tests. Additionally, physical health did not predict cognitive test scores in this sample.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".