Everyday memory compensation: The impact of cognitive reserve, subjective memory, and stress.
Bibliographic record
Abstract
To determine the potential importance of several unexplored covariates of everyday memory compensation, the authors examined relations between responses on the Memory Compensation Questionnaire (a self-report measure of everyday memory compensation) and cognitive reserve (education and verbal IQ), subjective memory, and life stress in 66 older adults (mean age = 70.55 years). Key results indicated that compensation occurred in people (a) whose IQ level was greater than their education level (representing cognitive reserve "discordance") but not in people whose IQ was commensurate with their education (representing cognitive reserve "concordance"); (b) who had greater perceived memory errors; and (c) who experienced heightened stress. Further, high-stress older adults compensated whether perceived memory errors were low or high, but low-stress older adults compensated only if they perceived high memory errors. Bootstrapped confidence intervals around model betas provided further support for estimate reliability. These results suggest boundary conditions for the concept of cognitive reserve, and highlight the importance of subjective memory and life stress for defining contexts in which compensation may occur.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".