Self-reported Memory Compensation: Similar Patterns in Alzheimer's Disease and Very Old Adult Samples
Bibliographic record
Abstract
Evidence pertaining to self-reported use of memory compensation techniques was collected using the Memory Compensation Questionnaire (MCQ). Five forms of everyday memory compensation were evaluated: (a) external memory aids, (b) internal mnemonic strategies, (c) investing and managing processing time, (d) applying more effort, and (e) reliance on human memory aids. The sample was derived from the Kungsholmen Project in Stockholm, Sweden, and consisted of (n = 85) healthy older adults (M age = 81.80 years; M MMSE = 28.34) and (n = 21) diagnosed Alzheimer's Disease (AD) patients (Mage = 81.80 years; M MMSE = 23.55). Participants were tested on two occasions, 6 months apart. Results showed that the MCQ was a largely reliable instrument in these two groups. Moreover, we observed substantial sample similarity in frequency of using the five forms of everyday memory compensation techniques. The healthy sample reported using the external techniques more than the AD sample. Over the 6-month interval, however, AD patients differentially increased their use of others to assist them in everyday memory performance. Results are interpreted in terms of insight into changes in memory skills and inthe implementation of effective memory support systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".