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Record W7117263648 · doi:10.1002/alz70857_105397

Who Uses Memory Strategies? Exploring the Influence of Cognitive Reserve on Use of Memory Strategies by Older Adults Referred for Neuropsychological Assessment

2025· article· en· W7117263648 on OpenAlexaffabout
Emily Q Wang, Jarod Joshi, Catherine Bosyj, Ana Badal, Kristoffer Romero, Renée K. Biss

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsYork UniversityUniversity of Windsor
Fundersnot available
KeywordsCognitive reserveNeuropsychologyEpisodic memoryNeuropsychological assessmentSemantic memoryCognitionCognitive agingAutobiographical memorySample (material)Memory errors

Abstract

fetched live from OpenAlex

BACKGROUND: In older age, the ability to learn and remember new information is influenced by a myriad of factors, including the use of internal memory strategies. It has been proposed that individual differences in cognitive reserve (CR) may be related to older adults' spontaneous use of these strategies. The current study examined the relative importance and moderating effects of two CR proxies-educational attainment and crystallized intelligence-on older adults' use of a highly effective memory strategy, semantic clustering-grouping words together based on semantic relationships-during a verbal list-learning task. METHOD: This study analyzed data from an archival sample of 185 older adults (n = 83 with normal cognition, n = 102 with mild cognitive impairment [MCI]) referred for neuropsychological assessment at a geriatric hospital in Ontario, Canada. A series of hierarchical regression models and relative weight analyses were conducted to examine the effects of education and crystallized intelligence (WASI Vocabulary scores) on semantic clustering, immediate recall, and delayed recall performance on the Kaplan-Baycrest Neurocognitive Assessment word lists subtest. A moderation analysis examined whether the relationship between semantic clustering and delayed recall was moderated by CR proxies. RESULT: Gender and English language background predicted semantic clustering. Semantic clustering strategy-use accounted for additional variance in memory performance beyond the effects of demographic or reserve variables. While CR proxies did not significantly enhance the predictive value of any model, a moderation analysis found that crystallized intelligence was negatively associated with delayed recall performance in patients with MCI. Semantic clustering predicted delayed recall performance, and the effect was not moderated by education or crystallized intelligence. CONCLUSION: In a clinical sample of older adults presenting for neuropsychological assessment, women and native English speakers were more likely to use semantic clustering, independent of CR. The relationship between CR and memory performance is complex in a clinical setting, where patients may be assessed at different stages of disease progression. Semantic clustering appears to bolster memory performance regardless of an individual's existing level of CR, suggesting that teaching older adults with both low and high CR internal memory strategies may help to reduce everyday memory problems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.084
GPT teacher head0.383
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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