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Record W2084204458 · doi:10.1076/jcen.25.3.382.13801

Self-reported Memory Compensation: Similar Patterns in Alzheimer's Disease and Very Old Adult Samples

2003· article· en· W2084204458 on OpenAlexaff
Roger A. Dixon, Grace A. Hopp, Anna-Lisa Cohen, Cindy M. de Frias, Lars Bäckman

Bibliographic record

VenueJournal of Clinical and Experimental Neuropsychology · 2003
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of VictoriaRiverview HospitalUniversity of Alberta
FundersNational Institute on Aging
KeywordsPsychologyAlzheimer's diseaseDegenerative diseaseDevelopmental psychologyCompensation (psychology)DiseaseMemoriaMemory disorderMemory impairmentNeuroscienceCognitive disorderCentral nervous system diseaseCognitive psychologyCognitionCognitive impairmentPsychoanalysisMedicinePathology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.076
GPT teacher head0.421
Teacher spread0.345 · 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 teacher head, 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

Citations46
Published2003
Admission routes1
Has abstractyes

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