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Record W2003807574 · doi:10.2190/9vaa-kmyv-u2hu-pvaw

Interrelations between Subjective Health and Episodic Memory Change in Swedish and Canadian Samples of Older Adults

2003· article· en· W2003807574 on OpenAlexaffabout
Åke Wåhlin, Scott B. Maitland, Lars Bäckman, Roger A. Dixon

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of AlbertaUniversity of Guelph
FundersNational Institute on Aging
KeywordsEpisodic memoryLongitudinal studyPsychologyGerontologyPerceptionCognitionClinical psychologyDemographyDevelopmental psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Recent research has documented associations between subjective health ratings and objective indicators of disease and death. Less is known about relations between subjective health ratings and level of cognitive performance in older adults. In this study, we explored whether subjective health ratings are related to episodic memory performance, both concurrently and across a three-year longitudinal interval. Persons aged 75-84 years, and participating in the Swedish Kungsholmen Project (n = 105) or the Canadian Victoria Longitudinal Study (n = 71), were examined. Results showed that in both samples, while the cross-sectional relationship was non-significant, longitudinal change in perceptions of subjective health were related to change in episodic memory performance. Next, the two samples were combined in additional analyses. Here, results further revealed that the associations between longitudinal change in subjective health and memory performance generalized across samples independently of demographic, changing physical health status, and subjective memory decline differences. Thus, the present findings suggest that subjective health may be added to the growing number of individual-difference variables that are predictive of episodic memory change in very old age.

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 score0.233
Threshold uncertainty score0.875

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 head0.055
GPT teacher head0.350
Teacher spread0.294 · 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

Citations18
Published2003
Admission routes2
Has abstractyes

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