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Record W2901486402 · doi:10.1177/0898264318813019

Memory Decline and Depression Onset in U.S. and European Older Adults

2018· article· en· W2901486402 on OpenAlexaff
Rebecca Bendayan, Amanda K. Weiss Kelly, Scott M. Hofer, Andrea M. Piccinin, Graciela Muñiz‐Terrera

Bibliographic record

VenueJournal of Aging and Health · 2018
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Victoria
FundersMedical Research CouncilNational Institutes of HealthHealth Services and Delivery Research ProgrammeNational Institute for Health and Care ResearchSouth London and Maudsley NHS Foundation TrustUniversity of MichiganNational Institute on AgingKing's College LondonEuropean CommissionU.S. Social Security Administration
KeywordsDepression (economics)CohortDemographyDepressive symptomsCohort studyPsychologyGerontologyRecallHealth and Retirement StudyMedicinePsychiatryCognitionInternal medicine

Abstract

fetched live from OpenAlex

Objectives: We explore the association between different patterns of change in depressive symptoms and memory trajectories in US and European Mediterranean (Spain, France, Italy, and Israel) and non-Mediterranean (Sweden, Denmark, Netherlands, Germany, Belgium, Switzerland, and Austria) older adults. Methods: Samples consisted of 3,466 participants from the Health Retirement Study (HRS) and 3,940 participants from the Survey of Health, Aging and Retirement (SHARE). Individuals were grouped as follows: non-case depression (NO DEP), persistent depression (DEP), depression onset (ONSET), depression recovery (RECOV), and fluctuating (FLUCT). Memory was measured using immediate and delayed recall tests. Linear mixed models were used. Results: DEP and RECOV had significantly lower baseline memory scores compared to NO DEP, at intercept level. At slope level, ONSET had a significantly faster decline in both tasks compared to NO DEP. Discussion: Cross-cohort robust and consistent new empirical evidence on the association between depression onset and faster decline in memory scores is provided.

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)0.002
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 score0.289
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.048
GPT teacher head0.420
Teacher spread0.372 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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