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Record W2900385038 · doi:10.1093/geroni/igy023.951

WIDOWHOOD STATUS AS A RISK FOR COGNITIVE DECLINE AMONG KOREAN OLDER ADULTS

2018· article· en· W2900385038 on OpenAlexaff
Jing Lyu, Joohong Min, G Kim

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsCognitionLongitudinal studyCognitive declineGerontologyPsychologyDepression (economics)Mental healthDemographyHealth and Retirement StudyLatent growth modelingEffects of sleep deprivation on cognitive performanceMedicineDevelopmental psychologyDementiaPsychiatry

Abstract

fetched live from OpenAlex

Previous studies have demonstrated that spousal bereavement has negative impacts on the survivor’s mental health. However, few studies have examined its impact on the survivor’s cognitive function in later life. Therefore, the purpose of this study was to investigate the longitudinal effect of widowhood status on cognitive change among older adults. The study sample was drawn from a nationally representative data set, the Korean Longitudinal Study of Aging (KLoSA), and the final sample consisted of 5,529 Korean adults aged 60 and over at baseline. As a dependent variable, cognitive function was measured by the Korean version of the Mini-Mental State Exam (K-MMSE) score. Widowhood status was measured as a time-varying dichotomous variable. Using five waves of KLoSA, longitudinal trajectories of cognitive change from 2006 to 2014 were examined using growth curve models. Adjusting for gender, education, self-rated health, chronic conditions, and depression, results from growth curve models showed that widowed older adults had significantly lower cognitive function than their counterparts (p<.001). Cognitive function declined over time, and the rate of decline in cognitive function was steeper among the widowed than among the non-widowed (p<.001). These findings suggest that widowhood is detrimental for late-life cognitive decline. Further research is needed to understand the mechanisms underlying this relationship. Policy and practice implications are discussed accordingly.

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 score0.097
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.371
Teacher spread0.346 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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