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Record W4414068866 · doi:10.3390/geriatrics10050120

Cognitive Function and Subjective Well-Being in Japanese Community-Dwelling Older Adults: A Cross-Sectional Study

2025· article· en· W4414068866 on OpenAlexaboutno aff
Bingyu Li, Tianshu Chu, Le Tian, Hiro Kishimoto

Bibliographic record

VenueGeriatrics · 2025
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsMontreal Cognitive AssessmentCognitionLogistic regressionCognitive impairmentOlder peopleAssociation (psychology)Activities of daily livingCross-sectional study

Abstract

fetched live from OpenAlex

Background: The relationship between mild cognitive impairment (MCI) and subjective well-being remains poorly understood. We examined associations between cognitive function and well-being domains in community-dwelling older Japanese adults with and without MCI. Subjects and Methods: A cross-sectional analysis of 710 community-dwelling Japanese adults aged 65–75 years was carried out. Well-being was measured using the Philadelphia Geriatric Center Morale Scale (PGCMS score ≥ 13 indicates high well-being), comprising agitation, attitude toward aging, and lonely dissatisfaction subscales. MCI was defined as a Montreal Cognitive Assessment (MoCA) score of 18–25. Multivariable logistic regression examined potential associations between socio-demographic and health factors. Results: Among the participants (mean age 70.0 ± 2.5 years, 49% women), 423 (59.6%) had MCI. The MCI status was not associated with overall well-being (OR 1.06, 95% CI: 0.72–1.57, p = 0.77). However, within the MCI group, each 1-point increase in the MoCA score was associated with lower agitation (OR 1.21, 95% CI: 1.04–1.41) but higher lonely dissatisfaction (OR 0.83, 95% CI: 0.70–0.98, p = 0.02). No associations were observed in the non-MCI group. Conclusions: Cognitive function shows domain-specific rather than global associations with well-being in individuals with MCI.

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 score0.022
Threshold uncertainty score0.868

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.341
Teacher spread0.322 · 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
Published2025
Admission routes1
Has abstractyes

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