Cognitive Function and Subjective Well-Being in Japanese Community-Dwelling Older Adults: A Cross-Sectional Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".