Exploring the minds of rural seniors: A journey into cognitive health in aging communities
Bibliographic record
Abstract
ABSTRACT Background: Cognitive function in older adults is a crucial aspect of overall health and well-being, particularly as the global population continues to age. Rural areas often face unique challenges that can impact cognitive health, including limited access to health-care services, lower educational opportunities, and lifestyle factors that may differ significantly from urban counterparts. By identifying the predictors of cognitive function and understanding the geographical disparities, the study seeks to inform targeted public health strategies and interventions to support cognitive health in rural populations. Aim: This study aims to investigate the level of cognitive function among older adults in four different rural areas, examining how demographic, socioeconomic, and lifestyle factors contribute to cognitive health. Methodology: A cross-sectional study was conducted with 800 participants (200 from each rural area). Cognitive function was assessed using the mini–mental state examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Demographic, socioeconomic, and lifestyle variables were recorded. Correlation analyses, analysis of variance, analysis of covariance, and multivariable regression analyses were performed to identify significant relationships and differences. Results: The study’s participants had a mean age of 72.4 years, with females comprising 55% of the sample. A quarter of participants reported education beyond primary school, and 42.5% had low socioeconomic status. Smoking was reported by 28.75% of participants, while 46.25% engaged in regular physical activity. Significant differences were observed in MMSE and MoCA scores between rural areas ( P < 0.001), with rural area D scoring the highest (MMSE: 27.5, MoCA: 24.2) and rural area C scoring the lowest (MMSE: 24.1, MoCA: 20.7). Positive correlations were found between cognitive scores and education level (MMSE: r = 0.35, MoCA: r = 0.40) and physical activity (MMSE: r = 0.21, MoCA: r = 0.22), while negative correlations were observed with age (MMSE: r = −0.15, MoCA: r = −0.12), smoking status (MMSE: r = −0.28, MoCA: r = −0.27), and alcohol use (MMSE: r = −0.25, MoCA: r = −0.23). ANOVA indicated significant differences in MMSE scores between areas ( F [3, 796] =12.34, P < 0.001). ANCOVA, adjusting for confounders, confirmed these differences (F[3, 792] =10.47, P < 0.001). Post hoc Tukey tests revealed that rural area D had significantly higher MMSE scores than Areas B and C ( P < 0.01), and Area A had higher scores than Area C ( P < 0.05). Significant factors associated with MMSE scores included age (β = −0.12, P = 0.01), education level (β =0.35, P < 0.001), physical activity (β =0.21, P < 0.05), smoking status (β = −0.28, P < 0.01), and alcohol use (β = −0.25, P < 0.01). Conclusion: Cognitive function among older adults varies significantly across different rural areas. Higher education levels and regular physical activity are associated with better cognitive performance, while older age, smoking, and alcohol use are negatively associated. These findings underscore the importance of targeted interventions to improve cognitive health in rural aging populations.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".