Social Networks, Social Integration, and Social Engagement Determine Cognitive Decline in Community-Dwelling Spanish Older Adults
Bibliographic record
Abstract
OBJECTIVES: To examine the influence of social networks and social engagement on cognitive decline in a population-based cohort of elderly people, and to assess gender differences in the effect of social relations on cognition. METHODS: A longitudinal study of community-dwelling people over 65 was carried out. Cognitive function (orientation and memory) in 1997 and cognitive decline (absent, mild, and severe) over 4 years (1993-1997) were assessed using an instrument previously validated for populations with a low level of education. The effect of social networks, social integration, and social engagement with friends, children, and relatives on cognitive function and cognitive decline was estimated by multiple linear and logistic regressions after adjusting for age, sex, education, depressive symptoms, systolic and diastolic blood pressure, and functional status. RESULTS: Poor social connections, infrequent participation in social activities, and social disengagement predict the risk of cognitive decline in elderly individuals. The probability of cognitive decline was lower for both men and women with a high frequency of visual contact with relatives and community social integration. Engagement with friends seemed to be protective for cognitive decline in women but not in men. DISCUSSION: This longitudinal study indicates that few social ties, poor integration, and social disengagement are risk factors for cognitive decline among community-dwelling elderly persons. The nature of the ties that influence cognition may vary in men and women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".