SOCIAL NETWORKS ASSOCIATED WITH COGNITIVE FUNCTION AMONG CHINESE ELDERS: A ONE-YEAR FOLLOW-UP STUDY
Bibliographic record
Abstract
This study aims to examine social networks of Chinese elders and explore their influences on cognitive function and the risk for elders having mild cognitive impairment (MCI). A longitudinal database of elderly Chinese from urban China during 2014–2015 was analyzed. A sample of 1295 elders was investigated in 2014 and a total of 614 (47.4%) were followed up in 2015. Two types of regression models were used to explore influences of social networks on cognitive function status (a change in the MoCA score) and the risk for having MCI (MoCA score < 26) among these urban elders. The approximate age of this group was 74 years and they had on average one chronic disease. More than half (57%) were at-risk of having MCI. The most important component of their social networks was family (100%) with the majority having face-to-face contact (91.3%). More complex and stable social networks were statistically significant in association with better cognitive function status. The findings imply that developing various types of social ties at an earlier age is beneficial to maintaining cognitive function over time. This study suggests that social interventions and services can provide opportunities for Chinese elders to keep or rebuild connections with old friends, classmates, colleagues, relatives, and acquaintances, which may be more helpful in maintaining cognitive function.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".