Association between social networks and cognitive impairment among older Chinese adults: the mediating effect of depression
Bibliographic record
Abstract
Objectives: This study aimed to explore the rationality of the social networks-depression-cognitive impairment pathway and to provide recommendations for the development of mild cognitive impairment intervention strategies. Methods: A cross-sectional survey was conducted in 2021. Sixteen urban communities in Xi 'an, Shaanxi China were selected as sample sites. The cognitive function, social networks and depression were measured by the Montreal Cognitive Assessment (MoCA), the Lubben Social Network Scale-6 (LSNS-6) and the Geriatric Depression Scale-15 (GDS-15), respectively. The generalized linear model was used to analysis the impact of social networks on cognitive impairment, and further analysis the mediating effect of depression. Results: A total of 745 elderly people aged 60 and above was included in this survey, with an average age of 68.90 ± 6.00 years. The prevalence of cognitive impairment was 18.52%, and the prevalence of cognitive impairment increased with age. According to the generalized linear model, poor social networks (relative network, friend network) was associated with higher risk of cognitive impairment (OR = 2.08, 95% CI: 1.27-3.41), and this association was more significant in women and older adults <70 years of age. Mediation analysis results showed that depression was the mediating path between social networks and cognitive impairment, with the indirect effects accounting for 34.44%. Conclusion: Social isolation increases the risk of cognitive impairment and depression has a significant mediating effect on the relationship between social isolation and cognitive impairment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".