Mediation effect of neuropsychological factors on the relationship between social networks and mild cognitive impairment in older adults
Bibliographic record
Abstract
BACKGROUND: Although links between social networks and mild cognitive impairment (MCI) have been suggested. The pathway between social networks and MCI from the Person-Environment-Occupation (PEO) model perspective among older adults remains inconclusive. OBJECTIVE: To examine the relationship between social networks and MCI, and further identify whether neuropsychological factors mediate the relationship. METHODS: 1036 participants aged 55 to 75 years were recruited from two districts of Fuzhou, China, from December 2020 to December 2022. Data were obtained via a face-to-face neuropsychological scale assessment. Social networks and cognitive function were assessed by the Lubben Social Network Scale and the Montreal Cognitive Assessment, respectively. The mediation model and structural equation model (SEM) pathway analysis were used to examine the direct and indirect effects of social networks on MCI via neuropsychological factors. RESULTS: Data from 580 participants were analyzed (year: 65.16 ± 5.38). Regression analysis indicated that higher levels of social networks and psychological resilience were positively correlated with improved cognitive function, even after adjusting for demographic data. Increased depressive symptoms and poor sleep quality were linked to cognitive decline. Support from family members had a greater impact than support from friends in reducing the risk of developing MCI. The SEM model supported the hypothesis that significant indirect effects of social networks on MCI via psychological resilience, depressive tendencies, and sleep quality. CONCLUSIONS: The effects of social networks on MCI are mediated by psychological resilience, depressive tendencies, and sleep quality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".