The Impact of Social Support and Resilience on Cognitive Function in Older Adults With Mild Cognitive Impairment: A Cross‐Sectional Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relationship between social support and resilience in cognitive functioning in older adults with mild cognitive impairment (MCI). BACKGROUND: The increase in the number of older adults with MCI poses a serious public health problem. Existing studies suggest that psychosocial factors, such as social support and resilience, can maintain or delay cognitive decline, but the mechanism behind this is not entirely clear. METHODS: A convenient sampling survey of 330 patients with MCI in the community, who were screened from February 2022 to December 2022, was conducted using the General Information Questionnaire, the Montreal Cognitive Assessment Scale (MoCA), the Connor-Davidson Resilience Scale (CD-RISC-10), the Geriatric Depression Scale (GDS-5), and the Social Support Rating Scale (SSRS). SPSS 25.0 and AMOS26.0 software were used for descriptive analysis, correlation analysis, structural equation model, and confirmatory factor analysis. RESULTS: The scores of cognitive function, resilience, and social support were 22.56 ± 1.40, 25.15 ± 3.60, and 35.96 ± 3.76, respectively. Resilience, social support, and cognitive function were all strongly correlated, and resilience mediated between social support and cognitive function. CONCLUSION: Social support and resilience scores are relatively low among Chinese older adults with MCI, which is influenced by various sociodemographic factors. In addition, the findings suggest that social support affects cognitive function through the mediating effect of resilience. Therefore, we should pay more attention to the degree of social support for the elderly with cognitive impairment, enhance the level of resilience, promote healthy behaviors, and delay the occurrence of MCI.
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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 | 0.000 | 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".