The Mediating Mechanism of the Digital Divide and Family Integration in the Relationship Between Sleep Disorders and Cognitive Function in Elderly Migrants
Bibliographic record
Abstract
Objective: This study aims to explore the relationship between sleep disorders and cognitive function in elderly migrants, and to analyze the mediating roles of family integration and the digital divide in this relationship. The study provides theoretical foundations and practical guidance for the health management of elderly migrants. Methods: A cross-sectional survey was conducted, involving 300 elderly migrants as subjects. Data were collected using the Pittsburgh Sleep Quality Index (PSQI), Montreal Cognitive Assessment (MoCA), Family Integration Scale, and Digital Skills Scale. Data analysis was conducted using SPSS 26.0 for descriptive statistics and correlation analysis, and AMOS 21.0 was used to construct a structural equation model for mediating effect analysis, exploring the mediating roles of family integration and the digital divide in the relationship between sleep disorders and cognitive function. Results: The study found a significant negative correlation between sleep quality and cognitive function (r = -0.45, p <0.01), while family integration was positively correlated with cognitive function (r = 0.20, p <0.05). Sleep quality was positively correlated with the digital divide (r = 0.35, p <0.01). The structural equation model analysis indicated that family integration and the digital divide played significant mediating roles in the relationship between sleep disorders and cognitive function. Conclusion: Family integration and the digital divide serve as dual mediators in the relationship between sleep disorders and cognitive function in elderly migrants. Improving family integration and reducing the digital divide can effectively enhance their sleep quality and cognitive function. It is recommended that policymakers and social service agencies strengthen family support and digital skills training for elderly migrants to improve their overall health, especially in terms of sleep quality and 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".