Effects of the social network on health in community‐dwelling older adults: a systematic review
Bibliographic record
Abstract
Abstract Background Many studies have been conducted to analyze the effects of social networks in the elderly, who experience a decline in social networks but only focus on their relationship with one variable. In addition, few studies have analyzed the impact of social networks on the elderly by integrating them into two dimensions of the structural and functional aspects of social networks. This study aims to synthesize the effects of social networks by analyzing the factors related to social networks of the elderly living in the community, including both the structural and functional aspects of the social network. Method Articles from the last 10 years were searched through RISS, NDSL, PubMed, and Embase. Among a total of 1,352 articles, 14 articles were finally selected according to selection and exclusion criteria. The quality of literature was evaluated using the Newcastle‐Ottawa Scale (NOS). The general characteristics of the study, measurement tools, data analysis method, and research result data were extracted and coded. Result A total of 16 factors related to social networks were derived from 14 studies: frailty, institutionalization, functional decline, mortality, fall risk, chronic pain, depression, loneliness, quality of life, advance care planning discussion, self‐efficacy, healthy aging, self‐perception of aging, self‐rated health, health status, health promoting behavior. These factors were classified into five health areas: physical health, mental health, socio‐emotional health, and perceived health and health behavior. Among the 16 factors related to social networks, ‘quality of life’ was found to be the most studied. Conclusion It was confirmed that the social network of the community‐dwelling adults had a significant impact not only on socio‐emotional health, but also on the remaining various health areas such as physical, mental, perceived health, and health behavior of the elderly. Therefore, it is necessary to improve the overall health level of the elderly and to promote and revitalize the social network in terms of prevention of negative health consequences due to aging. At this time, both the structural and functional aspects of the social network should be considered. Finally, this study is expected to serve as basic data for developing intervention programs using social networks.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".