基于贝叶斯网络模型的社区老年人社会衰弱影响因素分析
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女,硕士在读,护师,

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河北省社会科学基金项目(HB17RK004)


Analysis of influencing factors of social frailty of community-dwelling older adults based on Bayesian network model
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    摘要:

    目的 探讨社区老年人社会衰弱的影响因素及其因素间的网络关系,为开展针对性干预提供参考。方法 采用一般资料调查表、社会衰弱筛查工具、阿森斯失眠量表、衰弱筛查量表、社会支持评定量表、微型营养评估量表、简易精神状态检查表和精简版流调中心抑郁量表对唐山市路北区的1 055名社区老年人进行问卷调查。结果 社区老年人社会衰弱检出率23.70%。logistic回归分析结果显示,年龄、文化程度、慢性病数量、睡眠状况、社会支持、营养状况、体育活动、躯体衰弱、认知功能和抑郁是社会衰弱的影响因素(均P<0.05)。贝叶斯网络模型结果显示,年龄、躯体衰弱、认知功能、抑郁和社会支持与社会衰弱直接相关,体育活动通过慢性病数量、营养状况和抑郁与社会衰弱间接相关;年龄≥70岁,认知障碍且社会支持水平较低,发生躯体衰弱和抑郁的老年人发生社会衰弱的概率最高(达79.0%)。结论 社区老年人社会衰弱发生率偏高,医护人员应关注社会衰弱高危人群,需要通过多学科团队协作和综合性的干预措施,以降低其社会衰弱风险。

    Abstract:

    Objective To explore the influencing factors of social frailty of community-dwelling older adults and their network relationships, and to provide a theoretical basis for targeted intervention. Methods The general data questionnaire, the Help,Participation,Loneliness,Financial & Talk Scale (HALFT), the Athens Insomnia Scale, the FRAlL Questionnaire, the Social Support Rating Scale, Mini Nutritional Assessment Short Form (MNA-SF), the Mini Mental State Examination (MMSE) and the Rasch-derived short form of the Center for Epidemiologic Studies-Depression scale (CES-D) were used to investigate 1 055 elderly people in Lubei District of Tangshan city. Results The incidence of social frailty among the community-dwelling older adults was 23.70%. Logistic regression analysis showed that age, education level, number of chronic diseases, sleep status, social support, nutrition status, physical activity, physical frailty, cognitive function and depression were the main influencing factors of social frailty(all P<0.05). The results of Bayesian network model showed that age, physical frailty, cognitive function, depression and social support were directly associated with social frailty; physical activity was indirectly associated with social frailty through the number of chronic diseases, nutritional status and depression; older adults ≥70 years of age, and those with cognitive impairment and low level of social support, physical frailty and depression had the highest probability of social frailty (79.0%). Conclusion It is common for the community-dwelling older adults to suffer from social frailty. Medical staff should pay attention to the high-risk groups of social frailty, and it is necessary to reduce the risk of social frailty through the collaboration of multidisciplinary teams and comprehensive intervention measures.

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秦艳梅,史雪菲,毛美琦,郝杨,赵雅宁,刘瑶.基于贝叶斯网络模型的社区老年人社会衰弱影响因素分析[J].护理学杂志,2024,39(20):6-10,15

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  • 收稿日期:2024-05-29
  • 最后修改日期:2024-07-31
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  • 在线发布日期: 2024-11-20