中国全科医学 ›› 2024, Vol. 27 ›› Issue (07): 857-866.DOI: 10.12114/j.issn.1007-9572.2023.0014

• 论著 • 上一篇    下一篇

养老机构智慧化水平测度:理论与实证

田钦瑞1, 李桥兴1,2,*()   

  1. 1.550025 贵州省贵阳市,贵州大学管理学院
    2.550025 贵州省贵阳市,贵州大学喀斯特地区发展战略研究中心
  • 收稿日期:2023-06-14 修回日期:2023-09-16 出版日期:2024-03-05 发布日期:2023-12-19
  • 通讯作者: 李桥兴

  • 作者贡献:田钦瑞负责研究的构思与设计,数据的收集与整理,撰写论文,统计学处理,图、表的绘制与展示;李桥兴进行论文修订,对文章整体负责。
  • 基金资助:
    2023年度贵州省教育厅高校人文社会科学研究项目"贵州省社会保障和救助体系高质量发展研究"(2023GZGXRW152)

Measurement of the Intelligence Level of Elderly Care Institutions: Theory and Demonstration

TIAN Qinrui1, LI Qiaoxing1,2,*()   

  1. 1. School of Management, Guizhou University, Guiyang School of Management, Guiyang 550025, China
    2. Research Institute of Rural Revitalization in Karst Region of China, Guizhou University, Guiyang 550025, China
  • Received:2023-06-14 Revised:2023-09-16 Published:2024-03-05 Online:2023-12-19
  • Contact: LI Qiaoxing

摘要: 背景 随着我国老龄化的日益加剧,探索智慧养老机构的发展问题已经成为国内外养老事业领域的研究重点。 目的 建立养老机构智慧化水平的评价模型,为相关领域的研究工作提供理论借鉴和参考依据。 方法 基于文献分析法和描述性统计构建共含37个三级指标的养老机构智慧化的评价指标体系;基于各评价指标的变量数据进行层次聚类分析获得聚类谱系图;基于"手肘法"提出等级聚类法,并选取上海市43家高端养老机构进行等级聚类的实证分析。 结果 上海市的高端养老机构可分为智慧、半智慧和非智慧等三个类别;超过60%的养老机构属于非智慧型;超过70%的养老机构没有完善的智能设备、健全的智慧监管系统并无法提供深度的心理服务。 结论 上海养老机构的智慧化建设还处于较低的发展水平并存在较大的上升空间。最后基于文献综述和实证结果的分析提出智慧养老机构的内生和外延含义,为相关领域的研究提供参考。

关键词: 养老机构, 智慧养老, 评价体系, 层次聚类

Abstract:

Background

With the increasing aging of China, the exploring of smart elderly care institutions development has become a research focus in the field of elderly care at home and abroad.

Objective

To establish an evaluation model for the intelligence level of elderly care institutions, and provide theoretical reference and basis for research work in related fields.

Methods

A smart evaluation index system for the intelligence level of elderly care institutions containing a total of 37 three-level indicators was constructed based on literature analysis and descriptive statistics; hierarchical clustering analysis was performed based on the variable data of each evaluation index to obtain a cluster pedigree; the hierarchical clustering method was proposed, and 43 top-end elderly care institutions in Shanghai were selected for empirical analysis of hierarchical clustering based on the "elbow method".

Results

Top-end elderly care institutions in Shanghai can be divided into three categories of smart, semi-smart and non-smart; more than 60% of the elderly care institutions are non-smart; more than 70% of the elderly care institutions do not have comprehensive intelligent equipment or have sound intelligent supervision system unable to provide in-depth psychological services.

Conclusion

The intelligent construction of elderly care institutions in Shanghai is still at a low level of development with a large upside. Finally, the endogenous and exogenous meanings of smart elderly care institutions proposed based on the literature review and empirical analysis results can provide reference for research in related fields.

Key words: Elderly care institutions, Intelligent pension, Evaluation system, Hierarchical clustering