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养老机构床位使用率现状及影响因素分析——以安徽省阜阳市为例

Analysis on Current Situation and Influencing Factors of Bed Utilization Rate in Nursing Institutions——Taking Fu yang City,Anhui Province as an Example
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摘要 利用安徽省阜阳市100家养老机构调研数据,采用多元线性回归分析养老机构床位使用率现状及影响因素。研究表明:阜阳市养老机构平均床位使用率为45.3%;机构成立年限与床位使用率之间无显著性差异;民办模式养老机构床位使用率最高;提供员工培训的养老机构床位使用率高于未培训员工的养老机构;县城区域养老机构床位使用率高于农村与集镇;进一步回归分析证实:入住费用负向影响养老机构床位使用率;服务项目、医疗设备数及管理制度对养老机构床位使用率呈显著性正向预测趋势;养老机构可以通过设置合理的价格定位、提供适配化服务、完善医疗设备、建立健全机构管理制度,以期增加养老机构的床位使用率,优化养老机构资源布局。 Based on the survey data of 100 pension institutions in Fu yang City,Anhui Province,the status quo and influencing factors of bed utilization rate in pension institutions were analyzed by multiple linear regression.The privately operated shows that the average bed utilization rate of the pension institutions in Fu yang is 45.3%;There was no significant difference between the establishment years of institutions and the utilization rate of beds;The private type has higher bed utilization rate.Higher bed occupancy rates in nursing facilities that provide training for their staff than in those that do not.The utilization rate of beds in old-age care institutions in county towns is higher than that in rural and market towns;Further regression analysis shows that the occupancy cost negatively affects the utilization rate of beds in old-age care institutions;The service items,the number of medical equipment and the management system have a significant positive prediction trend on the utilization rate of beds in pension institutions;Pension institutions can increase the utilization rate of beds and optimize the distribution of resources by setting reasonable price positioning,providing adaptation services,improving medical equipment and establishing a sound institutional management system.
作者 孔萍 金艾裙 KONG Ping;JIN Ai-qun(School of Humanities,Anhui Polytechnic University,Wuhu 241000,China)
出处 《青岛农业大学学报(社会科学版)》 2022年第4期40-46,共7页 Journal of Qingdao Agricultural University(Social Science)
关键词 养老机构 床位使用率 影响因素 多元线性回归分析 pension institutions bed utilization rate influencing factors multiple linear regression analysis
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