摘要
目的评价2012—2021年我国中医类医院卫生人力资源配置的公平性,并预测各类卫生技术人员的数量,为推动我国中医类医院卫生人力资源的合理配置提供参考。方法收集全国中医类医院卫生技术人员的数据,并运用泰尔指数与集聚度分析卫生人力资源配置的公平性;运用灰色GM(1,1)模型预测我国中医类医院卫生技术人员的需求。结果2012—2021年,我国中医类医院卫生人力资源总体呈增长趋势,按各地区泰尔指数分析,泰尔指数贡献率表明地区间差异是造成人员配置差异的主要因素,按地理配置的中医类医院人员集聚度由大到小分别为东部地区、中部地区、西部地区。结论发挥优质医疗资源扩容作用,促进中医资源合理配置;明确目标责任,促进管理精细化,提高医护人员配置公平性;立足传统优势,拓宽培养思路,有效构建多维度中医药人才培养体系。
Objective To evaluate the equity of health human resources allocation in traditional Chinese(TCM)hospitals from 2012 to 2021,and forecast the number of various health technicians,so as to provide theoretical basis for the reasonable allocation of health human resources of TCM health service.Methods The data of health personnel in TCM hospitals were collected,and Theil index and clustering degree were used to analyze the equity of health human resource allocation.The grey GM(1,1)model was used to predict the demand of health personnel in TCM hospitals.Results From 2012 to 2021,the health human resources of TCM hospitals in China showed an overall growth trend.According to the analysis of Theil index in each region,the contribution rate of Theil index showed that the difference between regions was the main factor causing the difference in personnel allocation.The concentration degree of TCM hospitals in terms of geographical allocation was eastern region,central region and western region,respectively.Conclusion The expansion of high-quality medical resources and the rational allocation of TCM resources should be promoted.Clear target responsibility,promote fine management,improve the fairness of medical personnel allocation;Based on traditional advantages,broaden training ideas,and effectively build a multidimensional training system for TCM talents.
作者
娜地达·阿西木
尹悦
吴晓凡
闫丽娜
黄二丹
王忠
Nadida Aximu;Yin Yue;Wu Xiaofan(School of Medicine,Shihezi University,Shihezi,Xinjiang,832000,China;不详)
出处
《中国医院管理》
北大核心
2024年第4期78-82,共5页
Chinese Hospital Management
基金
中国与世界卫生组织2022—2023双年度合作项目(GJ2-2022-WHOPO-E1)。
关键词
卫生资源配置
中医类医院
卫生人力资源配置
公平性
预测模型
health resource allocation
TCM Hospital
health human resource allocation
fairness
prediction model