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作 者:汪慧媛 陈惠玲 卢英姿 梅萧萧 吴晓娜 叶增杰 WANG Huiyuan;CHEN Huiling;LU Yingzi;MEI Xiaoxiao;WU Xiaona;YE Zengjie(School of Nursing,Guangzhou University of Chinese Medicine,Guangzhou 510006,Guangdong Province,China)
机构地区:[1]广州中医药大学护理学院,广东广州510006
出 处:《解放军护理杂志》2021年第3期22-26,共5页Nursing Journal of Chinese People's Liberation Army
基 金:国家自然科学基金青年项目(71904033);教育部人文社会科学研究青年基金(19YJCZH227);广州中医药大学2020年度人文社科项目(2020SKXK01)。
摘 要:目的了解护理毕业生留职意愿的潜在类别及其与心理韧性等的关系。方法采用便利抽样法选取河南省郑州市和广东省广州市两所高校共466名护理毕业生为研究对象,采用中文版护士留职意愿问卷及10条目心理韧性量表(Connor-Davidson resilience scale,CD-RISC-10)对其进行调查,针对护理毕业生的留职意愿进行潜在剖面分析,并进行多项式Logistic回归分析探讨护理毕业生留职意愿的潜在类别与心理韧性等的关系。结果护理毕业生留职意愿分为“高留职意愿组”(36.9%),“中留职意愿组”(53.1%),“低留职意愿组”(9.0%)三个潜在类别;有无崇拜的护理前辈、实习体验及心理韧性水平对护理毕业生的潜在剖面分类有预测作用(均P<0.05)。结论护理毕业生留职意愿有三个潜在类别,心理韧性、有无崇拜的护理前辈、实习体验有助于预测其分类。Objective To explore the latent profiles of the intention to remain employed of nursing graduates and their relationships with the graduates’resilience.Methods Convenient sampling was used to select 466 nursing graduates from two universities in Zhengzhou City,Henan Province,and Guangzhou City,Guangdong Province.The Chinese version of Questionnaire for Nurse Intention to Remain Employed and the Connor-Davidson Resilience Scale with 10 items(CD-RISC-10)were used for investigation.Latent profile analysis was adopted to analyze nursing graduates’intention to remain employed.Polynomial logistic regression analysis was conducted to explore the relationships between the latent profiles of nursing graduates’intention to remain employed and their resilience.Results Three latent profiles of nursing graduates’intention to remain employed were reported,including high intention group(36.9%),medium intention group(53.1%)and low intention group(9.0%).Worshiping nursing seniors or not,internship experience and the level of resilience showed predictive roles on the latent profiles of nursing graduates(all P<0.05).Conclusions There are three latent profiles of nursing graduates’intention to remain employed.Resilience,worship of nursing seniors and internship experience are helpful to predict the profile.
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