中华护理杂志 ›› 2023, Vol. 58 ›› Issue (23): 2901-2909.DOI: 10.3761/j.issn.0254-1769.2023.23.012
许叶华(), 毛孝容(), 关晋英, 曾霞, 王海燕, 陈雪梅, 车洪
收稿日期:
2022-12-20
出版日期:
2023-12-10
发布日期:
2023-12-12
通讯作者:
毛孝容,E-mail:xiaorong_mao@qq.com作者简介:
许叶华:女,本科(硕士在读),主管护师,护士长,E-mail:155122743@qq.com
基金资助:
XU Yehua(), MAO Xiaorong(), GUAN Jinying, ZENG Xia, WANG Haiyan, CHEN Xuemei, CHE Hong
Received:
2022-12-20
Online:
2023-12-10
Published:
2023-12-12
摘要:
目的 分析医护救援人员灾害韧性的影响因素,构建其灾害韧性低水平风险的预测模型,并验证模型的预测效果。 方法 通过便利抽样法及滚雪球法,于2022年5月—7月选取全国18个省(区、市)参与过灾害救援的1 037名医护人员作为调查对象,采用一般资料调查问卷、医疗救援人员灾害韧性测量工具、正念注意觉知量表、简易应对方式问卷及抑郁-焦虑-压力量表精简版进行问卷调查,通过单因素分析及多因素Logistic回归分析确定医护救援人员灾害韧性低水平的影响因素,建立风险预测模型并绘制列线图。采用受试者操作特征曲线(receiver operating characteristic curve,ROC)、校准曲线等对模型效能进行评价,采用Bootstrap法进行内部验证。 结果 Logistic回归分析结果显示,家庭人均月收入、是否参与灾害现场救援、积极应对、正念水平、救援物资充足程度为医护救援人员灾害韧性水平的影响因素(P<0.05)。医护救援人员灾害韧性低水平风险的预测公式为:Logit(P)=8.741-0.381 × 家庭人均月收入-0.891 × 是否参与灾害现场救援-2.544 × 积极应对-0.020 × 正念水平-0.222 × 救援物资充足程度。ROC曲线下面积为0.823,最佳临界值为0.353,灵敏度和特异度分别为79.12%、71.43%。Hosmer-Lemeshow检验结果为χ2=12.250(P=0.140),预测曲线与理想曲线拟合较好。外部验证结果显示,该模型的灵敏度和特异度分别为75.00%、66.39%,总体正确率为69.95%。 结论 该研究构建的预测模型预测效果较好,可为管理者甄选、招募和培训医护救援人员提供参考和指导。
许叶华, 毛孝容, 关晋英, 曾霞, 王海燕, 陈雪梅, 车洪. 医护救援人员灾害韧性低水平风险预测模型的构建及验证[J]. 中华护理杂志, 2023, 58(23): 2901-2909.
XU Yehua, MAO Xiaorong, GUAN Jinying, ZENG Xia, WANG Haiyan, CHEN Xuemei, CHE Hong. Construction and validation of a low-level disaster resilience prediction model for medical rescue workers[J]. Chinese Journal of Nursing, 2023, 58(23): 2901-2909.
表1 医护救援人员的一般情况及灾害韧性水平的单因素分析结果(n=815)
Table 1 General information for medical rescue workers and the results of univariate analysis of disaster resilience(n=815)
表3 医护救援人员灾害韧性水平的多因素Logistic回归分析结果(n=815)
Table 3 Results of multivariate logistic regression analysis of disaster resilience of medical rescue workers(n=815)
图2 两组医护救援人员灾害韧性低水平风险预测模型ROC曲线 注:A为建模组,B为验证组。
Figure 2 Receiver operating characteristic curve of disaster resilience nomogram prediction model for 2 groups of medical rescue workers
图3 两组医护救援人员灾害韧性低水平风险预测模型校准曲线 注:A为建模组,B为验证组。
Figure 3 Calibration curve of disaster resilience nomogram prediction model for 2 groups of medical rescue workers
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