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目的:了解护士的心理健康状态，并探讨一般资料、应对方式、社会支持及工作适应障碍水平对护士心理健康状态的影响，初步构建临床护士心理健康状况的预测模型。方法:采用便利抽样法，选取2020年2月某市2家医院的374名临床护士作为研究对象。采用一般资料自制问卷、症状自评量表（SCL-90）、应对方式问卷、社会支持评定量表、工作适应障碍量表进行调查。将心理健康状况作为二分类资料处理，并利用神经网络中的多层感知器构建临床护士心理健康状况的预测模型，同时对该模型进行验证和分析。结果:374名临床护士的SCL-90得分为（123.06±41.70）分，应对方式问卷得分为（27.90±12.14）分，社会支持评定量表得分为（38.84±8.46）分，工作适应障碍量表得分为（9.72±6.35）分。相关性分析结果显示，工作年限、自责、幻想、退避、合理化、工作适应障碍得分与心理健康状况得分呈正相关（n P<0.05）；是否为编制、解决问题、求助、主观支持、支持利用度与心理健康状况呈负相关（n P<0.05）。将以上与心理健康状况相关的变量纳入预测模型中，将374名样本分为训练样本（177名）、检验样本（81名）和坚持样本（116名）。结果显示，训练、检验和坚持样本的建模准确率分别为79.7%、86.4%和81.0%，ROC曲线下面积为0.810。预测模型中正态化重要性为100%的变量为工作适应障碍。n 结论:本研究构建的临床护士心理健康状况预测模型的准确率较好，且神经网络模型的应用较简单，可作为评估临床护士心理健康状况的有效方法。“,”Objective:To understand the mental health of nurses, and to explore the influence of general information, coping styles, social support and work adaptation barriers on the mental health of nurses, and to initially construct a prediction model of clinical nurses' mental health.Methods:In February 2020, convenience sampling was used to select 374 clinical nurses from two hospitals in a city as the research objects. All nurses were investigated with the self-designed general information questionnaire, Symptom Checklist 90 (SCL-90) , Coping Style Questionnaire, Social Support Rating Scale (SSRS) and Work Adjustment Disorder Scale. The mental health was processed as binary data, and the multi-layer perceptron in the neural network was used to construct a prediction model of the clinical nurses' mental health, and the model was verified and analyzed.Results:Among 374 clinical nurses, the scores of SCL-90, Coping Style Questionnaire, SSRS and Work Adjustment Disorder Scale were (123.06±41.70) , (27.90±12.14) , (38.84±8.46) and (9.72±6.35) respectively. Correlation analysis results showed that working years, self-blame, fantasy, avoidance, rationalization and work adjustment disorder were positively correlated with mental health (n P<0.05) ; whether they were authorized strength, problem solving, asking for help, subjective support, support utilization were negatively correlated with mental health (n P<0.05) . The above variables related to mental health were incorporated into the prediction model, and 374 samples were divided into training samples (177) , test samples (81) and persistence samples (116) . The results showed that the modeling accuracy of training, testing and persistence samples were 79.7%, 86.4% and 81.0%, respectively, and the area under the receiver operating characteristic curve was 0.810. The variable which normalization importance was 100% in the prediction model was the work adjustment disorder.n Conclusions:The prediction model of clinical nurses' mental health constructed in this study has good accuracy, and the application of neural network model is relatively simple, which can be used as an effective method to evaluate the mental health of clinical nurses.