摘要
目的构建医学生就业焦虑预测模型。方法采用问卷调查法于2020年12月至2021年3月对某医学院校临床专业的753名学生进行就业焦虑以及相关因素的调查,收集到的数据经过数据清洗和特征选择后,采用机器学习中的随机森林算法构建预测模型。结果模型的预测准确率达86.37%,敏感度达70.42%,特异度达81.85%,受试者工作特征(ROC)曲线下的面积为95.56%。结论基于随机森林算法构建的医学生就业焦虑预测模型,可为筛选在校医学生就业焦虑和心理健康辅导提供科学依据。
Objective To construct an employment anxiety model of medical students.Methods From December 2020 to March 2021,a questionnaire survey was conducted among 753 students of clinical major in a certain college to investigate their employment anxiety and related factors.Data cleaning and feature selection were performed on the collected data.Random forest algorithm in machine learning was used to build a prediction model.Results The prediction accuracy of this model was 86.37%,the sensitivity was 70.42%,the specificity was 81.85%,and the area under receiver operating characteristic(ROC)curve was 95.56%.Conclusion The employment anxiety prediction model of medical students based on random forest algorithm can provide scientific basis for employment anxiety screening and mental health counseling for medical students in colleges.
作者
林鑫
黄雅莲
杨建
唐平
Lin Xin;Huang Yalian;Yang Jian;Tang Ping(School of Psychology,Chengdu Medical College,Chengdu 610500,China;Sichuan Research Center of Applied Psychology,Chengdu Medical College,Chengdu 610500,China;School of Clinical Medicine,The First Affiliated Hospital of Chengdu Medical College,Chengdu 610500,China)
出处
《成都医学院学报》
CAS
2022年第6期764-768,共5页
Journal of Chengdu Medical College
基金
四川省社会科学重点研究基地成都医学院四川应用心理学研究中心面上项目(No:CSXL-202A12)
成都医学院党建项目(No:DJZX20-015)。
关键词
就业焦虑
医学生
随机森林算法
机器学习
Employment anxiety
Medical students
Random forest algorithm
Machine learning
