Suppr超能文献

基于深度学习的困境儿童心理弹性教育结构

The Structure of Mental Elasticity Education for Children in Plight Using Deep Learning.

作者信息

Sun Xuanlu, Yang Xiaoyang

机构信息

School of Humanities and Social Science, Xi'an Jiaotong University, Xi'an, China.

School of Economics and Finance, Xi'an Jiaotong University, Xi'an, China.

出版信息

Front Psychol. 2022 Feb 22;12:766658. doi: 10.3389/fpsyg.2021.766658. eCollection 2021.

Abstract

The purpose is to solve the problem that the current research on the impact of the microstructure of mental elasticity and its constituent factors on the development of the mental elasticity of children is not comprehensive, and the traditional artificial analysis method of mental problems has strong subjectivity and low accuracy. First, the structural equation model is used to study the microstructure of poor children's mental elasticity, and to explore the structural relationship and functional path between the mental elasticity of children and the self-efficacy of their mental health, psychological anxiety, and attachment. Second, a prediction model of mental problems of children in plight based on the backpropagation neural network (BPNN) is constructed. Finally, middle schools in the representative areas of Northwest China are selected as the research unit. The relevant research data are collected by issuing questionnaires, and the data set is constructed to verify the performance of the model. The experimental results show that the average prediction errors of the BPNN model and the support vector regression (SVR) model are 1.87 and 5.4, respectively. The error of BPNN is 65.4% lower than that of SVR, so BPNN has a better performance. The prediction results of the test set show that the actual error and the relative error of the BPNN model are controlled in the range of 0.01, and the prediction accuracy is high. The structural equation model has a high fitting degree. The results of the questionnaire analysis show that attachment, self-efficacy, and psychological anxiety exert a significant direct impact on mental elasticity. This exploration aims to conduct a micro investigation on the relationship among the three core variables (attachment, self-efficacy, and mental health) in the resilience research of children in plight, and analyze their resilience, to provide a theoretical basis for the resilience intervention design of vulnerable groups.

摘要

目的是解决当前关于心理弹性微观结构及其构成因素对儿童心理弹性发展影响的研究不全面,以及传统心理问题人工分析方法主观性强、准确性低的问题。首先,运用结构方程模型研究贫困儿童心理弹性的微观结构,探索儿童心理弹性与心理健康自我效能感、心理焦虑及依恋之间的结构关系和功能路径。其次,构建基于反向传播神经网络(BPNN)的困境儿童心理问题预测模型。最后,选取中国西北代表性地区的中学作为研究单位,通过发放问卷收集相关研究数据,构建数据集以验证模型性能。实验结果表明,BPNN模型和支持向量回归(SVR)模型的平均预测误差分别为1.87和5.4。BPNN的误差比SVR低65.4%,所以BPNN性能更好。测试集的预测结果表明,BPNN模型的实际误差和相对误差控制在0.01范围内,预测准确率高。结构方程模型拟合度高。问卷分析结果表明,依恋、自我效能感和心理焦虑对心理弹性有显著直接影响。本探索旨在对困境儿童心理弹性研究中三个核心变量(依恋、自我效能感和心理健康)之间的关系进行微观考察,分析其心理弹性,为弱势群体心理弹性干预设计提供理论依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c7f/8902162/d36e05249a79/fpsyg-12-766658-g0001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验