Fuzzy evaluation of adolescents’ health literacy based on early warning theory of psychological risk

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

International Journal of Fuzzy Logic and Intelligent Systems Pub Date : 2021-04-13 DOI:10.3233/JIFS-189903

Feng Sheng, P. Yang

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引用次数: 2

Abstract

The mental health of adolescents not only affects their own healthy growth and success, but is also related to the future of the country and the development of the whole society. Based on the assessment and early warning of adolescents’ psychological problems, different ways to improve adolescents’ health literacy according to the psychological characteristics of adolescents at different ages were explored in this study. The methods of relative superior degree matrix evaluation, entropy value method, coefficient of variation method, subjective and objective weighting method and other methods were employed to establish the fuzzy comprehensive evaluation classification model. MATLAB, SPSS and other software were used for programming to analyze the impact of risk preference, cognitive needs, and decision-making style on the risk of psychological problems of teenagers. The obtained evaluation criterion was used to evaluate the psychological status of respondents. Finally, this study analyzed and established the early-warning mechanism based on the structure model in the system engineering theory, and proposed reasonable solutions to alleviate the risk of psychological problems from the perspective of reality and combines with conclusions.

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基于心理风险预警理论的青少年健康素养模糊评价

青少年的心理健康不仅影响着他们自身的健康成长和成功，而且关系到国家的未来和整个社会的发展。本研究在青少年心理问题评估与预警的基础上，针对不同年龄段青少年的心理特点，探索提高青少年健康素养的不同途径。采用相对优度矩阵评价法、熵值法、变异系数法、主客观加权法等方法建立模糊综合评价分类模型。利用MATLAB、SPSS等软件进行编程，分析风险偏好、认知需求、决策方式对青少年心理问题风险的影响。采用所得评价标准对被调查者的心理状态进行评价。最后，本研究基于系统工程理论中的结构模型对预警机制进行了分析和建立，并从实际出发，结合结论提出了缓解心理问题风险的合理解决方案。

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来源期刊

International Journal of Fuzzy Logic and Intelligent Systems COMPUTER SCIENCE, THEORY & METHODS-

CiteScore

2.80

自引率

23.10%

发文量

期刊介绍： The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.