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中国公众焦虑状况及影响因素分析:基于百度指数数据。

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data.

机构信息

Institute of New Rural Development, South China Agricultural University, Guangzhou, China.

Centre de Recherche Sur Les Liens Sociaux (CERLIS), Université Paris Descartes, Paris, France.

出版信息

Front Public Health. 2024 Apr 24;12:1360119. doi: 10.3389/fpubh.2024.1360119. eCollection 2024.

Abstract

BACKGROUND

Anxiety disorders have emerged as one of the most prevalent mental health problems and health concerns. However, previous research has paid limited attention to measuring public anxiety from a broader perspective. Furthermore, while we know many factors that influence anxiety disorders, we still have an incomplete understanding of how these factors affect public anxiety. We aimed to quantify public anxiety from the perspective of Internet searches, and to analyze its spatiotemporal changing characteristics and influencing factors.

METHODS

This study collected Baidu Index from 2014 to 2022 in 31 provinces in mainland China to measure the degree of public anxiety based on the Baidu Index from 2014 to 2022. The spatial autocorrelation analysis method was used to study the changing trends and spatial distribution characteristics of public anxiety. The influencing factors of public anxiety were studied using spatial statistical modeling methods.

RESULTS

Empirical analysis shows that the level of public anxiety in my country has continued to rise in recent years, with significant spatial clustering characteristics, especially in the eastern and central-southern regions. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models to examine the relationship between social, economic, and environmental factors and public anxiety levels. We found that the GWR model that considers spatial correlation and dependence is significantly better than the OLS model in terms of fitting accuracy. Factors such as the number of college graduates, Internet traffic, and urbanization rate are significantly positively correlated with the level of public anxiety.

CONCLUSION

Our research results draw attention to public anxiety among policymakers, highlighting the necessity for a more extensive examination of anxiety issues, especially among university graduates, by the public and relevant authorities.

摘要

背景

焦虑症已成为最普遍的心理健康问题和健康关注点之一。然而,先前的研究对从更广泛的角度衡量公众焦虑的关注度有限。此外,虽然我们知道许多影响焦虑症的因素,但我们仍不完全了解这些因素如何影响公众焦虑。我们旨在从互联网搜索的角度量化公众焦虑,并分析其时空变化特征和影响因素。

方法

本研究收集了中国大陆 31 个省份 2014 年至 2022 年的百度指数,以百度指数为基础,从 2014 年至 2022 年衡量公众焦虑程度。采用空间自相关分析方法研究公众焦虑的变化趋势和空间分布特征。采用空间统计建模方法研究公众焦虑的影响因素。

结果

实证分析表明,近年来我国公众焦虑水平持续上升,具有显著的空间集聚特征,尤其是在东部和中南部地区。此外,我们构建了普通最小二乘法(OLS)和地理加权回归(GWR)空间统计模型,以检验社会、经济和环境因素与公众焦虑水平之间的关系。我们发现,考虑空间相关性和依赖性的 GWR 模型在拟合精度方面明显优于 OLS 模型。高校毕业生人数、互联网流量和城市化率等因素与公众焦虑水平呈显著正相关。

结论

我们的研究结果引起了政策制定者对公众焦虑的关注,强调了公众和相关当局有必要更广泛地研究焦虑问题,尤其是针对高校毕业生。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79a2/11077890/08d953f012fc/fpubh-12-1360119-g001.jpg

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