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了解COVID-19大流行期间国家焦虑的内部动态:通过面板网络分析的七波纵向发现
1Computational Communication Collaboratory, School of Journalism and Communication, Nanjing University, Nanjing, China.
Applied psychology. Health and well-being
|September 22, 2024
概括
被视为一个网络系统的状态焦虑,揭示了复杂的积极和消极症状关系. 这种网络方法为焦虑理论和临床干预提供了新的见解.
科学领域:
- 心理学 心理学 心理学
- 网络科学 网络科学
背景情况:
- 传统的研究假设焦虑症状的常见原因.
- 状态焦虑可以有效地作为一个动态网络系统来建模.
研究的目的:
- 用网络分析来描述状态焦虑的内部动态.
- 探索随着时间的推移,个体内部和个体之间的症状关系.
主要方法:
- 使用了COVID-动态数据集 (波7-13,N=1,042).
- 采用高斯图形模型和强度中心性来估计网络模型.
- 分析了主体间,当代和时间网络.
主要成果:
- 确定了焦虑症状之间的积极和意想不到的负面关系.
- 在主体间 (3个社区) 和当代 (2个社区) 网络中检测到社区结构.
- 在时间网络中观察到积极的自相关性和混合的预测.
结论:
- 国家焦虑表现出复杂的网络动态,挑战传统的单维观点.
- 研究结果支持基于网络的焦虑概念化.
- 对在个人和群体层面开发有针对性的临床干预措施的影响.
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