青少年自我接受结构的网络分析
Xinying Li1, Li Guo1, Yuting Li1
1School of Humanities and Social Sciences, Shanxi Medical University, Jinzhong, China.
Discover mental health
|October 29, 2025
概括
这项研究使用了网络分析来了解中国大样本中的自我接受. "我对自己很满意"是最重要的指标,表明了这一点.
科学领域:
- 心理学 心理学 心理学
- 心理健康研究 心理健康研究
- 社会心理学 社会心理学
背景情况:
- 自我接受对心理健康至关重要,但其结构尚未得到充分理解.
- 了解自我接受对于临床研究和心理干预至关重要.
研究的目的:
- 通过网络分析,研究自我接受的结构.
- 在一个庞大的,具有代表性的中国人口样本中,确定自我接受的核心指标.
主要方法:
- 网络分析应用于中国普通人口的大样本 (n=2460).
- 估计了规范化的部分相关性网络,以项目为节点,部分相关性为边缘.
- 进行了中心性分析 (预期影响),以及稳定性和准确性评估.
主要成果:
- 项目z6 ("我对自己很满意") 显示出最高的强度中心性 (EI=1.478),表明它是最有影响力的节点.
- 项目z4 ("我总是害怕做事,因为害怕搞",EI=1.264) 和z16 ("我总是担心人们会视我",EI=1.007) 也显示出高预期的影响.
- 可靠地预测了网络结构和项目中心性.
结论:
- 网络分析显示,自我接受指标之间存在显著的相互关系.
- 中心项目,特别是z6,z4和z16,为改善自我接受的干预提供了潜在的目标.
- 需要进一步的研究,以改进基于这些网络发现的自我接受模型.
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