切断
心理学
焦虑
年轻人
项目反应理论
比例(比率)
潜在类模型
心理测量学
临床心理学
结构方程建模
芯(光纤)
荟萃分析
中国人
项目分析
接收机工作特性
焦虑敏感性
情感(语言学)
发展心理学
计算机辅助网络访谈
结构效度
中国
构造(python库)
作者
Hansen Li,Guodong Zhang,Jie Tian,Yang Cao,Haodong Tian,Haowei Liu,Li Huang,Yuping Zhu,Mingyue Yin,Xing Zhang
标识
DOI:10.1080/13548506.2026.2613314
摘要
The Climate Change Anxiety Scale (CCAS) is an emerging psychometric instrument designed to assess climate change anxiety (CCA). This study aimed to preliminarily identify reference cutoff scores and core items of the CCAS in a Chinese adult population. We conducted an online cross-sectional survey in China between May and June 2024, recruiting 653 Chinese adults (mean age = 32.62 ± 7.40 years; 53.8% female) via Wenjuanxing. CCA was assessed using the CCAS. External variables included generalized anxiety (Chinese GAD-7), self-rated sleep quality (single-item, past week), and self-reported experience of meteorological disasters (yes/no). Latent profile analysis (LPA) and receiver operating characteristic (ROC) analyses were used to derive reference cutoff scores, and network analysis was applied to identify core items. LPA supported a two-profile solution and yielded an overall reference cutoff score of 27.5, above which participants were categorized as having elevated CCA risk. Participants classified as high risk reported higher generalized anxiety, poorer sleep quality, and a higher likelihood of meteorological disaster experience. Sex-stratified analyses indicated different optimal cutoffs: 28.5 for males (sensitivity = 1.000; specificity = 0.982) and 26.5 for females (sensitivity = 0.986; specificity = 0.986). Network analysis further suggested that the item 'My concerns about climate change undermine my ability to work to my potential' exhibited the highest centrality, with statistical significance observed only among females. Overall, these findings provide practical, research-oriented evidence for using CCAS-based stratification in Chinese adults and offer preliminary guidance for future subgrouping and sensitivity analyses, while underscoring the need for further validation in broader and more representative samples.
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