思考未来:以人口为基础的自然语言处理分析年轻人对未来的希望和担忧
Christina Haag1, David Bürgin2, Lynn A Büeler3
1Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland; Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Journal of affective disorders
|January 23, 2026
概括
年轻人对个人,财务和全球问题表达了各种希望和担忧. 心理健康困扰与特定的担忧有关,为干预提供了目标.
科学领域:
- 心理学 心理学 心理学
- 公共卫生 公共卫生
- 计算语言学 计算语言学
背景情况:
- 青少年的心理健康状况正在下降,需要对焦虑和幸福因素进行研究.
- 开放式的文本回复是未充分利用的资源,可以为青年体验提供新的见解.
- 自然语言处理 (NLP) 的进步使大型文本数据集的有效分析成为可能.
研究的目的:
- 从年轻人对未来的希望和担忧中提取关键主题.
- 调查这些主题与内化症状之间的关系.
主要方法:
- 使用BERTopic建模,分析了来自1113名年轻成年人 (24岁) 的3973个文本段.
- 主题建模为希望和担忧确定了13个不同的主题.
- 回归分析检查了主题与内化症状之间的关联.
主要成果:
- 希望和担忧包括个人,人际关系,工作-生活-财务以及系统/全球问题.
- 希望更多地集中在人际关系上;担忧集中在系统性/全球性问题上.
- 内化症状与人际关系和财务担忧相关,但与系统性担忧 (例如气候变化,冲突) 相反.
结论:
- 调查结果揭示了青年政策和支持的共同,规范性希望和担忧至关重要.
- 与内化症状相关的人际关系和财务问题提出了明确的干预目标.
- 了解这些主题可以为减轻年轻成年人心理痛苦的策略提供信息.
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