在COVID-19流行病的12个月内预测焦虑和抑郁症:一项机器学习研究
Brooklynn Bailey1, Daniel R Strunk1
1Department of Psychology, The Ohio State University, Columbus, Ohio, USA.
Journal of clinical psychology
|June 13, 2023
概括
随着COVID-19的流行,人们的焦虑和抑郁症增加了. 在大流行期间,压力,心理因素和医疗条件是这些条件的关键风险因素.
科学领域:
- 精神病学和公共卫生
背景情况:
- 在全球范围内,COVID-19流行病显著增加了焦虑和抑郁症状.
- 了解流行病期间心理健康问题的个体风险因素至关重要.
研究的目的:
- 在COVID-19大流行期间识别累积焦虑和抑郁症严重程度的关键预测因素.
- 检查广泛的社会人口统计学,心理和流行病相关因素.
主要方法:
- 1200名美国成年人在12个月内完成了在线评估.
- 机器学习 (弹性净回归) 分析了68个基线变量.
- 累积焦虑和抑郁症的严重程度使用曲线下面积得分来衡量.
主要成果:
- 感知到的压力和社会人口统计学因素是焦虑的主要预测因素.
- 通用焦虑和抑郁症状的反应性预测了抑郁症.
- 免疫功能受损状态和医疗状况是这两种疾病的重要风险因素.
结论:
- 这项研究提供了大流行相关精神健康问题的风险因素的全面观点.
- 研究结果强调了心理变量和流行病特定因素的重要性.
- 结果可以为面临风险的个人提供有针对性的干预措施.
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