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职业地位的变化及其对自杀念头和抑郁症状的因果影响:使用机器学习算法进行边缘结构模型

Jaehong Yoon, Ji-Hwan Kim, Yeonseung Chung

¹Department of Environmental Health Sciences, Seoul National University, Room 718, Bldg 220, Gwanak-ro 1, Seoul 08826, Republic of Korea. kim.seungsup@snu.ac.kr.

Scandinavian journal of work, environment & health

|March 11, 2024

概括

从标准工作转变为非标准工作,增加了自杀念头和抑郁症状的风险. 这项研究使用机器学习来分析对心理健康结果的因果关系.

科学领域:

社会流行病学社会流行病学
心理健康研究心理健康研究
计量经济学计量经济学

背景情况:

就业状态是健康的重要社会决定因素.
职业的变化,特别是非标准形式的变化,可能会对心理健康产生负面影响.
要了解这种关系,需要使用因果推理方法.

研究的目的:

评估就业状况变化对自杀念头和抑郁症状的因果关系.
应用边际结构模型 (MSM) 与机器学习 (ML) 算法,以实现强大的因果推理.

主要方法:

利用来自韩国福利小组研究 (2013-2020) 的纵向数据.
采用了八个ML算法来构建就业状态变化倾向得分.
应用MSM与反向概率权重来估计因果关系.

主要成果:

随机森林算法显示出最好的性能 (AUC 0.702).
从标准就业到非标准就业的转变与自杀念头的可能性增加2.07倍有关.
在抑郁症状方面也观察到类似的趋势.

结论:

职业地位的转变可以显著提高自杀念头和抑郁症状的风险.
这凸显了稳定的就业对心理健康的重要性.
用ML增强的MSM为分析这种复杂关系提供了强大的工具.

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