深度学习揭示了多维社会地位驱动人口变化在11875名美国参与者队列中的变化
Justin Marotta1,2, Shambhavi Aggarwal1,2, Nicole Osayande1,2
1McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, Quebec, Canada.
PloS one
|August 13, 2025
概括
社会经济地位 (SES) 显著塑造了人口行为变化,贫困和特权创造了不同的社会分层. 了解这些多维SES因素对于公共卫生至关重要.
科学领域:
- 神经科学是一个神经科学.
- 遗传学 是一个遗传学.
- 社会学 社会学 社会学
背景情况:
- 行为关系受到人口变化的影响,但主要来源仍然不清楚.
- 像青少年大脑认知发展 (ABCD) 研究这样的人口规模队列为分析提供了深入的表型数据.
- 识别人口分层的来源对于理解健康差异至关重要.
研究的目的:
- 通过深度学习来识别和描述人口分层的主要来源.
- 分析社会经济地位 (SES) 与人口变化中的其他社会/环境因素之间的相互作用.
- 探索SES维度如何与行为和心理健康相关.
主要方法:
- 应用了一个有条件变化的自编码器深度学习框架.
- 利用了来自ABCD研究 (8,902种表型,11,875名参与者) 的全部现象数据.
- 分析了202个社会经济地位指标,以确定分层驱动因素.
主要成果:
- 排名前五的分层来源主要是由SES措施 (80%) 推动的.
- 与贫困相关的分层与心理健康,行为,城市化和少数群体有关.
- 与教育/收入机会和有利环境相关的特权相关分层.
- 地理差异突出了与贫困相关的问题和物质贫困地区.
结论:
- 社会经济地位是一个多维构造,对人口变化产生重大影响.
- 健康的社会决定因素,包括SES,对于理解行为差异至关重要.
- 调查结果强调需要在健康研究和政策中考虑各种SES指标.
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