Suppr超能文献

青春期和成年早期的交叉性与抑郁:1995-2008 年国家青少年健康纵向研究的 MAIHDA 分析。

Intersectionality and depression in adolescence and early adulthood: A MAIHDA analysis of the national longitudinal study of adolescent to adult health, 1995-2008.

机构信息

Department of Sociology, 1291 University of Oregon, Eugene, OR, 97403, USA.

Department of Sociology, 1291 University of Oregon, Eugene, OR, 97403, USA.

出版信息

Soc Sci Med. 2019 Jan;220:1-11. doi: 10.1016/j.socscimed.2018.10.019. Epub 2018 Oct 26.

Abstract

Depression in adolescents and young adults remains a pressing public health concern and there is increasing interest in evaluating population-level inequalities in depression intersectionally. A recent advancement in quantitative methods-multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)-has many practical and theoretical advantages over conventional models of intercategorical intersectionality, including the ability to more easily evaluate numerous points of intersection between axes of marginalization. This study is the first to apply the MAIHDA approach to investigate mental health outcomes intersectionally in any population. We examine intersectionality and depression among adolescents and young adults in the U.S. along dimensions of gender, race/ethnicity, immigration status, and family income using a large, nationally representative sample-the National Longitudinal Study of Adolescent to Adult Health. We find evidence of considerable inequalities between social strata, with women, racial/ethnic minorities, immigrants, and low income strata experiencing elevated depression scores. Importantly, the majority of between-strata variation is explained by additive main effects, with no strata experiencing statistically significant residual "interaction" effects. We compare these findings to previous intersectional research on depression and discuss possible sources of differences between MAIHDA and conventional intersectional models.

摘要

青少年和年轻人的抑郁症仍然是一个紧迫的公共卫生问题,人们越来越感兴趣的是评估抑郁症的人口不平等现象。最近,一种新的定量方法——个体异质性和判别准确性的多层次分析(MAIHDA)——在理论和实践上都比传统的分类交叉模型具有许多优势,包括更轻松地评估边缘化轴之间的众多交叉点的能力。本研究首次应用 MAIHDA 方法来研究美国青少年和年轻人的心理健康结果,涉及性别、种族/民族、移民身份和家庭收入等方面。我们使用一个大型的全国代表性样本——青少年到成人健康纵向研究,发现了社会阶层之间存在相当大的不平等现象,女性、少数族裔、移民和低收入阶层的抑郁评分较高。重要的是,大多数阶层之间的差异可以用加性主效应来解释,没有阶层经历统计学上显著的剩余“交互”效应。我们将这些发现与以前关于抑郁症的交叉研究进行了比较,并讨论了 MAIHDA 和传统交叉模型之间可能存在的差异来源。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验