使用隐性个人资料分析将美国各州分类为结构性种族主义的类型
Stephanie Veazie1, Elleni M Hailu2, Corinne A Riddell3
1University of California - Berkeley, School of Public Health, Division of Epidemiology, United States.
Social science & medicine (1982)
|January 18, 2025
概括
结构性种族主义推动了健康差异. 这项研究使用潜质分析将美国各州分为基于教育,就业,房屋所有权,监禁和收入方面的种族不平等的三个类型.
科学领域:
- 公共卫生 公共卫生
- 社会学 社会学 社会学
- 健康 公平 研究 健康 公平 研究
背景情况:
- 结构性种族主义是种族健康不平等的主要驱动因素.
- 由于结构性种族主义的复杂性和多维性质,量化结构性种族主义具有挑战性.
- 了解结构性种族主义的州级变化对于有针对性的干预至关重要.
研究的目的:
- 将美国各州分为结构性种族主义的类型.
- 在五个关键领域分析种族不平等的模式.
- 为此目的评估隐形形状分析的实用性.
主要方法:
- 隐性概况分析 (LPA) 用于对50个美国州进行分类.
- 数据包括教育程度,就业,房屋所有权,监禁和收入方面的黑人和白人不平等.
- 分析探讨了两个,四个和五个类型学,专注于三类型学模型.
主要成果:
- 在三种类型模型中,27个州形成了类型学1,16个州形成了类型学2,7个州形成了类型学3.
- 类型3州 (通常是中西部) 在大多数领域 (除了教育) 显示了最高的不平等.
- 类型2国家 (西部/东部) 呈现出最大的教育不平等.
结论:
- 潜在的个人资料分析有效地识别了结构性种族主义的不同州级类型.
- 国家分组在不同数量的类型学中保持一致.
- 这种方法有助于研究人员识别具有类似结构性种族主义特征的国家进行进一步研究.
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