研究慢性疾病和社会边缘化的人群支持网络的混合方法
Tessa M Nápoles1, Emily A Ekl2, Jeff Nicklas1
1Department of Social and Behavioral Sciences, University of California, San Francisco, San Francisco CA, USA.
Qualitative health research
|March 21, 2024
概括
这项研究引入了混合社交网络方法,以识别经常被忽视的支持系统,如"无名关系",用于面临健康挑战和社会劣势的边缘化个体.
科学领域:
- 社会学 社会学 社会学
- 社交网络分析 社交网络分析
- 健康差距 研究 研究 研究 研究
背景情况:
- 传统的社交网络分析往往忽略了非传统的支持系统.
- 现有的关于社会关系 (强/弱) 的理论正在扩大,包括细微的类别.
- 边缘化的人群经常依靠不那么明显的支持网络.
研究的目的:
- 展示混合社交网络方法在识别被忽视的支持方面的实用性.
- 分析边缘化社区内"无名关系"的形成和功能.
- 提出研究临界支持网络的新方法.
主要方法:
- 采用定量自我中心的网络分析与深入的采访和观察相结合.
- 利用一种新的工具来捕获社会群体网络数据.
- 专注于 专注于 在
主要成果:
- 混合方法成功地确定了"无名关系" (非亲属,非提供者,在上下文中分组的个人).
- 这些"无名关系"提供了至关重要的,但往往未被认可的社会支持.
- 传统的方法本身无法捕捉到这些重要的支持关系.
结论:
- 混合社交网络方法对于了解边缘化群体的支持系统至关重要.
- 拟议的组变更方法可以揭示极限支持关系.
- 这项研究为研究慢性残疾,贫困和住房不安全等背景下的社会支持提供了一个框架.
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