种族和民族群体的知识架构定义在学习中的健康系统
Matthew F Hudson1, Virginia M S van Staden1, Alicia M Oostdyk1
1Prisma Health, Cancer Institute Greenville South Carolina USA.
准确的种族和民族数据对于健康公平至关重要. 这项研究提出了一个学习健康系统 (LHS) 的模型,以改善种族数据的定义和捕获方式,增强健康公平评估.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 健康的社会决定因素
- 卫生公平研究 卫生公平研究
背景情况:
- 对于多个种族和种族群体而言,持续的医疗保健准入和结果差异存在.
- 种族和种族的定义是社会构造的,并且在不同环境中存在差异,使健康结果的测量变得复杂.
- 自我报告和电子健康记录种族/种族数据之间的非最佳协议突出了数据捕获改进的机会.
研究的目的:
- 为参与个人和家庭在学习健康系统 (LHSs) 中定义种族数据提供理由,模型和策略.
- 为应对各种种族和种族群体可靠测量医疗保健结果的挑战.
- 提高种族定义的精度,以改善健康公平评估和干预开发.
主要方法:
- 概念讨论和建议的模型参与患者的种族数据定义.
- 专注于改善LHS内部种族数据征集和捕获的策略.
- 强调测试拟议的理论和步骤,以获得全面和精确的种族定义.
主要成果:
- 通过患者参与来定义种族数据的拟议模型和实际策略.
- 确定LHS作为解决种族数据挑战的合适框架.
- 在种族定义中提高精度的理由,以告知公平评价.
结论:
- 让个人和家庭参与定义种族数据对于健康公平评估至关重要.
- 提高种族定义的准确性可以导致更有效的公平评估和干预LHSs.
- 鼓励进行进一步的测试,以验证竞赛数据定义的拟议方法.
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