两位研究人员分享了他们的跨学科合作如何推动工作以引领数据科学的未来。
Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science.
作者信息
Delano Maggie, Albert Kendra
机构信息
Swarthmore College, Swarthmore, PA, USA.
Harvard Law School, Cambridge, MA, USA.
出版信息
Patterns (N Y). 2022 Aug 12;3(8):100573. doi: 10.1016/j.patter.2022.100573.
Abstract
In their recent perspective published in , Maggie Delano and Kendra Albert highlight the limitations of sex and gender data classification in health systems and show how this contributes to the marginalization of trans and non-binary individuals. They provide recommendations to improve incorporating gender data into healthcare algorithms. Here they discuss their collaboration and how it enabled this cross-disciplinary research.
摘要
在玛吉·德拉诺和肯德拉·阿尔伯特最近发表在《》上的观点文章中，他们强调了卫生系统中性别数据分类的局限性，并展示了这是如何导致跨性别者和非二元性别者被边缘化的。他们提出了将性别数据纳入医疗算法的改进建议。在此，他们讨论了他们的合作以及这种合作如何促成了这项跨学科研究。
Zotero 插件