PDF(Pubmed)
Abstract：
As federal strategic plans prioritize increasing diversity within the biomedical workforce, and STEM training and outreach programs seek to recruit and retain students from historically underrepresented populations, there is a need for interrogation of traditional demographic descriptors and careful consideration of best practices for obtaining demographic data. To accelerate this work, equity-focused researchers and leaders from STEM programs convened to examine approaches for measuring demographic variables. Gender, race/ethnicity, disability, and disadvantaged background were prioritized given their focus by federal funding agencies. Categories of sex minority, sexual (orientation) minority, and gender minority (SSGM) should be included in demographic measures collected by STEM programs, consistent with recommendations from White House Executive Orders and federal reports. Our manuscript offers operationalized phrasing for demographic questions and recommendations for use across student-serving programs. Inclusive demographics permit the identification of individuals who are being excluded, marginalized, or improperly aggregated, increasing capacity to address inequities in biomedical research training. As trainees do not enter training programs with equal access, accommodations, or preparation, inclusive demographic measures can welcome trainees and inform a nuanced set of program outcomes that facilitate research on intersectionality to support the recruitment and retention of underrepresented students in biomedical research.
摘要：
随着联邦战略计划优先考虑增加生物医学劳动力的多样性，和STEM培训和外展计划寻求从历史上代表性不足的人群中招募和留住学生，有必要询问传统的人口统计学描述符，并仔细考虑获取人口统计学数据的最佳做法。为了加快这项工作，以公平为重点的研究人员和STEM计划的领导者召集起来研究测量人口变量的方法。性别,种族/民族,残疾,鉴于联邦资助机构的关注重点，处境不利的背景被优先考虑。少数性别类别,性（倾向）少数，和性别少数群体（SSGM）应包括在STEM计划收集的人口统计数据中，与白宫行政命令和联邦报告的建议一致。我们的手稿为人口统计问题提供了可操作的措辞，并建议在学生服务计划中使用。包容性人口统计数据允许识别被排除在外的个人，边缘化，或者聚合不当，提高解决生物医学研究培训不平等问题的能力。由于学员不能平等地进入培训项目，住宿,或准备，包容性人口措施可以欢迎学员，并告知一套细致入微的计划成果，以促进交叉性研究，以支持在生物医学研究中招募和保留代表性不足的学生。
