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衡量STEM和生物医学研究培训项目中代表性不足人群人口统计学特征的包容性方法。

Inclusive Approaches for Measuring Demographics of Underrepresented Populations in STEM and Biomedical Research Training Programs.

作者信息

Paris S E, Dinno A, Marr M C, Raz Link A, Lentz B L, Setthavongsack A, Espinosa S N, Shusterman G, Abel J, Harrison K, Hook J, Alvord T W, Richardson D M, Chase K, Marriott L K

机构信息

Oregon Health and Science University, Portland, OR.

Portland State University, Portland, OR.

出版信息

J STEM Outreach. 2024 Feb;7(2). doi: 10.15695/jstem/v7i2.10. Epub 2024 Feb 1.

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

随着联邦战略计划将增加生物医学劳动力的多样性作为优先事项,并且科学、技术、工程和数学(STEM)培训及推广项目试图招募和留住历史上代表性不足群体的学生,有必要审视传统的人口统计学描述符,并认真考虑获取人口统计数据的最佳做法。为了加快这项工作,来自STEM项目的关注公平性的研究人员和领导者齐聚一堂,研究测量人口统计变量的方法。鉴于联邦资助机构对性别、种族/族裔、残疾和弱势背景的关注,这些方面被列为优先事项。根据白宫行政命令和联邦报告的建议,性少数群体、性(取向)少数群体和性别少数群体(SSGM)类别应纳入STEM项目收集的人口统计指标中。我们的手稿提供了人口统计问题的可操作表述以及在学生服务项目中使用的建议。包容性的人口统计数据能够识别被排除、边缘化或不当汇总的个体,增强解决生物医学研究培训中不平等问题的能力。由于学员进入培训项目时所获得的机会、便利条件或准备并不均等,包容性的人口统计指标能够欢迎学员,并为一系列细致入微的项目成果提供信息,从而有助于开展交叉性研究,以支持在生物医学研究中招募和留住代表性不足的学生。

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