Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science.
Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science.
复制标题
DOI：
10.3390/ijerph20010279
复制
发表时间：
2022-12-24
影响因子：
--
通讯作者：
Idris, Muhammed Y.
中科院分区：
文献类型：
--
作者：
Awad, Christopher S.;Deng, Youping;Kwagyan, John;Roche-Lima, Abiel;Tchounwou, Paul B.;Wang, Qingguo;Idris, Muhammed Y.
尽管受到健康差距的不成比例的影响，黑人，西班牙裔，土著和其他代表性不足的人口占生物医学数据科学相关学科毕业生的极少数。鉴于他们致力于教育代表性不足的学生和学员，少数民族服务机构（MSI）可以在提高生物医学数据科学劳动力的多样性方面发挥重要作用。关于提供这些数据科学培训计划的覆盖范围，课程广度和最佳实践的报道很少。本文的目的是总结六个研究中心在少数民族机构（RCMIs）授予资金从少数民族健康差异（NIMHD）的国家研究所开发新的数据科学培训计划。进行了一项横断面调查，以更好地了解学生的人口统计服务，课程主题涵盖，教学和评估，挑战的方法，并由程序主任的建议。课程在覆盖范围和课程多样性方面取得了整体成功，为广泛的学生和教师提供服务，同时也涵盖了广泛的主题。强调的主要挑战是缺乏资源和基础设施，以及教授经验和知识水平各不相同的学习者。需要对MSI进行进一步投资，以维持培训工作，并为生物医学数据科学劳动力的多样化开发途径。
Despite being disproportionately impacted by health disparities, Black, Hispanic, Indigenous, and other underrepresented populations account for a significant minority of graduates in biomedical data science-related disciplines. Given their commitment to educating underrepresented students and trainees, minority serving institutions (MSIs) can play a significant role in enhancing diversity in the biomedical data science workforce. Little has been published about the reach, curricular breadth, and best practices for delivering these data science training programs. The purpose of this paper is to summarize six Research Centers in Minority Institutions (RCMIs) awarded funding from the National Institute of Minority Health Disparities (NIMHD) to develop new data science training programs. A cross-sectional survey was conducted to better understand the demographics of learners served, curricular topics covered, methods of instruction and assessment, challenges, and recommendations by program directors. Programs demonstrated overall success in reach and curricular diversity, serving a broad range of students and faculty, while also covering a broad range of topics. The main challenges highlighted were a lack of resources and infrastructure and teaching learners with varying levels of experience and knowledge. Further investments in MSIs are needed to sustain training efforts and develop pathways for diversifying the biomedical data science workforce.
登录
查看更多内容
DOI：
10.1093/jamia/ocaa206
发表时间：
2020-11-01
影响因子：
6.4
作者：
Wiley, Kevin;Dixon, Brian E.;Menachemi, Nir
通讯作者：
Menachemi, Nir
影响因子：
3.2
作者：
Canner, Judith E.;McEligot, Archana J.;Zhang, Xinzhi
通讯作者：
Zhang, Xinzhi
DOI：
10.3390/ijerph18041569
发表时间：
2021-02-07
影响因子：
--
作者：
Yanagihara R;Berry MJ;Carson MJ;Chang SP;Corliss H;Cox MB;Haddad G;Hohmann C;Kelley ST;Lee ESY;Link BG;Noel RJ Jr;Pickrel J;Porter JT;Quirk GJ;Samuel T;Stiles JK;Sy AU;Taira DA;Trepka MJ;Villalta F;Wiese TE
通讯作者：
Wiese TE
DOI：
10.3390/ijerph17228373
发表时间：
2020-11-12
影响因子：
--
作者：
Sy A;Hayes T;Laurila K;Noboa C;Langwerden RJ;Hospital MM;Andújar-Pérez DA;Stevenson L;Cunningham SMR;Rollins L;Madanat H;Penn T;Mehravaran S
通讯作者：
Mehravaran S
影响因子：
16.6
作者：
通讯作者：
--