Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 91 - High Dimensional Data, Causal Inference, Biostats Education, and More
Type: Contributed
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Teaching of Statistics in the Health Sciences
Abstract #318127
Title: Updating an Introductory Biostatistics Course to Support Diversity, Equity, Inclusion, and Transparency
Author(s): Priya Srikanth* and Amber Lin
Companies: Oregon Health & Science University and Oregon Health & Science University
Keywords: Diversity; Equity; Inclusion; Teaching; Transparency; Biostatistics
Abstract:

As educators of an introductory biostatistics course, our goal is to help students learn and develop a lasting interest in statistics. With this goal, we implemented changes to our course material and structure to better support diversity, equity, inclusion, and transparency. This was a multi-pronged effort where we modified course material to address topics such as structural racism, gender binarism, heterosexism, and sexism; introduced and expanded use of free course materials including statistical software; extended flexibility in the course schedule; and built personal connections between students, TAs, and faculty. Most of these interventions required a one-time investment while others required ongoing engagement. Our goal for this submission is to introduce educators to a variety of ideas on supporting diversity, equity, inclusion, and research transparency and to better engage and connect with students. Next steps would include using data examples from underreprested and minority populations, creating awareness of racism in data, interpreting results highlighting disparities in a sensitive manner, and exploring options for providing free access to statistical software.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program