Author ORCID Identifier

https://orcid.org/0000-0002-6121-485X

Date of Award

5-13-2022

Degree Type

Thesis

Degree Name

Master of Public Health (MPH)

Department

Public Health

First Advisor

Dr. Ike Okosun

Second Advisor

Dr. Barbara Yankey

Abstract

This study describes implicit bias among healthcare workers in the United States (US) before and during the COVID-19 pandemic (2018 and 2020 respectively), and the rate of implicit bias in the US from 2006 to 2020. Using the Implicit Association Test (IAT), which is a component of the publicly available data from Project Implicit, linear regression analysis between demographic data and mean IAT score, and trend analysis were performed. All statistical analyses were done using SAS 9.4, and the trend analysis graph was created through Microsoft Excel. Our analysis shows that healthcare professionals’ race, gender, and political spectrum were the most consistent predictors of implicit bias. Healthcare providers’ implicit bias decreased from pre- to during COVID-19. Furthermore, healthcare workers’ implicit bias decreased from 2013 to 2016 but saw an increase in 2017. We conclude that the COVID-19 pandemic had no significant impact on the implicit bias of healthcare providers, but there was a significant relationship between implicit bias and healthcare workers’ race, gender, and political affiliation which may impact the health outcome of their patients.

DOI

https://doi.org/10.57709/28909864

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