IMPLICIT–EXPLICIT DISCREPANCIES
What is most unexplainable to researchers surrounding the subject of biases is the discrepancies between implicit versus explicit, what is realized and what is not (Greenwald & Lai, 2020). Most bias testing is done using the IAT. An example of a widely occurring discrepancy is the White versus Black preference. In the U.S., around 30% of Black individuals showed an extreme preference for "like us" through explicit bias testing measures; however, on the IAT, the implicit preference was for White, the "not like us." Another discrepancy is found in the measure of self-esteem, with implicit measures being high while explicit measures rated low (Jordan et al., 2003; Schroder-Abe et al., 2007). The discrepancy seen in the measurement of self-esteem implicitly versus explicitly correlates to a diagnosis of depression and is a pattern commonly documented in Japanese adults (Smeijers et al., 2017). Evidence-Based Consideration Affective cognition is a newer area of study within cognitive science that focuses on how we reason about others' minds and intuitively understand what someone is feeling, thinking, and believing. In other words, it looks at how emotions affect how we think, learn, and reason within our environment. Affective cognition connects what we accept as our identity back to stereotypical perspectives and opinions of those close to us. What we interpret others as feeling can unconsciously affect how we interact with them (Wu et al., 2021). Self-esteem is also linked to being originated from those we interact with most within our environment; thus, it is linked to cultural stereotyping. Cultural stereotyping causes
an emotional belief that affects how the person cognitively processes the situation, which is reflected in how they behave. This is known as affective cognition. An example is the belief that men are better at math than females are. This belief's explicit form correlates to higher anxiety levels in females in the context of their ability to perform well in mathematics. But even implicitly, it causes a female to behave in ways that reveal lowered self-esteem in areas where math is needed (Greenwald & Lai, 2020). Poling Question: Do you think the affective cognitions of math stereotyping hinder young girls and women from pursuing career aspirations that require a strong proficiency in mathematics?
● Yes ● No Video: Dangers of Implicit Bias
IMPLICIT BIAS IN HEALTHCARE
The U.S. is becoming more diverse, and the percentage of people identifying with two or more ethnicities rose from 2.9% to 10.2% between 2010 and 2020 (Agency for Healthcare Research and Quality [AHRQ], 2022). Many states have mandated implicit bias training that focuses on reducing barriers and disparities in the access to and delivery of healthcare services. See Box 1 for an example of state-mandated training. Box 1 Michigan R.338.7004 “Beginning June 1, 2022, and for every renewal cycle thereafter, in addition to completing any continuing education required for renewal, reregistration, or relicensure, an applicant for license or registration renewal, reregistration, or relicensure under article 15 of the code MCL 333.16101 to 333.18838, except those licensed under part 188 of the code, MCL 333.18801 to 333.18838 shall have completed a minimum of 1 hour of implicit bias training for each year of the applicant's license or registration cycle.” Note: From https://ars.apps.lara.state.mi.us/AdminCode/ DownloadAdminCodeFile?FileName=R%20338.7001%20to%20 R%20338.7005.pdf&ReturnHTML=True While research studies on implicit and explicit biases date back to the 1990s, a renewed interest in the role of systemic biases began through the noticeable inequalities highlighted during the Coronavirus Disease 2019 (COVID-19) pandemic (Gleicher et al., 2022). Data released in 2022 by the AHRQ showed that racial and ethnic minority communities have outcomes comparable to those of White communities for just under half of the quality-of-care measures. While this may be true for most communities
nationwide, there are still communities where disparities are much greater between Whites and racial/ethnic minorities. A specific example are the American Indian and Native American communities who had better outcomes on 12% of the quality measures. In comparison, White communities had better outcomes on 43% of the quality measures. Healthcare disparities are also noted for specific disease processes, such as breast cancer, where Hispanics and Blacks have been identified as consistently not receiving high-quality care in treating breast cancer (AHRQ, 2022). See the Highlighting the Agency of Healthcare Research and Quality box for more information on the quality measures tracked. Highlighting the Agency of Healthcare Research and Quality AHRQ releases the National Healthcare Quality and Disparities Report annually, and the information released is from the year prior to the publication year. For example, the 2022 report presents data gathered for 2021. The comprehensive data in the report provide yearly updates on social determinants of health, including the overall quality of healthcare and healthcare disparities. The report presents measurable trends for access to care, affordable care, care coordination, effective treatment, healthy living, patient safety, and person-centered care. The organization’s mission is to “produce evidence to make healthcare safer, higher quality, more accessible, equitable, and affordable, and to work within the U.S. Department of Health and Human Services and with other partners to make sure that the evidence is understood and used.” You can find more information and the most current data at https://www.ahrq.gov/
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