It appears that similar behavior is judged differently when occurring by an African American versus a White student. Blake and colleagues went a step further and examined race, complexion, and suspension rates. They found that Case study exercise 1 You’ve just come from a meeting with a group of African American and Latinx community residents. They presented the committee, which you are a part of, with a list of demands related to what they perceive as a racist hospital environment. Your first patient is a 24-year-old African American male, dressed in a t-shirt and jeans. You greet him and before you can ask any questions, he asks you a few questions. “Where did you grow up?” “Did you have any Black friends?” “Why are you looking at your watch?” “Is this going to be more than a 10-minute visit?” Question 1 Why might the community members perceive a hospital or healthcare system as being racist? Commentary on question 1 In addition to the history and present state of a particular hospital or healthcare system, the history of racism in America in general, as well as continuing racial health disparities, may contribute to some African Americans and Latinx community residents perceiving a hospital as being racist. Question 2 Why might some African American patients question White providers about their background and experience in working The history of race relations in America has contributed to many divisions. White providers may not have many close friends who are African American or spend significant amounts of time in predominately African American communities. Stereotypes about casually dressed young African American men may operate for some providers. As mentioned earlier, implicit bias operates not only for race, given the historical context of race in America, but with African American patients? Commentary on question 2
African American teenage girls with darker complexions were suspended at a higher rate than those with lighter complexions (Blake et al., 2017). Again, unconscious bias seems to be a major factor. gender, sexual orientation, height, weight, and even accent can unconsciously influence attitudes and decisions. In one experiment, subjects listened to two separate English speakers reading the same script. When they saw a photograph of an Asian person as the speaker, they rated the accent as being stronger than when the speaker was paired with a photo of a White person. They also rated the understanding of the content as being more difficult to understand when they saw the face of an Asian person. The assessment of the speaker, prompted by the photograph of an Asian individual, appeared to be influenced by unconscious bias (Zheng & Samuel, 2017). Before the Covid-19 pandemic, it was more common to have online courses with Power Point slides and videos, without seeing the actual instructor. MacNell constructed a research design where a male and a female instructor each led two sections of a discussion group. During one section they both used a male name; during the other section they both used a female name. Students couldn’t see the face of the instructor or hear their voice. They tried to teach all four sections similarly. At the end of the semester, the students in all four discussion groups were asked to rate the instructors on 12 different traits, covering characteristics related to their effectiveness and interpersonal skills. The male-named instructors were rated highest on all characteristics, regardless of whether the instructors were actually male or female. Class work was graded and returned to students at the same time in all four sections. Students who thought that they were being taught by a male instructor gave a promptness rating of 4.35 out of 5. Student gave the female-named instructors a rating of 3.55 (MacNell et al., 2014; Mitchell & Martin, 2018). Again, this points to the powerful influence of unconscious bias.
HEALTHCARE RESEARCH
What does this have to do with healthcare? In addition to a provider’s conscious adherence to high ethical standards and a commitment to quality care, they are also subject to implicit bias, like the rest of the population. Fitzgerald and Hurst examined 42 peer-reviewed articles(FitzGerald & Hurst, 2017). The evidence indicated that healthcare professionals exhibit the same level of implicit bias as the wider population. A couple decades earlier, Shulman and his colleagues published research that many view as a major stimulus for further research regarding implicit bias and healthcare (Schulman et al., 1999). They presented 720 physicians with videos of patients (actors) who were similar in physical appearance and medical history, differing only by race and sex. All were candidates for cardiac catheterization. After the physicians saw the videos of the patients and reviewed their history, the researchers found that women and African Americans were less likely to be referred for cardiac catheterization than men and Whites. It appeared that, in spite of the conscious commitment to equitable care, unconscious bias was an influence in referral decision making. The national interest in implicit bias in healthcare intensified when the Institute of Medicine delivered its report, Unequal Treatment , in 2003 (Smedly et al., 2003). It concluded that implicit bias against social groups, including racial and ethnic groups, can impact the clinical encounter. Much of the research supporting this report utilized the online Implicit Association Test (IAT). The IAT measures the strength of associations between concepts such as African American or White, old or young, good or bad, desirable or undesirable, and dangerous or friendly.
The reaction time (association) to various pairs of words or photographs is a measure of the strength of the association. Millions of people used this website (operated by Harvard University) to take the IAT or one of the other tests. The racial disparity in the judgement of pain has been studied as an example of implicit bias in healthcare. In research by Mende- Siedlecki and colleagues, White providers demonstrated more stringent thresholds in perceiving pain on African American faces versus White faces, and those with more stringent thresholds for African American patients prescribed fewer non-narcotic pain relievers (Mende-Siedlecki et al., 2019). This was not true for Asian faces, suggesting that other-face dynamics were not at play. This research did not investigate whether gaps in empathy or perspective taking skills might be a partial explanation for the disparity. Implicit bias has also been shown to impact the quality of the clinical encounter, particularly communication. In an early study, primary care physicians took the IAT and had their clinical encounters recorded (Cooper et al., 2012). Provider race bias on the IAT was associated with lower quality communication with African American patients, such as more provider verbal dominance, lower patient positive affect, poorer patient ratings of interpersonal care,lower perceptions of respect from clinicians, and lower likelihood of recommending the clinician.
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