Scientific Racism Attitudes Among Diverse Undergraduate Pre

Abstract

Scientific racism, or the belief that racial hierarchies are explained by biological differences, among health care professionals may contribute to the oversight of social causes of health problems and result in poor quality of care. This study examines the extent to which scientific racism may permeate undergraduate pre–health professions student worldviews before entering advanced training. Undergraduate students (n = 251) pursuing training in health professions from The University of Texas at San Antonio completed a survey in 2018 measuring respondent level of agreement with statements about biological differences between U.S. racial and ethnic groups, as well as agreement with statements about cognitive differences, health behaviors, and systemic racism. Analyses showed that the students agreed with false statements regarding biological, behavioral, and cognitive differences among races and agreement was significantly stronger among students at earlier stages of education but did not differ by student race/ethnicity. Adjusted analyses showed that third- and fourth-year undergraduate students exhibited less agreement with false statements about race than first- and second-year students ( $\hat{β} = - 10.86,$ 95% CI [−18.33, −3.39], p < .00). The joint effect of higher class level and public health major ( $\hat{β} = - 6.57,$ 95% CI [−11.98, −1.16], p = .02) was also significantly associated with less agreement with false statements regarding systemic racism. This exploratory study suggests that even among diverse student populations, scientific racism persists but may improve over educational careers. Future research may determine how structural reforms and training can further improve student understandings of race as a social construct and not a biological reality.

Keywords

race social determinants of health undergraduate education health equity

“Scientific racism” describes pseudoscientific beliefs that human population differences in behavior, cognitive abilities, socioeconomic status, and physical attributes are the consequences of inflexible, biological differences between racial groups (A. Smedley & Smedley, 2005). Contrary to this view of race, the vast majority of experts agree that race is a social construction with no objective reality but is defined by collectives according to their interpretations of history and culture (Krieger, 2005; Robillard et al., 2015). The completion of the Human Genome Project further supports the view of race as a social category by producing scientific evidence that race is not biologically determined (Yudell et al., 2016), showing that about 85% of the variation in gene frequencies occurs within populations of the same race while only 15% of variation occurs between such groups (Freeman, 1998). The malleability of racial categories over time further solidifies the absence of biological underpinnings of race, such as the creation of the “mulatto” category to describe people of mixed Black and White ancestry in the United States. This racial category was created in part to prove that an individual was not entirely able to be categorized as Black and, as a result, could avoid being forced into slavery (Reece, 2018). Despite the academic consensus around the social origins of race, forms of scientific racism persist among the general public and at times even in scientific communities (Mccann-Mortimer et al., 2004). Studies of racism are numerous in health care research (Blair et al., 2013; Puumala et al., 2016), but few specifically tease out scientific racism as a specific type of racism with which providers may consciously or unconsciously agree. Some studies of conscious and unconscious racism show racial and ethnic differences in these measures among both providers and trainees of different backgrounds (Sabin et al., 2009; White-Means et al., 2009). Some research shows that implicit bias favoring non-Hispanic White over Black populations is higher among non-Hispanic White respondents and that these biases are associated with both higher explicit/conscious racial biases (Sabin et al., 2009) and lower cultural competency (White-Means et al., 2009). Attributing race to biological factors is especially concerning among health professionals because it may contribute to oversight of the social causes of health problems (Fincher et al., 2004), incorrect diagnoses and undertreatment of patients of color due to false biological beliefs (Trawalter et al., 2012), and racial discrimination toward patients and colleagues alike (King-Okoye et al., 2019).

Previous research from 2016 on White U.S. medical students found that a considerable number of study participants held false beliefs about biological differences between Black and White people, such as the perception that Black people feel less pain than White people, and these beliefs were associated with biased treatment recommendations among study participants (Hoffman et al., 2016). These beliefs were more common among the less advanced students. Such attitudes indicate that colleges and universities may not adequately incorporate rigorous social science perspectives on race into courses taken by many medical students and that standard training may only marginally reduce scientific racism. In fact, a large body of research finds that race is often poorly understood and used in clinical settings by medical students and physicians, resulting in ignorance of the systemic issues that produce racial inequities (Tsai et al., 2016).

Yet promising approaches, such as the structural competency model, exist for improving understandings of race in medical training. Research shows that this is an effective model for addressing stigma and inequities in health care settings by combining clinical education with recognition of how social and economic factors influence inequitable health outcomes among racial/ethnic groups (Metzl et al., 2018). Social understandings of race are commonly addressed in public health, sociology, and other social science disciplines. Structural competency incorporates these perspectives into medical training, thus undermining biological understandings of racial differences.

Although previous research illuminates forms of scientific racism among non-Hispanic White medical students (Hoffman et al., 2016) and also explores how race is inadequately addressed in clinical education (Tsai et al., 2016), the presence of scientific racism in the attitudes of racially and ethnically diverse pre-health professions students at the undergraduate level remains unclear. To elucidate whether scientific racism also affects the attitudes of both White students and students of color with intentions of future health professional training, this exploratory study examines undergraduate student beliefs regarding the connections between biology and race, as well as their knowledge of the social determinants of racial differences in health as described in more nuanced approaches such as the structural competency model.

This study proposes three hypotheses that build on previous research showing decreased racism among upper-level medical students compared with lower-level medical students (Hoffman et al., 2016), structural competency research showing that social science training improves understandings of race by highlighting the social basis of racial variation in health (Metzl et al., 2018), and studies indicating racial differences in conscious and unconscious bias, specifically that non-Hispanic White providers and trainees express a stronger preference for non-Hispanic White patients than for other groups (Sabin et al., 2009). The hypotheses are as follows:

Hypothesis 1: Upper-level students (third and fourth year) are more likely to have lower racism scores than lower-level students (first and second year).

Hypothesis 2: Public health majors, whose educational program includes required social science courses and courses on the social determinants of health, are more likely to have lower racism scores than non–public health majors.

Hypothesis 3: Non-Hispanic White students are more likely to have higher racism scores than students of color.

Brief History of Scientific Racism

The concept of race began around the 15th century, when European exploration and colonization led to increased contact with people from distant regions of the world (A. Smedley & Smedley, 2005). Europeans reacted to the phenotypic differences they observed by establishing “race” categories that assigned meanings to cultural, linguistic, and physical differences. The categories the Europeans made aligned with their own understanding of the world and justified actions like natural resource extraction in distant lands and enslavement of indigenous populations (Dennis, 1995).

The social origins of racial categories eventually became obscured by scientific efforts to explain phenotypic variation as stemming from biogenetic differences. These efforts contributed to the removal of cultural and language elements from definitions of racial categories and birthed a large-scale effort among European scientists to find a biological basis for race (A. Smedley & Smedley, 2005). Now labeled as “scientific racism,” these efforts combine two lines of scientific thought: heredity and the believed supremacy of northern European groups (Jackson & Weidman, 2006).

Scientific racism seeks to show that permanent inequality is natural and uses the biophysical differences among humans to justify this worldview. Many groups have utilized scientific racism for political and economic gain, resulting in forms of violence that persist today (A. Smedley & Smedley, 2005). Such false scientific ideology in the United States was used to justify the genocide and exploitation of multiple groups, including the enslavement of Africans and the systematic, violent removal of Native Americans from their lands. The treatment of each group was tied to their placement in a racial hierarchy in which White European Americans maintained dominance (Fairchild, 1991). U.S. physicians such as Charles Caldwell used phrenology, or the study of skull shape as a measure of cognitive abilities, to justify the enslavement of Africans in the 1830s and 1840s. Caldwell studied the skull shapes of people from different geographic origins and claimed that African skulls indicated “tamable-ness,” which warranted their role as slaves (Horsman, 1975). Harriet A. Washington’s (2006) book Medical Apartheid provides a sweeping history of racist medical practices in the United States, such as tracing the ways modern gynecological practice was built on medical experimentation on Black slave women. To find a cure for vaginal fistulas, James Marion Sims, considered the “father of gynecology,” performed painful vaginal surgeries on Black slave women without anesthesia, claiming that Black people did not feel pain as acutely as White people (Washington, 2006). To justify the removal of Native Americans from their lands, physiologists including Samuel Morton studied skull configurations and mental capabilities, concluding that Native Americans opposed cultivation and were slow in acquiring knowledge (Horsman, 1975). In the 19th century, biology was invoked to justify the displacement of indigenous people, including Brazilian Black and Mestiço populations, from their native lands (Arteaga, 2017). The “mulatto hypothesis,” or the presumption that persons of mixed race inherit the worst characteristics from both parent racial groups, has been employed against African Americans, Brazilians, and other Latin Americans (Hudson, 1995). These examples illustrate the breadth of scientific racism and how it permeates the histories of multiple racial/ethnic groups.

Modern cases of scientific racism also exist. For example, The Bell Curve, a book written by psychologists Richard Herrnstein and Charles Murray in 1994, argues that economically disadvantaged Black people are innately less intelligent than White and Asian people (Herrnstein & Murray, 1994). This book has been used by political groups as well as researchers. In addition, the Canadian psychologist J. Philippe Rushton not only posited that Black people are less intelligent than White Europeans but also reinforced racist tropes about Asians, including positive correlations between brain size and cognitive performance. His work linking race and intelligence was published in scientific journals, with only some journals retracting his publications for their racist foundations (Cernovsky, 1990, 1991). Modern inequities in health care may also have roots in this ideology of race. In a 2018 study measuring racial bias in perception of pain, college sports medical staff assumed that Black student athletes feel less pain than White student athletes, even when suffering the same injury (Druckman et al., 2018). While Western scientific consensus no longer supports the ideology of scientific racism, recent examples exemplify the insidious, persistent nature of this worldview. Ongoing research is needed to determine the extent to which health professionals and students harbor such false racial beliefs and to identify opportunities for intervention. The purpose of this research is to assess the extent to which attitudes aligned with scientific racism are present among undergraduate pre-health professions students.

Method

Survey Design

To determine if diverse undergraduate pre-health professions students affirm false racial beliefs, the authors developed an online survey using Qualtrics software (Qualtrics, 2018, Provo, UT, USA) that contained 62 questions. Likert-type scales were used to indicate respondent level of agreement with true and false statements about the relationships between race/ethnicity and health. Level of agreement was indicated using a 6-point scale ranging from definitely untrue to definitely true. The authors created a framework of four different forms of racism to be included in the survey based on previous research on racism among health professionals and medical students: biophysical, cognitive/intelligence, health behavior, and systemic. The survey questions were derived from both a survey of scientific racism developed by Hoffman et al. (2016) and a literature review. Biophysical racism questions examined attitudes about biological differences between races/ethnicities (23 questions), and cognitive/intelligence racism questions included beliefs that races/ethnicities differ in intellectual abilities (5 questions); these two categories measured scientific racism most directly. Additional question categories addressing health and race, included health behavior questions that asked about how races/ethnicities differ in their behavioral risk and protective factors (9 questions), and systemic differences, which inquired about how social trends and structures affect races/ethnicities differently (12 questions). All survey questions and supporting sources are included in the Supplemental Table B.

Each survey statement about the relationship between race/ethnicity and health also included a particular racial or ethnic group. For example, a statement assessing agreement with biophysical racism in the survey was phrased as “Blacks/African Americans are more fertile than non-Hispanic Whites.” This survey design allowed assessment of interracial and interethnic variation within the four forms of racism. The survey included statements about anti-Black/African American racism (16 questions), anti-Asian racism (11 questions), anti-Hispanic/Latinx racism (14 questions), and non-Hispanic White superiority (9 questions).

The survey also included demographic questions (11 questions), such as expected health profession and race and/or ethnicity. A reliability analysis was carried out on the survey, comprising the 51 racial attitude items. Cronbach’s alpha showed the overall survey to reach excellent reliability (α = .94). Cronbach’s alpha for the subscales also showed good reliability (α = .93, biophysical racism; α = .87, cognitive/intelligence racism; α = .81, health behavior racism; and α = .86, systemic racism).

Data Collection

Undergraduate pre–health profession students at a large U.S. Hispanic-serving university were recruited for participation in the online survey during 2018. All students enrolled at the university in the spring and fall 2018 semesters who were pursuing a pre–health profession degree, including pre-medical, pre-dental, pre–dental hygiene, pre-nursing, pre–occupational therapy, pre-optometry, pre-pharmacy, pre–physical therapy, pre–physician assistant, and pre–respiratory care, were invited to participate. Participants were recruited by the first author, who promoted the study in person at science and public health classes and via emailed newsletters from college programs. Survey respondents had the option to remain anonymous or to enter their email address into a drawing for $30 cash cards. Email addresses were kept confidential and not linked to survey responses. The University of Texas at San Antonio institutional review board approved this study.

Measures

The dependent variables included domains of quantified and combined scoring for questions related to the four different forms of racism previously discussed and to racism toward the four racial/ethnic groups also previously listed, and an overall “false racial idea score,” compiling a total score for all survey responses. Question agreement/disagreement was scored using a 6-point scale, where a score of 1 indicated that the survey participant agreed strongly with a true statement (or disagreed strongly with a false statement) and a score of 6 indicated that the survey participant disagreed strongly with a true statement (or agreed strongly with a false statement). Lower total scores suggested a more thorough understanding of race as a social category than higher scores.

The primary predictors included demographic and educational factors. These included race/ethnicity, major, and class level. Race/ethnicity was constructed as a three-category variable consisting of non-Hispanic White, Hispanic/Latinx, and other students of color. All other independent variables were constructed as binary predictors, including major (public health major vs. non–public health major) and class level (upper level [third and fourth year] vs. lower level [first and second year]). The analyses also considered binary predictors for sex (male vs. female), health profession (pre-medical vs. other health service profession), and parent education (high-level education [bachelor’s degree and above] vs. low-level education [below bachelor’s degree]) as additional exploratory analyses to determine if sex, socioeconomic status, or intended health profession may also demonstrate associations with racism scores.

Data Analysis

All analyses were conducted using STATA software, Version 15.1 (StataCorp LLC, College Station, TX, USA). Two-way associations between the dependent variables and individual demographic characteristics were conducted using analysis of variance. Given the exploratory nature of the study, these two-way comparisons were used in a data-driven approach for selection of independent variables in generalized linear regression models testing the study hypotheses. In these final adjusted models, variables significant in two-way comparisons were kept, while others were omitted to avoid overfitting and to maintain parsimonious models. An exception was the inclusion of the sex and race/ethnicity variables, which were theoretically driven and sustained across models. Social science research suggests that race/ethnicity both is an important factor in studies of racial attitudes and should be included as context in all studies of racism (Garcia, 2017), and significant associations between sex and racial attitudes have been found in studies of clinicians (Sabin et al., 2009). Post hoc exploratory analyses also examined the interactions between the predictor variables in each regression model. Coefficients and F-statistics were considered significant if their p-value was less than the significance level of 5%. Coefficients for the three-category race variable were considered significant if their p value was less than the Bonferroni-corrected value of .017. Regression model fit was assessed using R².

Results

There were 28,675 students enrolled at the university during the spring 2018 semester and 32,101 during the fall 2018 semester (The University of Texas at San Antonio, n.d.). The university health professions office listed 814 students as pre–health professions majors during these two semesters. A total of 251 survey responses were collected from these 814 students, yielding a response rate of 31%. The survey respondents were 85 (34%) first-year, 42 (17%) second-year, 50 (20%) third-year, and 71 (28%) fourth-year students. Thirty-seven (15%) identified as non-Hispanic White, 133 (53%) as Hispanic/Latinx, and 80 (32%) as other students of color. Other students of color included Asian, Black/African American, Middle Eastern or North African (Egyptian, Iranian, Saudi Arabian, Lebanese, etc.), and Native Hawaiian or other Pacific Islander. Additional demographic characteristics are recorded in Table 1.

Table 1.

Sample Characteristics for Pre–Health Professions Student Survey Respondents (2018).

Characteristic	Responding students (N = 251), n (%)
Expected future health profession
Pre-medical	124 (49.4)
Pursuing other health service professions	127 (50.6)
Major
Public health major	83 (33.1)
Non–public health major	168 (66.9)
Class level
First year	85 (33.9)
Second year	42 (16.7)
Third year	50 (19.9)
Fourth year	71 (28.3)
Not stated	3 (1.2)
Race/ethnicity
Hispanic/Latinx	133 (53.0)
Non-Hispanic White	37 (14.7)
Other students of color	80 (31.9)
Not stated	1 (0.4)
Parent education
High level (≥BA degree)	98 (39.0)
Low level (<BA degree)	153 (61.0)
Sex
Female	196 (78.1)
Male	53 (21.1)
Not stated	2 (0.8)

Two-way comparisons showed that student health profession (pre-medical vs. other health service profession) resulted in significantly different means in the anti-Black/African American and cognitive/intelligence racism domains. Student class level (upper-level students vs. lower-level students) also resulted in significantly different means in the total false racial idea scores domain and the anti-Black/African American, anti-Hispanic/Latinx, racial ideas about non-Hispanic Whites, biophysical racism, and systemic racism domains. Parent’s level of education resulted in significantly different means in the health behavior racism domain. Other two-way comparisons did now show significant differences between groups for any of the racism domains. Descriptive statistics for total false racial idea scores stratified by respondent demographics are provided in Table 2. Results for the other racism subscales are provided in Supplemental Table A.

Table 2.

Total False Racial Idea Scores for Pre-Health Professions Student Survey Respondents (2018).

Characteristic	Average total false racial idea score (±SD)Mdn score (min = 74, max = 198)
Race/ethnicity
Non-Hispanic White n = 37	141.06 (26.02)148
Hispanic/Latino n = 133	140.41 (28.29)144
Other student of color n = 80	140.66 (28.65)142.5
Class level
Upper (third and fourth year) n = 124	134.81 (31.48)* 137
Lower (first and second year) n = 127	146.21 (23.32)* 148
Major
Public health n = 83	142.32 (28.64)146
Non–public health n = 168	140.12 (27.79)143
Sex
Male n = 53	136.43 (27.53)134.5
Female n = 196	142.52 (27.81)146
Health profession
Pre-medical n = 124	138.25 (28.34)140
Other health service profession n = 127	143.19 (27.61)145
Parent education
High level (≥BA degree) n = 98	137.69 (27.27)138
Low level (<BA degree) n = 153	142.83 (28.40)145

F statistic significant at p < .05.

In the adjusted analyses (Tables 3 and 4), class level was the most consistent predictor across different racial attitudes, thus confirming Hypothesis 1. Students in their third or fourth year of higher education better identified false and evidence-based statements about race/ethnicity than students in their first or second year ( $\hat{β} = - 10.86,$ 95% CI [−18.33, −3.39], p < .00), after controlling for student race/ethnicity and sex. Specifically, third- and fourth-year students had lower agreement with false statements about Black/African American ( $\hat{β} = - 4.99,$ 95% CI [−7.26, −2.72], p < .00) and Hispanic/Latinx ( $\hat{β} = - 3.05,$ 95% CI [−5.57, −0.54], p = .02) inferiority, and non-Hispanic White ( $\hat{β} = - 2.15,$ 95% CI [−3.78, −0.52], p < .00) superiority, as well as lesser agreement with statements regarding biophysical racism ( $\hat{β} = - 6.34,$ 95% CI [−11.13, −1.55], p = .01), a domain that measures scientific racism in particular. Class level was also a significant predictor of systemic racism, with upper-level (third and fourth year) students significantly less likely to agree with false statements ( $\hat{β} = - 3.84,$ 95% CI [−6.38, −1.30], p = .00; Table 4, Model 9a). Major was not significant in any models, thus rejecting Hypothesis 2. Moreover, a post hoc analysis examining the interaction between class level and major revealed that third- and fourth-year public health majors in particular had lower agreement with false statements regarding systemic racism than lower-level non–public health majors ( $\hat{β} = - 6.57,$ 95% CI [−11.98, −1.16, p = .02; Table 4, Model 9b), suggesting that there is a joint effect of major and class level that warrants further investigation regarding curriculum content and timing. Respondent race/ethnicity was not significantly associated with racial attitudes, thus rejecting Hypothesis 3.

Table 3.

Generalized Linear Models Showing the Adjusted Relationships Between Racism Domains and Demographic Characteristics of Pre–Health Professions Student Survey Respondents (2018).

Characteristic	Model 1: Overall false racial idea score		Model 2: Anti-Black/African American racism score		Model 3: Anti-Asian racism score		Model 4: Anti-Hispanic/Latinx racism score		Model 5: Racial ideas about non-Hispanic Whites score		Model 6: Biophysical racism score		Model 7: Cognitive/intelligence racism score		Model 8: Health behavior racism score
Characteristic	$\hat{β}$	95% CI	$\hat{β}$	95% CI	$\hat{β}$	95% CI	$\hat{β}$	95% CI	$\hat{β}$	95% CI	$\hat{β}$	95% CI	$\hat{β}$	95% CI	$\hat{β}$	95% CI
Race/ethnicity
Hispanic/Latinx	−2.32	[−12.77, 8.13]	−1.56	[−4.87, 1.75]	−0.87	[−4.03, 2.30]	0.88	[−2.75, 4.52]	−1.88	[−4.24, 0.47]	−2.14	[−8.93, 4.66]	0.25	[−1.85, 2.36]	0.80	[−1.41, 3.00]
Other students of color	−0.38	[−11.85, 11.10]	−0.61	[−3.63, 3.51]	0.09	[−3.33, 3.50]	1.35	[−2.61, 5.30]	−2.18	[−4.73, 0.37]	2.74	[−4.69, 10.18]	0.64	[−1.62, 2.91]	0.42	[−1.95, 2.78]
Non-Hispanic Whites	Ref.	—	Ref.	—	Ref.	—	Ref.	—	Ref.	—	Ref.	—	Ref.	—	Ref.	—
Sex
Male	−6.07	[−15.14, 3.00]	−2.09	[−4.87, 0.69]	−2.21	[−4.93, 0.51]	−2.39	[−5.44, 0.67]	0.32	[−1,68, 2.32]	−5.95*	[−11.84, –0.05]	−0.74	[−2.54, 1.05]	0.24	[−1.63, 2.12]
Female	Ref.	—	Ref.	—	Ref.	—	Ref.	—	Ref.	—	Ref.	—	Ref.	—	Ref.	—
Major
Public health	—	—	—	—	—	—	2.65	[−0.06, 5.36]	−1.16	[−2.91, 0.60]	—	—	—	—	—	—
Other major	—	—	—	—	—	—	Ref.	—	Ref.	—	—	—	—	—	—	—
Class level
Upper (third- and fourth-year students)	−10.86*	[−18.33, −3.39]	−4.99*	[−7.26, −2.72]	—	—	−3.05*	[−5.57, −0.54]	−2.15*	[−3.78, −0.52]	−6.34*	[−11.13, −1.55]	—	—	—	—
Lower (first- and second-year students)	Ref.	—	Ref.	—	—	—	Ref.	—	Ref.	—	Ref.	—	—	—	—	—
Class Level × Major	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Health profession
Pre-medical	—	—	−2.74*	[−5.00, −0.47]	−1.86	[−4.08, 0.36]	—	—	—	—	—	—	−1.54*	[−3.01, −0.08]	—	—
Other health service profession	—	—	Ref.	—	Ref.	—	—	—	—	—	—	—	Ref.	—	—	—
Parent education
High level (≥BA degree)	—	—	—	—	—	—	—	—	—	—	—	—	−1.16	[−2.66, 0.35]	−1.50	[−3.07, 0.07]
Low level (<BA degree)	—	—	—	—	—	—	—	—	—	—	—	—	Ref.	—	Ref.	—
R ²	.05		.11		.03		.05

Note. Ref. = reference category.

Coefficient significant at p < .05.

Table 4.

Generalized Linear Regression Models Showing the Adjusted Relationships Between Systemic Racism Score and Demographic Characteristics of Pre–Health Professions Student Survey Respondents.

Characteristic	Systemic racism score
	Model 9a		Model 9b
	$\hat{β}$	95% CI	$\hat{β}$	95% CI
Race/ethnicity
Hispanic/Latinx	−2.08	[−5.76, 1.59]	−1.72	[−5.37, 1.93]
Other students of color	−2.49	[−6.45, 1.47]	−1.89	[−5.84, 2.06]
Non-Hispanic Whites	Ref.	—	Ref.	—
Sex
Male	1.91	[−1.20, 5.03]	2.08	[−1.01, 5.17]
Female	Ref.	—	Ref.	—
Major
Public health	−2.28	[−5.02, 0.46]	0.98	[−2.84, 4.79]
Other	—	—	Ref.	—
Class level
Upper (third- and fourth-year students)	−3.84*	[−6.38, −1.30]	−1.79	[−4.82, −1.24]
Lower (first- and second-year students)	—	—	Ref.	—
Class Level × Major	—	—	−6.57*	[−11.98, −1.16]
Health profession
Pre-medical	—	—	—	—
Other health service profession	—	—	—	—
Parent education
High level (≥BA degree)	−2.66*	[−5.28, −0.05]	−2.74*	[−5.33, −0.15]
Low level (<BA degree)	Ref.	—	Ref.	—
R ²	.08		.10

Note. Ref. = reference category.

Coefficient significant at p < .05.

Additional exploratory analyses indicated that students pursuing a pre-medical health profession better identified false ideas about Black/African American people ( $\hat{β} = - 2.74,$ 95% CI [−5.00, −0.47], p = .02) and cognitive/intelligence racism ( $\hat{β} = - 1.54,$ 95% CI [−3.01, −0.08], p = .04), which is one of the domains measuring scientific racism, than students pursuing other health service professions. Students with parents who attained a high level of education ( $\hat{β} = - 2.74,$ 95% CI [−5.33, −0.15], p < .05) had lower agreement with false statements regarding systemic racism than students with parents who had a lower level of education. Finally, male students better identified false ideas about biophysical racism ( $\hat{β} = - 5.95,$ 95% CI [−11.84, −0.05], p < .05) than female students.

Discussion

The results from this exploratory study demonstrate that scientific racism ideologies may be present at early stages in health education and among students pursuing nonphysician medical training. Several quantified racism attitude scores, including in the biophysical racism domain, were lower among students in the upper academic levels of undergraduate education (i.e., third- and fourth-year students). Additionally, scores were lower for the cognitive/intelligence racism domain among pre-medical students, and scores were lower among third- and fourth-year public health majors for the systemic racism domain compared with lower-level non–public health majors. However, despite increased rejection of these notions among subsets of undergraduate students, there is a persistence of some false racial ideas even among pre-medical, public health, and upper-level students, as shown by average scores that still rise above the minimum for each domain in these groups.

This exploratory study has several notable limitations. Given the cross-sectional nature of this research, causality cannot be determined. The overall small sample size and the relatively small sample of non-Hispanic White respondents may have limited the ability to measure differences in racial attitudes across demographic groups. The sample also has a possibility of self-selection bias, and these findings cannot be generalized to the larger population. Additional longitudinal studies with representative samples of students are needed. Scale construction was based on a survey developed by Hoffman et al. (2016), and more questions were added based on a review of the scientific racism literature. However, scale evaluation still requires more testing to determine its suitability for research in additional contexts. Racial/ethnic groups with the highest representation at the university where participants were drawn from were included in the survey. Measures of scientific racism for indigenous populations were not included because of small representation among the respondent population at the university, but this should be explored in future research. Because the study explicitly recruited students to participate in a survey dealing with issues of race, it is possible that only students who were most comfortable thinking about issues of race participated, while those with limited knowledge and interest in issues of race opted not to respond. Future research may randomize students into studies of scientific racism to minimize this bias. Additional research may also consider the effect of particular courses, as well as campus and off-campus experiences, on the progression of student beliefs about race, as well as the effect of structural factors that may mitigate scientific racism ideology more broadly.

A broader cultural shift toward accepting race as a social construct can be seen with the addition of the Psychological, Social, and Biological Foundations of Behavior section to the MCAT (Medical College Admission Test) in 2015. This structural change in expected knowledge among medical students was implemented to recognize cultural, behavioral, and social determinants that may influence health outcomes. The MCAT changes may explain the relatively lower racism scores among the pre-medical students in this study. Even so, the MCAT is the only health profession entrance exam that tests on such topics. Other entrance exams, such as the Dental Admission Test and Pharmacy College Admission Test, could benefit from including similar sections. Health education experts have argued that teaching race as a social construction rather than as a biological category may better allow a range of health professionals to combat racial inequities in clinical settings, research institutions, and communities (Sharma & Kuper, 2017).

In addition, including social science courses in pre-health profession major curricula may also help lessen scientific racism attitudes. For example, some evidence shows that patient race/ethnicity may influence health care provider beliefs about the patient and ultimately affect provider behavior toward the patient (van Ryn, 2002). Having discussions in the classroom that explore upstream determinants of health may help students realize that race is not biological but social. One approach that has been successful in improving understanding of race as a social category is the structural competency model. A recent study conducted at Vanderbilt University evaluated the inclusion of structural competency training in undergraduate pre-health programs. The researchers found that students who took courses emphasizing how social issues shape health were three times more likely to identify a structural factor as a primary force in explaining health disparities than students who did not take such courses (Metzl et al., 2018). Such attention to how a broad array of social conditions, including those outside of health care, affect health outcomes is essential for preparing students to tackle health inequities through their professions and in their communities after training (B. D. Smedley, 2006).

These efforts to combat scientific racism are important because such attitudes affect health services, research, and outcomes. For example, a 2017 study claimed that African American patients express greater airway inflammation than White individuals, which explains their greater burden of asthma. This research failed to take into account how African American populations face greater exposure to environmental hazards and have less access to quality health care, which are most likely the underlying reasons for the higher asthma rates (Nyenhuis et al., 2017). Another study published in 2021 outlined how racialized algorithms for kidney function affect medical practices for people with chronic kidney disease, contributing to a deep-rooted hierarchy of difference in medicine (Braun et al., 2021). This research showed that undergraduate students, even students of color, may hold such racial beliefs early in their academic career. There is thus a need to address scientific racism among students prior to matriculation into health profession training programs. Pre-health professions programs in particular must require students to take courses that promote understanding of race as a social construct. Undergraduate curriculum planning should incorporate this knowledge as a component of core competencies. This study may assist in the conceptualization of theoretical approaches to a pedagogy seeking to dismantle explicit and implicit educational practices reifying scientific racism. Addressing these false ideas about the origins of racial and ethnic inequities in health will help future health care professionals correctly utilize race in clinical settings as a marker for potential risks due to social inequities, not as an innate risk factor responsible for different health outcomes.

Supplemental Material

sj-docx-1-php-10.1177_23733799211043136 – Supplemental material for Scientific Racism Attitudes Among Diverse Undergraduate Pre–Health Professions Students

Supplemental material, sj-docx-1-php-10.1177_23733799211043136 for Scientific Racism Attitudes Among Diverse Undergraduate Pre–Health Professions Students by Kelly M. Bentley, Deborah Fortune, Ronica Rooks, Gayle Walter, Karina Chowdhury and Erin Fanning Madden in Pedagogy in Health Promotion

Footnotes

Acknowledgements

The authors would like to thank the UTSA Mellon Humanities Pathway Program for their support of this project, and all of the students and faculty members who contributed to the development and completion of this study. The authors would also like to thank the anonymous reviewers and editor for their generous and constructive feedback.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Karina Chowdhury

Supplemental Material

Supplemental material for this article is available online at .

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