Elizabeth S. Focella
Department of Psychology, University of Wisconsin Oshkosh, Oshkosh, WI, USA
In the social psychological literature, stereotypes are defined as beliefs, usually overgeneralizations, associating particular traits and attributes with members of a particular group (e.g., “members of this group are lazy”). Prejudice, in contrast, is an attitude (usually unfavorable, e.g., dislike) toward a person based solely on that person's presumed group membership. Both stereotyping and prejudice may be considered a form of bias. Literature in social psychology has long been interested in the concepts of stereotyping and prejudice as a way to understand intergroup relations. More recently, stereotyping and prejudice have been studied in the context of healthcare to help explain the apparent health disparities between groups, particularly between Whites (a term that will be used hereafter to refer to a diverse group of people who identify as White, Caucasian, or European American and people of European descent who may or may not be American) and Blacks (a term that will be used hereafter to refer to a diverse group of people who identify as Black or African American and people of African descent who may or may not be American).
Researchers in a variety of disciplines, including psychology, medicine, and public health, find that even when controlling for factors such as access to healthcare, low socioeconomic status (SES), and disease severity, members of low‐power groups (Blacks in particular) still have worse health outcomes, including increased risk of mortality. Among the potential sources for this disparity, some research documents the presence of differential treatment in numerous areas of healthcare, including medicine, surgery, primary care, and mental health, and for a variety of conditions and procedures, including coronary artery disease, renal disease and kidney transplantation, stroke, cancer, HIV/AIDS, medication for pain management, and advice for smoking cessation (e.g., see Geiger, 2003). Among a number of explanations for these disparities in treatment and outcomes, stereotyping and prejudice in healthcare has received compelling empirical support (for a review, see Chapman, Kaatz, & Carnes, 2013; Dovidio & Fiske, 2012).
A substantial portion of literature concerning stereotyping and prejudice in healthcare examines racial/ethnic bias, particularly against Black patients (van Ryn & Burke, 2000). This is likely due to the fact that Blacks have poorer health outcomes, including risk of mortality, than other racial and ethnic groups. Hence, the literature on bias against Blacks represents a large portion of our empirical knowledge on the topic of bias in healthcare. The literature examining stereotyping and prejudice in healthcare does, however, document prejudice and stereotyping for other racial and ethnic groups, including Hispanic and Native American patients, and exists beyond examination of racial and ethnic bias, including bias toward other low‐power groups including people who identify as lesbian, gay, bisexual, or transgender (LGBT), women, the elderly, and people who are overweight, are diagnosed with mental illness, and abuse drugs and alcohol.
At times, people are able to consciously access and report their feelings (prejudice) and beliefs (stereotypes) about particular groups of people. In such cases, prejudices and stereotypes are conscious, or explicit. In the past few decades, however, research has shown that in some cases, people hold stereotypes and prejudices that they are not consciously aware of (i.e., nonconscious), or are implicit. The literature on prejudice in healthcare examines both explicit and implicit stereotyping and prejudice among clinicians.
In the context of healthcare, the literature documenting clinicians' explicit bias is comparatively less robust than literature documenting implicit bias. This may be the case because most physicians and other healthcare providers intend to treat their patients fairly and may not wish to endorse stereotyping or prejudice directed at these groups, thus making the prevalence of explicit stereotyping and prejudice less pronounced among healthcare providers. When explicit stereotypes are documented, they are typically directly related to concerns about compliance and adherence to treatment. Explicit negative attitudes (i.e., dislike) are typically less reported than stereotypes and are often expressed toward groups for which bias is more socially accepted (e.g., people who are overweight, people who are drug users).
The literature finds that racial and ethnic groups including Blacks, Hispanics, and Native Americans, as well as members of other groups, including persons who are overweight and former injecting drug users, are explicitly stereotyped by healthcare providers as noncompliant. For example, one study found that medical and nursing students reported awareness of stereotypes associating Hispanics and Native Americans with noncompliance (Bean et al., 2014). Similarly, other studies show that healthcare providers stereotype Blacks, people who are overweight, and former injecting drug users as less likely to adhere to treatment. Further, the aforementioned study of medical and nursing students found that medical and nursing students reported awareness of stereotypes associating Hispanics and Native Americans with risky health behavior and difficulty understanding and/or communicating health‐related information.
While the stereotype of noncompliance is associated with several groups, other stereotypes are more group specific. In particular, overweight patients face more unique stereotypes in addition to noncompliance, including perceptions that they lack motivation and are lazy, self‐indulgent, and physically exhausting to care for. Other more general negative stereotypes toward overweight patients include being lower in social attractiveness, as well as physically unattractive.
Other well‐documented explicit stereotypes in healthcare relate to perceptions of controllability, blame, and fault for the patient's health state. These beliefs are often associated with patients who abuse drugs and alcohol, are overweight, are diagnosed with mental illness, and are current or former injecting drug users. Interestingly, these negative attitudes toward those who abuse substances may be influenced by beliefs about controllability and social mores. Literature on healthcare providers' beliefs about overweight patients, as well as those coping with mental illness (e.g., those with schizophrenia) and substance abuse, shows that a portion of healthcare providers express the belief that those patients are responsible for their current state. For example, as documented in some literature, nurses have expressed the belief that their overweight patients' failure to lose weight can be attributed to noncompliance with treatment recommendations and lack of motivation. Further, the literature suggests a link between these negative stereotypes and the clinician's preexisting beliefs about personal responsibility. One study shows that clinicians who identify as conservative, as opposed to liberal, may have more negative attitudes toward patients who are injecting drug users. Mediation analyses indicated that conservative clinicians' negative attitudes toward injecting drug users are due to the belief that injecting drug use is in the patient's control (Brener, Hippel, Kippax, & Preacher, 2010). Taken together, the literature suggests that negative attitudes toward these groups of patients can be characterized by negative personal attributions for the patients' state.
In relation to stereotyping, literature documents the possibility that healthcare providers may, at times, overly attribute their patients' physical ailments to the patients' more salient condition. For example, studies show that nurses in particular express the belief that their overweight patients' ailments are likely the result of their patients' weight and that nurses caring for patients who are injecting drug users express the belief that their patients' ailments are caused by their injecting drug user status. Importantly, this occurs regardless of whether the ailment is in fact due to the patients' obesity or drug use. The tendency to overly rely on a patient's group membership when making a determination about their treatment and diagnoses is a form of stereotyping and presents a potential for diagnostic overshadowing, in which a patients' symptoms and ailments are blamed on another, more obvious condition. In diagnostic overshadowing, a disease or otherwise harmful or life‐threatening condition is not detected because it is “overshadowed” by a more obvious and more salient condition—a circumstance that delays treatment for the true cause of the patient's ailments and possibly an increased likelihood of death. Diagnostic overshadowing occurs for people with a variety of conditions including people who are overweight, have intellectual disabilities, have physical disabilities, and are diagnosed with mental illness.
In addition to explicit stereotyping of patients, the literature also documents the existence of prejudice. In a study about prejudice toward schizophrenic patients, researchers found that healthcare providers showed greater desire for social distance from a schizophrenic, compared with a non‐schizophrenic patient. Other research documents some evidence of explicit prejudice against Black patients, including physicians' lower feelings of affiliation with Black, compared with White patients. Further, prejudice toward those who are overweight among healthcare providers is widely documented, including some healthcare providers' feelings of disgust, repulsion, and pity for their obese patients. Physicians also express embarrassment at the prospect of discussing sexuality and sexually related healthcare with LGBT patients. The literature also documents more general negative affective responses of healthcare providers toward those who abuse drugs and alcohol, as well as those with mental disorders.
Implicit bias can be assessed in a number of ways including psychophysical reactions, neuroimaging, and computerized reaction time tasks that measure participants' associations of particular groups of people with certain attributes as well as general evaluative concepts like “good” or “bad.” Implicit bias is most often assessed through a particular reaction time task known as the Implicit Association Test (IAT). The IAT is administered on a computer, in which participants are asked to categorize a set of words or images. The IAT measures the strength of associations between particular concepts (typically groups of people such as Blacks and Whites) and evaluations (such as good or bad) or stereotypes (such as noncompliant). The notion behind the IAT is that when a person holds an implicit association between a group of people and a stereotype or evaluation (e.g., bad), they should be able to categorize that group of people with words related to the stereotype or evaluation more quickly and with greater accuracy compared with when they are asked to categorize that stereotype or evaluation with other groups of people. For example, if a clinician holds an association between Blacks and noncompliance, they should be able to more easily categorize Blacks with words related to noncompliance compared with when they are asked to categorize Whites with noncompliance.
The literature documenting implicit stereotypes and prejudice among healthcare providers documents a number of empirical findings. First, numerous studies on implicit bias in healthcare show that both physicians and other clinicians such as nurses hold an implicit, and often strong, preference for Whites relative to Blacks. The literature also documents a similar bias against those who are overweight. On a “fat–thin” IAT, healthcare providers show an implicit preference for thin, compared with fat, people. The literature also finds a similar preference among physicians and nurses toward people who are higher, compared with lower, in SES, and there is some discussion in the literature about physicians' implicit as well as explicit bias against LGBT older adults (e.g., see Foglia & Fredrikson‐Goldsen, 2014). Interestingly, the implicit bias that physicians and nurses exhibit toward Blacks and those who are overweight is similar to that of the larger population, suggesting that current medical education does little to reduce implicit bias.
The literature on implicit bias toward patients also shows evidence of stereotyping. Often, these implicit stereotypes pertain to noncompliance. Numerous studies show that physicians, nurses, and medical students implicitly associate Blacks, for example, as being noncompliant, medically uncooperative, and uncooperative generally. Literature shows that Hispanics are also implicitly stereotyped as noncompliant, as well as implicitly associated with health risk (Bean, Stone, Moskowitz, Badger, & Focella, 2013). Notably, the literature also shows that Blacks are not only implicitly associated with diseases that are epidemiologically relevant for Blacks (e.g., sickle cell anemia) but that they are also implicitly associated with conditions and social behaviors with no inherently biological or genetic relationship with Blacks including obesity and drug use (Moskowitz, Stone, & Childs, 2012).
Some recent empirical studies not only document the existence of prejudice and stereotyping among clinicians but also show that these biases influence their clinical decision making, including treatment, as well as their interactions with patients. In a landmark study by Green et al. (2007), physicians read a clinical vignette of either a Black or White patient presenting to the emergency department with an acute coronary syndrome. These physicians were later asked to make recommendations of whether to treat the patient with thrombolysis. Green et al. (2007) found that while physicians reported no explicit preference for Whites relative to Blacks or differences between Whites and Blacks on perceived cooperativeness, they did exhibit an implicit preference favoring Whites over Blacks as well as implicit stereotypes of Blacks as less cooperative with medical procedures and less cooperative more generally. Importantly, they identified a link between physicians' implicit bias and their treatment recommendations—as physicians' implicit pro‐White bias increased, the lower their likelihood of treating Black patients with thrombolysis.
Other studies show similar findings, providing evidence that bias influences clinical decision making. For example, literature shows that implicit bias against the mentally ill can predict overdiagnosis, while explicit bias predicts more negative prognoses (Peris, Teachman, & Nosek, 2008). Additionally, research finds that perceptions of cooperativeness are predictive of treatment recommendations. This may disadvantage members of minority groups including Blacks, Hispanics, people who are overweight, and people who are mentally ill, who are stereotyped (implicitly as well as explicitly) as being less medically cooperative relative to Whites, the thin, and those not labeled as mentally ill.
Further, it is widely observed that White patients are more likely to receive pain treatment than non‐White patients. This disparity in treatment, research indicates, can be predicted by physicians' implicit pro‐White bias. For example, as pediatricians' implicit pro‐White bias increases, the lower their likelihood of prescribing narcotic medication to Black patients (Sabin & Greenwald, 2012). To explain why pro‐White bias influences pain treatment, some research has explored the possibility of an empathy gap—namely, that physicians with a stronger implicit pro‐White bias are less able to empathize with their non‐White patients. One study in particular (Drwecki, Moore, Ward, & Prkachin, 2011) showed that the more participants (including undergraduate students, physicians, and nurses) empathized with Whites compared with Blacks, the less pain treatment participants gave to Blacks relative to Whites. This work suggests that the widely observed disparity in pain treatment between Whites and Blacks is due, in part, to clinicians' lowered ability to empathize with, and thus assess, their Black patients' pain.
Stereotyping and prejudice not only influence treatment recommendations, but they also impact the way that clinicians interact with their patients. For example, some research shows that nurses who have implicit prejudice against those who are overweight are less likely to make eye contact with an overweight patient (Persky & Eccleston, 2011). Other studies show that healthcare providers' (including physicians' and genetic counselors') pro‐White bias predicts using less emotionally responsive communication when counseling non‐White patients and, when interacting with Black patients, more clinical verbal dominance, lower patient positive affect, and poorer ratings of interpersonal care. When interacting with White patients, in contrast, physicians and other healthcare providers exhibit lower levels of verbal dominance, receive more positive ratings of nonverbal effectiveness, and have higher ratings of interpersonal care.
With regard to patient stereotyping, race and compliance stereotyping is associated with longer visits, slower speech, less patient‐centeredness, and poorer ratings of interpersonal care with Black patients. When interacting with White patients, in contrast, physicians exhibit less verbal dominance and typically have shorter visits, faster speech, and higher clinician positive affect. Importantly, several studies show that medical visits with minority patients compared with White patients are less patient‐centered, which is predicted by implicit pro‐White bias. The more pro‐White bias on the part of the clinician, the less patient‐centered their care of their minority patients (e.g., see Cooper et al., 2012).
Other sophisticated research on the topic of stereotyping and prejudice in healthcare finds interesting nuances and implications of the physician–patient interaction on patient health. For example, research exploring patients' perceptions of discrimination on the part of their clinicians finds that the more patients' perceive discrimination and bias from their healthcare providers, the lower their adherence to their clinicians' treatment recommendations, which may have negative impacts on their health (e.g., see Bird, Bogart, & Delahanty, 2004).
In addition, research in social psychology finds that while people might be low in explicit prejudice, they could nonetheless harbor nonconscious, or implicit, bias. In the context of healthcare, clinicians who possess egalitarian beliefs (i.e., who have low explicit prejudice), but who have implicit prejudice (termed aversive racists), may have particularly poor interactions with their patients. Notably, research shows that many healthcare providers, including physicians, tend to have low explicit bias, but nonetheless hold implicit biases. In interactions with a minority group member, people who are aversive racists tend to send mixed messages—while their low explicit prejudice tends to express itself in overt behavior (e.g., positive verbal content), their high implicit prejudice tends to “leak out” in their nonverbal behavior (e.g., less eye contact, distancing nonverbal behaviors). Since the minority interaction partner attends to the nonverbal cues in making judgments about the quality of the interaction, the minority person tends to have more negative ratings of the interaction and of their interaction partner. In fact, a study by Penner et al. (2010) found that Black patients even had more positive interactions with physicians who were high in both explicit and implicit bias than physicians who had low explicit but high implicit bias (i.e., aversive racists). Taken together, the work on explicit and implicit bias suggests that the conscious (explicit) and the unconscious (implicit) bias held by clinicians interact to influence clinical decision making, the quality of clinicians' interactions with patients, and patients' perceptions of bias.
While several studies have documented that stereotyping and prejudice influence physicians' treatment of patients, some have failed to find a direct effect of biases on treatment recommendations. For example, while several studies find that clinicians explicitly and implicitly stereotype their overweight patients as less adherent to treatment, some of these studies find that the extent to which clinicians hold this noncompliance stereotype has no predictive influence on treatment recommendations. Further, a subset of studies documenting explicit and implicit bias toward Black, Hispanic, and mentally ill patients failed to find effects of these biases on clinical decision making including physicians' treatment recommendations. This literature that documents bias but shows no impact of bias on treatment suggests that further study is needed to understand the specific conditions under which bias may be predictive of clinical decision making, as well as the more nuanced impact of bias on healthcare.
For example, although several studies do not show a direct effect of race (or other low‐power group status, e.g., mental illness) on clinical recommendations, several show that clinicians' perceptions of uncooperativeness and doubts about adherence alter treatment recommendations. Namely, they show that lower perceived adherence is predictive of lower likelihood to recommend treatment. These results suggest that differences in treatment are not necessarily directly influenced by race or other group status but that they may be indirectly influenced through stereotypes, including lower perceptions of cooperativeness and adherence.
In addition, in the studies that fail to show a link between stereotyping and prejudice and treatment recommendations, there is often a standard of care that is typically followed. With little ambiguity and thus less discretion, it might be difficult to capture bias. Further, authors of some studies that fail to show a link between explicit or implicit bias and treatment recommendations note that participants might have been aware and/or were suspicious of the study's purposes. With greater attention paid to race as a variable of interest, participants may have been particularly sensitive to make sure that their minority patients were given treatment as opposed to not. In Green et al.'s (2007) study, for example, 67 physicians were excluded from the analyses because they expressed awareness of the nature of the study. Yet, when these 67 “aware” physicians were included in the results, Green et al. found an interaction effect. As unaware physicians' pro‐White bias increased, their likelihood of recommending thrombolysis to Black patients decreased. In contrast, increase in pro‐White bias among aware physicians was associated with more thrombolysis recommendations for Black patients.
The extant literature suggests situations in which stereotyping and prejudice are more likely to bias clinical decisions. First, situations marked with ambiguity are more likely to elicit bias. Research shows that in cases with clinical uncertainty, where a medical procedure is discretionary, or treatment guidelines are not well defined, the likelihood of clinician bias is greater. In addition, previous research shows that being under high cognitive load makes people, including physicians, more likely to behave according to stereotypes and prejudice. Similarly, when people are in time‐pressured situations, which necessitate that they simultaneously attend to numerous tasks, they tend to be more likely to engage in stereotyping and to act according to their biases. Unfortunately, the medical context, which is often one marked by limited time and high cognitive load, may be especially likely to elicit bias.
To ameliorate the impact of stereotyping and prejudice in healthcare, the extant literature suggests several solutions. First, hiring a more diverse set of physicians may alleviate some of the observed disparities. Previous research finds that racially concordant medical interactions (those in which the physician and patient are the same race) are marked with less perceived discrimination, greater trust, greater satisfaction, and more effective communication (e.g., Cooper et al., 2003). Hence, increasing racial minority patients' access to racially concordant physicians may help to produce more productive medical visits for racial minority patients. The literature also suggests that workshops may help by making physicians, nurses, and medical students aware of their implicit bias and how it may influence their clinical decision making and their interactions with patients. Finally, bias‐reduction strategies that have been successful in laboratory and some field settings may also be effective in the context of healthcare to reduce prejudice and stereotyping. These include taking the perspective of the minority patient (i.e., perspective taking), perceiving the patient as an individual as opposed to a member of their minority group (i.e., individuation), fostering a sense of common identity between the provider and the patient (i.e., common identity), and considering the ways in which the patient does not fit with stereotypes of his or her minority group (i.e., counterstereotyping). Individuation and perspective taking in particular have been shown to reduce biases in medical treatment, including pain treatment (see Chapman et al., 2013). Specifically, this research finds that inducing perspective taking increases physicians' empathy for their minority patients, improving their ability to assess their patients' pain and, consequently, increase the provision of treatment. Importantly, these bias‐reduction strategies may have a place in medicine to alleviate the impact of prejudice and stereotyping in healthcare.
There are several directions for future research that will enhance our understanding of stereotyping and prejudice in healthcare. For example, while some bias‐reduction strategies have been shown to be effective, further research is needed to understand how, and under what circumstances, these known strategies may be useful specifically in the healthcare context. In addition, given that some literature has failed to find a link between stereotyping and prejudice and clinical recommendations, further research is needed to determine the specific conditions under which stereotyping and prejudice will influence providers' recommendations and interactions with patients and the instances in which it will not.
Future research may also examine the role of clinicians' expectations on patient health outcomes. For example, if a clinician has low expectations (based on existing prejudice and stereotypes) that a patient will commit to therapy or treatment recommendations, some research suggests that they may be less likely to counsel the patient or to suggest a more challenging, yet perhaps more effective, course of treatment. As a result, the patient may experience worse health outcomes. Future research should more thoroughly examine the extent to which these expectations influence interactions with patients and how they influence the type and quality of treatment recommendations.
Further, research has focused more on the existence of stereotyping and prejudice among clinicians, but less on examining patients' own lived experience of stereotyping and prejudice. Not only would it be beneficial to obtain a qualitative understanding of these experiences, but it would also be beneficial to understand the cumulative effect of these experiences on patients, how these experiences may influence patients' perception of and participation in healthcare, and how these experiences impact patients' subsequent interactions with clinicians. Given that a great deal of work examines stereotyping and prejudice in short‐term studies, future work may also include more long‐term studies that examine how negative interactions with clinicians, and the healthcare system more generally, may influence patients' subsequent participation in healthcare.
Importantly, a more thorough investigation of patient–provider communication is warranted. Medical decisions are not made in a vacuum—rather, they derive from an interaction between the clinician and the patient. Future work may investigate how the nuances of these interactions shape the patients' attitudes and behavior toward the physician and how these interactions may influence the physician's clinical decision making. Research that investigates the more nuanced and reciprocal nature of patient–provider interaction (e.g., using non‐obtrusive observation methods such as the Electronically Activated Recorder; Mehl & Robbins, 2012) may be particularly effective at understanding prejudice and stereotyping in the dynamic, and multifaceted, context of healthcare.
Dr. Elizabeth S. Focella is an assistant professor of psychology at the University of Wisconsin Oshkosh. Her research examines attitudes and behavior change, chiefly in the context of health promotion. One major focus of her research examines prejudice and prejudice reduction, particularly in healthcare. Another line of her work, in cognitive dissonance, examines how people are motivated to change when they are made aware of the discrepancy between their pro‐health attitudes and their unhealthy behavior.