Affective Interactions in Medical Visits

Abstract

Objective: Investigate influence of ethnicity on older American Indian patients’ interpretations of providers’ affective behaviors. Method: Using data from 115 older American Indian patients, random effects ordered logit models related patient ratings of providers’ respect, empathy, and rapport first to separate measures of American Indian and White American ethnicity, then to “ethnic discordance,” or difference between providers’ and patients’ cultural characteristics. Results: In models accounting for patients’ ethnicity only, high scores for American Indian ethnicity were linked to reduced evaluations for providers’ respect; high scores on White ethnicity were associated with elevated ratings for empathy and rapport. In models accounting for provider–patient ethnic discordance, high discordance on either ethnicity scale was associated with reduced ratings for the same behaviors. Discussion: Findings support “orthogonal ethnic identity” theory and extend “cultural health capital” theory, suggesting a pathway by which ethnicity becomes relevant to experience of health care among older adults.

Keywords

ethnicity medical interaction American Indians cultural health capital

High quality medical interaction is associated with positive health outcomes via improved adherence to doctors’ advice, satisfaction with care, utilization, and trust (reviews in Brédart, Bouleuc, & Dolbeault, 2005; Roter & Hall, 2006; Smedley, Stith, & Nelson, 2002). Frameworks for evaluating interaction commonly distinguish health care providers’ instrumental or “cure-oriented” behaviors, such as instructions, from affective or “care-oriented” behaviors, such as displays of respect. Affective behaviors ideally assist providers in “building rapport and demonstrating responsiveness to the patient’s emotions” (Roter & Hall, 2006, p. 51), often functioning as a gateway to instrumental interactions (Miaoulis, Gutman, & Snow, 2009, p. 56). Quality of affective exchange is also a strong—sometimes the strongest—predictor of patient outcomes, showing relationships with satisfaction, quality of life, and treatment effects that persist for months after the medical visit (Dibbelt, Schaidhammer, Fleischer, & Greitemann, 2009; Ong, Visser, Lammes, & de Haes, 2000).

Affective dimensions of medical interaction may take on special relevance for vulnerable patient subgroups. Black, Hispanic, and Asian patients are more likely than Whites to rate providers’ concern, courtesy, and respect as “more important than anything else” about their health care (Murray-Garcia, Selby, Schmittdiel, Grumbach, & Quesenberry, 2000). Much the same may be true of elders, to the extent that a recent review urged health care providers to view “effective and empathic communication” with older patients as a literal “therapeutic agent” (Williams, Haskard, & DiMatteo, 2007).

Yet patients for whom the affective dynamics of medical encounters carry special significance may confront unusual challenges. Ethnic minority patients, including African Americans and Hispanics, are less satisfied than White patients with their providers’ ability to communicate emotions such as friendliness, caring, and compassion (Stewart, Nápoles-Springer, Gregorich, & Santoyo-Olsson, 2006; Taira et al., 2001). These same patient subgroups perceive “disrespectful” treatment from health care providers significantly more often than Whites (Agency for Health Care Research and Quality, 2006; Blanchard & Lurie, 2004; Hogue & Hargraves, 2000; Johnson, Roter, Powe, & Cooper, 2004; cf. Blanchard, Nayar, & Lurie, 2007). Analyses using objective measures, including audiotape analyses or ratings from independent observers, support perceptions of lower quality affective interaction with racial-ethnic minority patients (review in van Ryn, 2002). Their medical visits feature less chatting (Oliver, Goodwin, Gotler, Gregory & Stange, 2001), poorer rapport (Ghods et al., 2008; Shapiro & Saltzer, 1981; Siminoff, Graham, & Gordon, 2006), fewer empathic responses (Hooper, Comstock, Goodwin, & Goodwin, 1982; Squier, 1990; review in Ferguson & Candib, 2002), and fewer positive provider statements (Sleath, Rubin, & Arrey-Wastavino, 2000).

A separate set of studies finds older patients similarly at risk for certain medical communication problems (review in Roter & Hall, 2006, pp. 58-62). Elders receive less of providers’ time (Clark, Potter, & McKinlay, 1990) and less advice on managing their conditions (Young & Kahana, 1989); they frequently have less “participatory” medical visits (Greene, Adelman, Friedman, & Charon, 1994; Kaplan, Gandek, Greenfield, Rogers, & Ware, 1995). Older patients also report difficulty directing medical talk toward problems they believe important (Rost & Frankel, 1993), while doctors are disproportionally likely to use “baby talk”—simplified speech delivered with exaggerated intonation—when addressing institutionalized elders (Caporael, 1981; Ryan, Hamilton, & See, 1994). In a rare study using outside evaluators, observers rated physicians less egalitarian, engaged, patient, and respectful when they interacted with older as compared to younger patients (Greene, Adelman, Charon, & Hoffman, 1986).

In short, race, ethnicity, and age all affect medical communication and outcomes. Nevertheless, very little research has examined quality of medical interaction with patients occupying an intersection of vulnerable statuses, such as elders within ethnic minority populations. Research commonly attributes disrupted medical interaction in patient subpopulations to cultural misunderstandings (Nápoles-Springer, Santoyo, Houston, Pérez-Stable, & Stewart, 2005). Yet current work sheds remarkably little light on how ethnic characteristics of diverse, older patients influence affective exchange during health care visits. Do patterns of provider–patient interaction respond only to patients’ minority ethnicities, or does their familiarity with the White American culture matter, too? Do patients’ specific cultural characteristics relate to different dimensions of affective interaction? Is the provider’s ethnic identification also important? With guidance from current work on “orthogonal ethnic identity theory” and from the emerging theory of “cultural health capital,” we address such questions using a sample of primary care visits with older American Indians who vary in their ethnic (cultural) characteristics.

Affective Interaction With Older American Indian Patients

American Indian older adults constitute an especially appropriate population in which to examine issues of ethnic diversity in medical interaction. Confronting multiple health challenges (Goins & Spencer, 2005; U.S. Department of Health and Human Services, n.d.), they also exhibit considerable ethnic variation; while citizens of America’s more than 560 tribal nations often retain distinctive linguistic and cultural practices, many simultaneously identify in varying degrees with the larger American culture. Although the U.S. exercises legal responsibility to deliver health care to American Indians (Dixon & Roubideaux, 2001), studies of medical interaction that include this subpopulation are scarce. The publications in this small body of work nevertheless often deal with communication needs among elders in particular, and they suggest concerns for affective interaction resembling those observed in other vulnerable populations.

General research on medical communication has commonly examined several variables describing affective dimensions of medical interaction, with particular attention to the qualities of providers’ respect (Beach, Roter, Wang, Duggan, & Cooper, 2006; Blanchard & Lurie, 2004), empathy (Barnett, 2001; Hull, 2007), and rapport (Barnett, 2001; Hull, 2007). These variables are central to the widely invoked “functional model” of medical interviewing, having particular relevance for the function of “relationship building” (Cole & Bird, 2000); they are likewise highlighted in important frameworks for analyzing medical talk such as the Roter Interaction Analysis System (Roter & Hall, 2006). The limited and almost exclusively qualitative literature on medical interaction with American Indian patients, including elders, reflects interest in the same variables, which we discuss below.

The authors of a commonly used textbook on medical interviewing observe that a provider’s respect “will foster a positive relationship and promote the patients’ capacity for coping” (Cole & Bird, 2000, p. 20). A deficiency of this quality is the most commonly raised issue in research involving Native patients from both the United States and Canada (Browne & Fiske, 2001; Garwick, 2000; Wittig, 2004). In-depth interviews conducted on 12 American Indian reservations in New Mexico found that patients described lack of respect from providers as the most common barrier to receiving care, with 50% of respondents reporting it (Malach & Segel, 1990; similarly, Browne & Fiske, 2001; Ketchin, 1997; Rose & Garwick, 2003). Qualitative research among Canadian Native patients has suggested a similar theme in both reservation (Kelly et al., 2009; Towle, Godolphin, & Alexander, 2006) and urban populations (Hendrix, 2003). Asked for specific indicators of disrespect, respondents observed that doctors sometimes addressed them in ways that make them feel inferior (Towle et al., 2006); others explicitly identified issues with providers who “talked down” (Fifer, 1996). Tribal respondents have underscored the relevance of deference particularly toward older patients, whom they describe as especially “proper” and “formal” in interpersonal interaction (e.g., Dixon & Iron, 2006, p. 121).

Empathy, as discussed in the general literature on medical interaction, “is a term indicating one person’s appreciation, understanding, and acceptance of someone else’s emotional situation” (Cole & Bird, 2000, p. 16). Studies that have examined providers’ empathy with Native patients are rare. They suggest that American Indian respondents have recognized empathic health professionals by their soft vocalizations and comforting words, patience while patients make decisions, and similar kindnesses. Lack of empathy has been identified with criticism (specifically of elders), with “pushiness,” and with behaviors aimed at “getting rid of” patients (Fifer, 1996; Kelly et al., 2009; Towle et al., 2006). Research on the Rosebud Reservation found that its Lakota (Sioux) respondents also construed providers’ neutral affect and unemotional delivery—even of very bad news—as indicating lack of empathy (Robertson 2009).

Finally, rapport in medical interaction has been characterized as “nonverbal synchrony between two persons.” More succinctly, “rapport means ‘I am with you’” (Cole & Bird, 2000, p. 227). Work in both the general (Hall, Milburn, Roter, & Daltroy, 1998) and American Indian populations (Fifer, 1996; Kelly & Brown, 2002) argues that patients often link rapport with social chat or “small talk.”

Exceptions to the qualitative emphasis of research on medical interaction with American Indians are few, but they support the conclusion that relationships between ethnicity and medical interaction in older adult American Indian populations are complex. In particular, an analysis of survey data found that older Cherokee patients who rated themselves high on American Indian ethnicity reported reduced satisfaction with aspects of medical interaction related to providers’ social skills and attentiveness (Garroutte, Kunovich, Jacobsen, & Goldberg, 2004). In its analytic design, that study neither specifically examined affective interaction nor had opportunity to use insights derived from the subsequently published work on cultural health capital (CHC) theory; nevertheless, its findings recommend further investigation of how older patients’ ethnic (cultural) characteristics may relate to experience of care. The current study attempts such an investigation by focusing on affective interaction and drawing on the newly emerging body of theory relating ethnicity and medical interaction.

Theoretical Background

Orthogonal Ethnic Identity Theory

The first theoretical literature relevant to our investigation of culture and medical interaction comprises current work on ethnic identities. Older models of ethnic identification conceived ethnicity as a continuum on which minority group members moved from cultural distinctiveness to “acculturation.” In this view, ethnicity could be assessed by a single measure because stronger ties to the dominant culture implied weakened ties to minority culture.

By contrast, current work in “orthogonal ethnic identity theory” assumes that individuals may identify to varying degrees with a minority and with White American culture, without necessarily becoming acculturated (Moran, Fleming, Somervell, & Manson, 1999; Oetting & Beauvais, 1990-1991). Conceiving majority and minority identifications not as endpoints on a continuum but as independent (though perhaps not entirely uncorrelated) qualities, this perspective proposes their assessment by separate measures; it thus accommodates complex ethnic expressions, such as biculturalism. Consistent with orthogonal ethnic identity theory, our study asked patients to describe the extent of their identification with both American Indian and White American ethnicities and examined implications for medical interaction for each ethnicity separately.

Cultural Health Capital Theory

The second relevant literature focuses on the recently propounded theory of cultural health capital (CHC). Extending Pierre Bourdieu’s broader concept of cultural capital, CHC theory offers a “coherent framework for understanding how broad social inequalities operate in patient–provider interaction, and shape the content and tone of health care encounters” (Shim, 2010, p. 1). It argues that patients’ display of certain skills and values—including “linguistic facility, a proactive attitude toward accumulating knowledge, the ability to understand and use biomedical information, and an instrumental approach to disease management”—confer advantages in establishing “attentive and satisfying engagements with health professionals” (p. 2). Often viewed within medical systems as simply “commonsense” behaviors, such orientations are nevertheless “socially-transmitted and differentially distributed,” reflecting perspectives and values favored in dominant societal groups (p. 1).

By highlighting unequally distributed interaction resources, CHC theory directs attention to “variables and phenomena that might be potentially important in stratifying care and, in turn, disease risks and outcomes” (p. 12). The empirical analyses described below combine insights from the emerging perspective of CHC theory with ideas about ethnic complexity drawn from orthogonal ethnic identity theory. These analyses assume that American Indian patients may bring multiple ethnic identities to medical encounters, each of which may carry implications for different affective interactions. They also explore how complex ethnicities may become important for ratings of affective interaction in visits we labeled as high on “ethnic discordance,” meaning that they involve providers and patients with substantially different scores on measures of American Indian or of White American ethnicity.

Research Questions

Using a sample of American Indian older adult patients at one primary care clinic operated by the Cherokee Nation in northeastern Oklahoma, we selected questions asking patients to rate behaviors related to three domains of providers’ affective interaction highlighted in the literature: respect, empathy, and rapport. We examined hypotheses from approaches that accounted for ethnicity in two different ways. One approach (A) focused exclusively on individual patients’ ethnicity, as measured by separate scales of American Indian and White American ethnicity. The second approach (B) treated ethnicity as a characteristic of patients and providers by focusing on “ethnic discordance” or difference between the ethnic identities of patients and providers on either American Indian or White American identity. Hypotheses were:

	Approach A Individual patient ethnicity	Approach B Provider–patient ethnic discordance
Hypotheses	A-1. Patients strongly identifying with American Indian ethnicity will give reduced ratings for provider behaviors as compared to patients who identify weakly.	B-1. Visits characterized by high provider–patient discordance on American Indian ethnicity will be associated with reduced ratings of providers’ affective behaviors, as compared to low discordance visits.
	A-2. Patients strongly identifying with White American ethnicity will give elevated ratings for provider behaviors as compared to patients who identify weakly.	B-2. Visits characterized by high provider–patient discordance on White American ethnicity will also be associated with reduced patient ratings of providers’ affective behaviors, as compared to low discordance visits.

Method

Data

The Cherokee Nation, headquartered in Tahlequah, Oklahoma, is the second largest American Indian tribe, embracing close to 300,000 citizens. Under a self-governance agreement with the U.S. Congress, the Cherokee Nation receives direct funding to deliver health care to American Indians. At time of data collection, these were provided through the Cherokee Rural Health Network: two hospitals and six tribally managed outpatient clinics dispensing services without charge. Data were collected at the chronic care unit of one clinic, chosen in consultation with the Cherokee Nation Institutional Review Board to maximize patients’ ethnic diversity. Primary care was provided at the study site by four physicians and three midlevel practitioners (one nurse practitioner, two physicians’ assistants). The specialization of all providers was family practice; all agreed to participate. We obtained providers’ written consent and collected survey information on their characteristics before data were collected from patients.

Patient participants were persons of American Indian race and varying ethnic characteristics. For American Indians, race is a social classification (Krieger, 2000) inscribed in federal and tribal law (Garroutte, 2003). In this study, “American Indian race” describes individuals eligible to use tribal clinics by virtue of tribal citizenship. People of American Indian race, however, are a multiethnic population wherein members identify to varying degrees with tribal culture and the larger American culture. All participants were ≥ 50 years of age; this selection reflected concern from the Cherokee Nation IRB for a patient group that is held in high cultural regard while suffering serious health challenges (Goins & Spencer, 2005). All patients had an appointment for treatment of a nonacute medical condition, such as diabetes or cardiovascular disease; all were citizens of a federally recognized tribe, usually Cherokee. Inclusion criteria required patients to understand English and be mentally competent, as judged by the intake nurse.

Notices describing the study were distributed to arriving patients on 11 consecutive clinic days in July-August 2001. During normal clinic intake, a nurse screened patients for eligibility and asked if they wished to learn about the study. To maximize comfort for bilingual participants, interested patients received a description from a Cherokee/English translator who secured written consent. At conclusion of their medical visit, patients completed a survey about providers’ interactions. This postvisit questionnaire also asked personal characteristics, including extent of identification with American Indian and White American ethnicities. Each patient saw only one provider during the medical visit and was instructed to report only on “today’s visit.” The questionnaire was designed to permit self-administration, although some respondents (27%) asked to have the form read to them; statistical comparisons showed no significant patterns of differences in outcomes between self- and interviewer-administered forms.

Subsequent to a process of planning and negotiation between multiple stakeholders involved in the research, the study was approved by the Cherokee Nation and the Boston College Institutional Review Board. This process has been described in detail elsewhere (Manson et al., 2004). Patients received a US$15 store gift certificate; providers were not compensated. Overall, 115 unique patient visits were assessed. Of patients invited to participate, 27% refused; those who refused tended to be women and were, on average, 2 years older than participants. Exclusion criteria did not disallow any participant. Although high patient flow occasionally required researchers to cease recruitment, we invited participation from more than 90% of eligible patients.

Measures

Dependent variables: Five dependent variables, selected for their correspondence to issues raised in the literature on provider–patient interactions with American Indians, measured each of three main domains of affective interaction. We drew our items from a set of measures commonly used to address multiple dimensions of providers’ interaction in general population studies (e.g., Bertakis, Roter, & Putnam, 1991; Roter et al., 1997); selected items also resemble measures of affective medical interaction used in ethnic subpopulations (e.g., Stewart et al., 2006). All measures invited patients to use a 5-point scale (1 = strongly disagree to 5 = strongly agree) to evaluate statements about affective interactions characterizing that day’s visit. Our measures of respect were based on responses to the following two statements: “My health care provider acted as though he or she were doing me a favor by talking to me,” and “My health care provider talked down to me.” We reversed the scale (5 = strongly disagree) for these measures of respect so that higher ratings implied higher patient satisfaction. A measure of empathy asked patients to rate the statement, “My health care provider really seemed to care about me and my health problems.” Two items measured rapport by asking patients to evaluate the statements, “I count on my health care provider to set my mind at ease when I am worried,” and “My health care provider and I discussed social topics such as sports or hobbies.”

Ethnicity and ethnic discordance measures: Drawing on a published measure developed and tested in American Indians (Moran et al., 1999; Oetting & Beauvais, 1990-1991), we created two separate measures of patients’ and providers’ self-rated ethnic identities by averaging responses to a series of questions with ordinal response options ranging from 0 = not at all to 3 = a lot. On the scale measuring American Indian ethnicity (Cronbach’s α = .80), the first item inquired about extent to which respondents “live by or follow the American Indian way of life.” Because cultural participation occurs within kinship relations, the second item asked the same question about the patient’s family. A third item asked the importance patient assigned to “follow[ing] religious or spiritual beliefs that are based on traditional Indian beliefs.” A separate scale comprised questions about White American ethnicity (Cronbach’s α = .81). The first item asked how much respondents “live by or follow the White American way of life.” The second item asked the same question about the patient’s family. A question assessing importance of Christian beliefs was dropped from this measure due to its low correlations with other items.

Notably, because the American Indian and White American scales are independent, respondents’ scores on one scale do not imply anything about scores on the other. Consistent with orthogonal ethnic identity theory, respondents can describe themselves as monoculturally White, monoculturally Indian, or bicultural in varying degrees. They can even score themselves low on both scales, indicating an ethnic identity marginal to both cultures.

For analyses including measures of patients’ ethnicities, the highly skewed distribution of scores on the White American identity—with more than half of the patients (59%) having the maximum possible value—made treating it as a continuous variable problematic; therefore, we created a dichotomy indicating strong White American identification (scores coded as 1 ranged from 2.5 to 3 and represented two upper terciles of the distribution). Scores for the American Indian identity scale were more evenly distributed and thus amenable to treatment either as continuous or dichotomous; for consistency, we used a dichotomy indicating strong American Indian ethnicity, defined as the upper tercile of the distribution (scores ranging from 2 to 3).

For analyses including measures of ethnic discordance, we used the continuous scales of ethnic identity to create two measures of provider–patient discordance by calculating absolute values for the difference between patient’s and provider’s scores for each identity. Similar to the White American identity scale for the patients, the distribution of scores for White American ethnic discordance had a pronounced skew. Therefore, we created a dichotomy indicating high ethnic discordance on the White American scale, defined as discordance of ≥ 1 unit, which represents the upper tercile of discordance scores. Scores for the American Indian ethnic discordance were more evenly distributed. For consistency, we also created a dichotomy but selected a higher threshold (≥ 2 units) to ensure that a similar proportion of cases (approximately one third) was classified as highly discordant on both ethnic discordance measures.

Patient demographics: Demographic information included age, sex, marital status, and tribe; socioeconomic measures included education and income. Age and education were continuous variables measured in years. Sex and marital status were dichotomies indicating males and married respondents. Respondents specified their primary tribe, which was recoded into a dichotomy indicating Cherokee. A single question inquired about combined, pretax income of everyone living in the household in the calendar year. This variable contained much missing data and was excluded from multivariate analyses; missing data analyses showed no significant differences on the ethnicity measures between respondents who reported income data and those who did not.

Visit characteristics: Number of previous visits with same provider was measured by a dichotomy indicating those having two or more prior visits. Time in waiting room was measured by a dichotomy indicating those waiting greater than 30 minutes.

Provider measures: In addition to scores on American Indian and White American ethnicity scales, we collected providers’ age, sex, race, marital status, and tribe (if any). Providers also reported training (doctor, nurse practitioner, physician’s assistant, other).

Statistical Analyses

We conducted bivariate analyses and examined means and percentages for patient, provider, and visit characteristics. Next, we estimated two sets of random effects ordered logit models. Such models are ideally suited to our multilevel data because dependent variables are ordinal, and patients are nested within providers. All our analyses were patient-level rather than provider-level, including those focusing on patient–provider discordance. Lack of independence of observations was explicitly accounted for by modeling provider-specific influences as a random variable. Given the small number of providers, we could not directly use measures of provider characteristics as independent variables in analyses, but random effects models controlled for effects of systematic variation associated with any provider characteristic (sex, race, etc.); this avoided omitted variable bias with regard to provider characteristics.

The first set of models tested Hypotheses A-1 and A-2, which accounted for ethnicity as a characteristic of individual patients. They examined relationships between the two measures of patients’ self-reported ethnicities and their ratings for each provider behavior after adjusting for covariates. The second set of models tested Hypotheses B-1 and B-2, which accounted for ethnicity as a feature of the relationship between patient and provider cultural characteristics. These examined links between patients’ ratings for provider behaviors and the measures of provider–patient ethnic discordance, also with adjustment. Additional analyses (not shown) included interaction terms between the two ethnicity variables and between the two discordance measures. No statistically significant interactions were identified in any model; therefore, we used the two ethnicity variables and the two discordance measures as independent predictors.

Regression diagnostics conducted to assess linearity and multicollinearity indicated no issues. Given the skewed distribution of dependent variables, we also conducted analyses with dichotomized outcomes (strong agreement/disagreement vs. other responses); these produced similar results (not shown). Because few cases contained missing data on independent variables, we relied on listwise deletion; three cases were deleted. Power analyses using G*Power indicated that our models had excellent power to detect a medium-size effect; given the listwise deletion-based sample size of 112 and α = .05, the power for detecting a significant medium-size effect for a given predictor in a regression model with nine predictors was β = .98. All bivariate and multivariate analyses used Stata 11.

Results

As shown in Table 1, all patients were citizens of a federally recognized tribe. The majority of patients were Cherokee (91%) while an additional 6% were Choctaw; other tribes included Chickasaw, Creek, and Sac and Fox (about 1% each). Patients were distributed across the range of scores on both American Indian and White American ethnicity scales although White American identification was more prominent: 67% of patients reported strong White American identification while only 33% reported strong American Indian ethnicity. Patient scores on the two ethnicity scales were only moderately correlated (r = –.28), confirming that they measured separate ethnicities. A cross-tabulation of dichotomies showed that 51% of the sample identified as mostly White, and 17% identified as mostly American Indian; 16% reported strong ethnic identification on both dimensions, and another 16% identified as “neither.” Such findings are consistent with the assumption of orthogonal ethnic identity theory that, although scores on White American and American Indian identity may be correlated, they nevertheless represent separate dimensions.

Table 1.

Provider, Patient, and Visit Characteristics

Characteristics	Providers (n = 7)^a	Patients (n = 115)
Demographic
Age, mean years (range)	47 (26-62)	63 (50-89)
Male (%)	57	40
Married (%)^b	100	63
Race (%)
American Indian	57	100
White	43	0
Primary tribal affiliation (%)
Cherokee	29	92
Other tribe	29	8
None	43	0
Socioeconomic
Education
≥12 years (%)	100	65
Mean years (range)		11.7 (1-19)
Family income <US$10,000/year (%)^c		21
Ethnicity
American Indian Mean score (range)	0.3 (0-2.0)	1.4 (0-3)
Strong identification (%)	14	33
Mean discordance (range)		1.3 (0-3)
High discordance (%)		31
White American Mean score (range)	2.9 (2.0-3.0)	2.5 (0.5-3.0)
Strong identification (%)	100	67
Mean discordance (range)		0.5 (0-2.5)
High discordance (%)		34
Visit
Previously seen provider (%)^b
Never		4
Once		6
≥Twice		90
Waited >30min (%)^d		10

4 physicians and 3 midlevel providers.

Missing data yielded n = 114 patients.

Missing data yielded n = 86 patients.

Missing data yielded n = 113 patients.

In contrast, only 4 of 7 providers reported tribal affiliation (all Cherokee); 6 strongly identified with White American but not with American Indian ethnicity, and 1 strongly identified with American Indian but not with White ethnicity. Visits classified as highly discordant on American Indian ethnicity (31%) typically involved patients who scored higher than their providers on this measure; only 3 patients scored lower than providers, and only 16% of patients exhibited no discordance from their providers. By contrast, visits classified as highly discordant on White American ethnicity (34%) usually involved providers who scored higher than their patients. Only two patients scored higher than their providers on this measure, and 58% of patients did not exhibit any discordance from providers.

All measures of affective interaction were scored so that higher ratings implied more favorable patient evaluations. As shown in Figure 1, scores for all outcome measures were skewed toward positive evaluations. For example, roughly one half to two thirds of patients gave the highest possible scores for providers’ respect, while only 7% to 8% offered critical ratings.

Figure 1.

Patient ratings of providers’ behaviors: Distributions

Ratings nevertheless varied significantly across subgroups of patients distinguished by ethnic characteristics. Table 2 reports odds ratios from a series of random effects ordered logit models using measures of respondents’ self-rated ethnic identities as key independent variables. Each model related patients’ levels of self-identification with American Indian and White American ethnicities to scores on one of five measures of affective interaction, while adjusting for covariates. Odds ratios > 1 indicated more favorable patient evaluations while odds ratios < 1 indicated poorer ratings.

Table 2.

Random Effects Ordered Logit Models Predicting Ratings of Provider Behaviors Using Patients’ Ethnicity (N = 112).

	Provider behavior
	Respect		Empathy	Rapport
Variables	Did not talk down	Did not act as if doing a favor	Really cared	Put mind at ease	Discussed social topics
Ethnicity
Strong American Indian	0.23**	0.27**	0.91	1.37	1.20
	[0.09, 0.60]	[0.11, 0.67]	[0.36, 2.31]	[0.58, 3.28]	[0.54, 2.68]
Strong White American	1.57	2.16	2.64*	3.09**	2.60*
	[0.63, 3.90]	[0.88, 5.31]	[1.07, 6.47]	[1.32, 7.24]	[1.17, 5.78]
Covariates
Age	0.95	0.93**	1.01	1.01	1.00
	[0.91, 1.00]	[0.88, 0.97]	[0.96, 1.06]	[0.97, 1.06]	[0.96, 1.04]
Male	0.99	0.95	0.91	0.39*	0.62
	[0.42, 2.35]	[0.41, 2.18]	[0.40, 2.11]	[0.18, 0.87]	[0.30, 1.28]
Cherokee	3.68	1.08	2.28	1.73	0.77
	[0.93, 14.52]	[0.25, 4.58]	[0.58, 8.97]	[0.47, 6.40]	[0.21, 2.87]
Married	1.24	0.91	1.72	0.77	1.01
	[0.51, 2.97]	[0.39, 2.12]	[0.73, 4.07]	[0.35, 1.69]	[0.48, 2.13]
Education	0.96	1.06	0.97	0.90	0.92
	[0.83, 1.10]	[0.92, 1.22]	[0.85, 1.12]	[0.79, 1.03]	[0.81, 1.03]
2+prior visits	3.48	8.64**	2.99	3.04	1.53
	[0.86, 14.05]	[2.20, 33.95]	[0.79, 11.28]	[0.88, 10.47]	[0.51, 4.60]
Waited >30min	2.52	1.31	3.08	0.23*	1.27
	[0.52, 12.24]	[0.30, 5.78]	[0.56, 16.85]	[0.07, 0.82]	[0.34, 4.71]

Note: Coefficients presented as odds ratios, with confidence intervals in brackets.

Statistical significance is indicated as follows: *p < .05. **p < .01. ***p < .001, two-tailed tests.

Model A accounted for ethnicity as a characteristic of individual patients. Our test of Hypothesis A-1 showed significant relationships between patients’ ratings and their identification with American Indian ethnicity for both measures of providers’ respect. Specifically, odds ratios < 1 linked strong American Indian ethnic identification with reduced ratings of respect—that is, with lower chances of disagreeing that provider had “talked down” (OR = 0.23; p < .01) or “acted like he or she was doing me a favor” (OR = .27; p < .01). In contrast, no significant relationships with American Indian ethnicity emerged for empathy and rapport.

Relevant to Hypothesis A-2, significant relationships did not emerge between White American ethnicity and respect measures but did appear for all measures of empathy and rapport. The latter relationships reversed the direction of relationship for American Indian ethnicity. Patients who strongly identified with White American ethnicity had 2 to 3 times higher odds of perceiving that providers “really cared” (OR = 2.64; p < .05) and created rapport by “putting their mind at ease” (OR = 3.09; p < .01) and “discussing social topics” (OR = 2.60; p < .01). Notably, all models included variables for patient scores on both ethnicity scales. This meant that associations of each ethnicity scale with patients’ ratings occurred while controlling for the other ethnicity. Because the interaction term between the two ethnicity variables was not statistically significant in any of the models, the effect of White ethnicity did not depend on whether participants scored high or low on American Indian ethnicity and vice versa. Few covariates were statistically significant, none consistently.

Model B incorporated both patient and provider ethnicities using measures of provider–patient ethnic discordance. It yielded significant, but more pronounced, results for the same outcomes highlighted in Model A. As shown in Table 3, odds ratios < 1 associated visits characterized by high discordance on American Indian ethnicity with reduced ratings for provider respect, as compared to low-discordance visits (OR = .20-.21; p < .01). Visits highly discordant on White American ethnicity were likewise associated with reduced ratings for providers’ empathy (OR = .40; p < .05) and rapport (OR = .34-.37; p < .05).

Table 3.

Random Effects Ordered Logit Models Predicting Ratings of Provider Behaviors Using Provider–Patient Ethnic Discordance (N = 112)

	Provider behavior
	Respect	Empathy		Rapport
Variables	Did not talk down	Did not act as if doing a favor	Really cared	Put mind at ease	Discussed social topics
Ethnic discordance
High American Indian	0.20***	0.21**	0.75	1.07	1.38
	[0.08, 0.50]	[0.08, 0.54]	[0.30, 1.88]	[0.45, 2.52]	[0.62, 3.08]
High White American	0.71	0.52	0.40*	0.37*	0.34**
	[0.29, 1.77]	[0.21, 1.26]	[0.17, 0.98]	[0.16, 0.86]	[0.15, 0.75]
Covariates
Age	0.95	0.93**	1.00	1.01	1.00
	[0.91, 1.00]	[0.88. 0.97]	[0.96, 1.06]	[0.97, 1.05]	[0.96, 1.04]
Male	0.97	0.93	0.90	0.39*	0.60
	[0.41, 2.31]	[0.40, 2.14]	[0.39, 2.09]	[0.18, 0.87]	[0.29, 1.25]
Cherokee	3.72	1.10	2.44	1.84	0.79
	[0.94, 14.74]	0.26, 4.73]	[0.62, 9.64]	[0.50, 6.82]	[0.21, 2.97]
Married	1.28	0.94	1.78	0.80	1.03
	[0.53, 3.11]	[0.40, 2.21]	[0.76, 4.20]	[0.36, 1.75]	[0.49, 2.16]
Education	0.96	1.07	0.97	0.90	0.92
	[0.83, 1.11]	[0.93, 1.23]	[0.85, 1.12]	[0.80, 1.03]	[0.82, 1.04]
2+prior visits	3.42	8.88**	3.39	3.50*	1.84
	[0.84, 13.98]	[2.21, 36.65]	[0.89, 12.92]	[1.01, 12.12]	[0.60, 5.65]
Waited >30min	2.62	1.34	3.17	0.24*	1.22
	[0.53, 12.87]	[0.30, 5.98]	[0.58, 17.38]	[0.07, 0.84]	[0.33, 4.54]

Note: Coefficients presented as odds ratios, with confidence intervals in brackets.

Statistical significance is indicated as follows: *p < .05. ** p < .01. ***p < .001, two-tailed tests.

Discussion

The theoretical model of orthogonal ethnic identity proposes the relevance of both minority and majority ethnicity for social interaction (Moran et al., 1999; Oetting & Beauvais, 1990-1991). An emerging theory relevant specifically to medical interaction argues that patients possess varying amounts of cultural health capital or CHC, “a specialized form of cultural capital that can be leveraged in health care contexts to effectively engage with medical providers” (Shim, 2010, p. 3). Following a direction suggested by Malat (2006), this theory encourages researchers to examine how patients’ social attributes, including both ethnicity and age, supply them with varying access to symbolic and instrumental resources in medical interaction. Our analysis combined insights from both theories in empirical analyses that related older American Indian patients’ ethnic characteristics to their ratings of providers’ affective behaviors while operationalizing the complexity of ethnic identity in two ways: first as an attribute of individual patients and second as a feature of social relationships, especially differences between the ethnic characteristics of both providers and patients.

Effects of American Indian Ethnicity: Hypotheses A-1 and B-1

As expected from the literature on medical interaction, at least some findings were consistent with Hypothesis A-1 that patients’ stronger American Indian ethnicity would be associated with reduced evaluations of providers’ affective interactions: for patients who placed themselves high on this scale, odds of favorable ratings on both measures of providers’ respect diminished by about 75% as compared to patients who rated themselves low. A similar pattern emerged for Hypothesis B-1, which proposed that high provider–patient ethnic discordance on American Indian identity would likewise be associated with poorer evaluations: patients in high-discordance visits had about 80% lower odds of assigning high ratings for providers’ respect, as compared to patients in visits pairing them with a provider whose American Indian ethnicity scores were similar to theirs.

These associations between reduced ratings of providers’ respect and American Indian ethnicity—whether measured by level of identification characterizing each patient or by level of provider–patient discordance—reflect the most prominent theme in the literature on minority experiences with health care. They are also consistent with the interpretation that access to resources summarized in the concept of cultural health capital varies with ethnicity.

Our measures cannot reveal specific instrumental or symbolic exchanges that may have contributed to lower ratings among patients who strongly identified with American Indian ethnicity. Prior research has suggested, however, that difficulties in communicating respect with American Indians in medical interaction can be attributed to differing norms regarding nonverbal communication. Researchers have urged medical practitioners to familiarize themselves with distinctive tribal cultural values related, for instance, to eye contact (Cattarinich, Gibson, & Cave, 2001; Hendrix, 2003), interruptive patterns, speech speed and volume, and use of silence (Kelly & Brown, 2002; Satter, Veiga-Ermert, Burhansstipanov, Ches, & Restivo, 2005; Towle et al., 2006). Others have encouraged providers to seek knowledge about tribal culture, history, and values as a foundation for respectful relationships (Dixon & Iron, 2006; Reid & Rhoades, 2000; Towle et al., 2006; Wittig, 2004). Yet another especially comprehensive publication recommended an “indirect” style of provider communication that draws on stories, examples, and metaphors when treating patients who strongly identify with American Indian ethnicity (Kalbfleisch, 2009, p. 161; cf. Long Feather, 2007).

Effects of White American Ethnicity: Hypotheses A-2 and B-2

Hypothesis A-2, which focused on patients’ White American ethnicity, found it unrelated to respect measures but significantly associated with all measures of empathy and rapport. Patients with high scores on the White American scale had two to three times larger odds of giving the highest rating for these provider behaviors as compared to patients who scored low. Similarly, examination of Hypothesis B-2, which used measures of provider–patient White American ethnic discordance, showed that high-discordance visits had odds of high ratings for empathy and rapport that diminished by 60% to 66% as compared to low-discordance visits.

Higher ratings of empathy and rapport among patients who identified strongly with White American ethnicity, combined with lower ratings among patients who visited providers most different from themselves on that measure, are generally consistent with cultural health capital theory. This theory portrays medicine as dominated by a historically specific “cultural logic about how patients should approach health care and what their duties as patients are”; patients who can fit their behaviors to this logic enjoy greater facility in medical interaction (Shim, 2010, p. 6). Given our finding that the quality of specific types of interactions—at least as perceived by patients themselves—varies according to their level of identification with White American ethnicity, our results are consistent with the conclusion that this cultural logic is tied to that White American ethnicity.

Clinical and Theoretical Implications

These findings highlight the importance of recommendations, drawn from the mainly qualitative literature, for practically adapting medical interviewing for American Indians, and they carry implications for clinical practice. In particular, our results suggest that providers serving patients who strongly identify with American Indian ethnicity should remain alert to behaviors that patients may interpret as condescension. This advice may be especially relevant for providers who do not similarly identify. Our finding that only one third of sampled patients strongly identified with American Indian ethnicity raises the possibility that such persons may avoid conventional medical care; perhaps refinements in providers’ affective interaction might encourage service utilization in this population.

Our findings further recommend that providers treating patients who differ considerably from them on White American ethnicity may wish to take special care to convey empathy and establish rapport. A recent monograph examining cultural competence training at sites across Indian Country, including the Cherokee Nation, reported advice from personal interviews with tribal respondents about communicating appropriate affective cues to American Indian patients, especially elders; the tribal respondents urged providers to avoid pressing for personal information such as income and discourage “push[ing] people to share hurtful things about their family and personal life” (Dixon & Iron, 2006, p. 121).

The relevance of patients’ identifications with both American Indian and White American ethnicity to clinical care can justifiably encourage providers to seek such information from patients through social conversation—and to consider sharing similar information about themselves. While it is not out of the question that clinics might collect information on ethnic identity as part of patient records by employing items from the instrument used in this analysis, a more personal approach might best enhance interaction; this seems especially likely in view of research suggesting that “small talk” elevates perceptions of rapport (Fifer, 1996; Kelly & Brown, 2002).

Our results also assist in interpreting the literature on provider–patient concordance in medical interaction. Such work has typically compared outcomes of interactions between patients and providers of the same or different race, sometimes finding that patients are most satisfied with care received from same-race providers (e.g., Saha, Komaromy, Koepsell, & Bindman, 1999). Research on racial concordance, however, has proven inconsistent; significant associations with patient outcomes are absent in some studies, while some studies have even found patients less satisfied in racially concordant medical visits (e.g., Blanchard et al., 2007; Lyles et al., 2011).The current analysis, and particularly its grounding in CHC theory, suggests a reason for such variant outcomes. While previous research on provider–patient concordance has focused almost exclusively on provider and patient race as operationalized by a single, check-one-box question, our analysis examines ethnic concordance; findings suggest that providers’ and patients’ similar cultural characteristics may matter as much or more than their racial characteristics. Indeed, our findings raise the distinct possibility that racial concordance operates as an unreliable proxy for the ethnic (cultural) concordance that our study measured directly. These findings recommend that future work aimed at identifying, explaining, and improving the dynamics of affective and other interactions with vulnerable patient populations should not only examine measures of race but should also include measures of cultural characteristics.

Our models accounted, first, for ethnicity as a property of individuals (Table 2) and, second, for ethnic discordance as a property of provider–patient relationships (Table 3). Comparison of these models suggests an idea that CHC theory has not previously developed: the possibility that ethnic discordance may be a pathway by which patients’ ethnicity becomes important to care. This interpretation would imply that American Indian ethnicity is associated with poorer patient ratings while White American ethnicity is associated with elevated ratings for certain affective behaviors largely because of the differential risks of an ethnically discordant visit. Because sampled providers rarely reported strong American Indian ethnicity, while all providers reported strong White American ethnicity, patients whose ethnicity departed from these patterns faced a greater risk of ethnically discordant visits and the interactional disadvantages these may imply. Prior research on racial discordance has led to recommendations that medical schools and practices work to create diversity in the medical workforce so that minority patients enjoy greater opportunities to have same-race providers. By contrast, our findings suggest a need to also diversify the medical workforce in terms of cultural characteristics.

Limitations and Directions for Future Research

This study has limitations. First, while our results were partially consistent with hypotheses, they did not correspond perfectly, and the varying associations of specific ethnicities with only some outcomes raise questions. Why was American Indian ethnicity associated only with patients’ ratings of respect, while White American ethnicity was associated only with perceptions of empathy and rapport? While CHC theory provides no guidance in interpreting such specific outcomes, one might speculate that respect is an exceptionally salient value for patients strongly identified with tribal cultures, heightening their sensitivity to its indicators. Such dynamics might carry special resonance for older adults, who occupy a status that cultural conventions often accord special regard (Robertson, 2009). One might further speculate that empathy and rapport carry similar resonance in the larger American culture, explaining their relationship to measures of White ethnicity. It is alternatively possible that general labels such as “respect” function as umbrella terms by which respondents reference many culturally rooted norms and behaviors that researchers do not yet understand. Choice between such interpretations requires further inquiry.

Next, our findings raise the possibility that visits with American Indian patients may constitute one example in which cross-cultural medical interactions may go astray for reasons suggested in CHC theory. Nevertheless, readers should understand that the specific dynamics of interaction and interpretation may not characterize other populations. Given known issues confronting both older adults and ethnic minority patients, we focused exclusively on a sample drawn from this subpopulation. Sampling was further limited to patients receiving services at a single, rural clinic operated by one American Indian tribe. Efforts to generalize findings to other groups—different age cohorts, other ethnic populations, or even to urban Indian populations—must proceed with caution.

The 27% refusal rate raises the possibility that nonparticipants differed significantly from participants. Our inclusion of covariates used in previous research reduces the possibility that observed relationships were influenced by unmeasured variables. Yet there may be other factors—perhaps level of health literacy (Robertson, 2009)—that were related to ethnicity and to patients’ evaluations.

Given the few providers in our sample, all our analyses are patient-level rather than provider-level, including those focusing on patient–provider discordance. While the random effects models that we use controlled for effects of systematic variation associated with any provider characteristic, they do not allow direct examination of how providers’ individual characteristics may become relevant to interaction outcomes. Future inquiries examining the effects of provider characteristics such as training in cultural competency or length of time working with Native populations are recommended.

An additional caveat is that average patient evaluations on all dimensions of providers’ affective behavior were high. In this, our results resemble findings from many studies of medical satisfaction in the general population (e.g., Coyle & Williams, 2000; Roter & Hall, 2006; Williams, Coyle, & Healy, 1998). Previous research, however, shows that even small differences in patient perceptual measures can be quite meaningful in their relationship to health outcomes and should be taken seriously (Doescher, Saver, Franks, & Fiscella, 2000; Hall & Dornan, 1990). Nevertheless, we also acknowledge that this finding of highly positive evaluations contrasts with qualitative studies in American Indian patients, all of which have documented considerable concerns for affective aspects of medical interaction. Our high average patient ratings may reflect the special skill of providers at the clinic studied, or they may be a function of study design. Whereas qualitative studies usually inquire about health care experiences over an extended period or a lifetime, our project asked about “today’s visit” and should have elicited fewer concerns. Nevertheless, our high average ratings imply that findings may not suggest strategies for remediating situations of serious patient discontent. They are better interpreted as guidance for achieving highest patient satisfaction.

Our estimates were further limited in that measures of ethnic discordance in our sample did not reflect the full range of possible cultural combinations. Instead, they nearly always reflected visits wherein patients overscored providers on American Indian ethnicity and underscored them on White American ethnicity. These patterns of discordance, however, typify the experience of American Indian patients generally (Dixon & Iron, 2006, p. 2), such that our sample represents common experience. At the same time, it is possible that the degree of discordance not only with individual providers but also with the entire health care system played a role in patients’ experiences of care (e.g., Robertson, 2009). Yet, as orthogonal ethnic identification theory reminds us, some providers who strongly identify with the White American culture may embrace other ethnic cultures, too. For minority patients, having a culturally similar provider may dampen negative effects of the system as a whole. Future research might explore whether cultural capital theory can illuminate not only patients’ relationships with individual providers, but also relationships with health care systems, and consider implications for care delivery.

We examined the ratings of affective interaction using five separate models. Although this analytic approach did not allow us to observe correlations among outcomes, it is possible that some outcomes were related to each other. For example, some models of provider–patient interaction suggest that providers’ exercise of empathic skills is a means to rapport (Norfolk, Birdi, & Walsh, 2007). Our focus on areas of concern previously highlighted in the qualitative literature, along with the consistent direction of all observed relationships, increases confidence in the validity of our findings. Nevertheless, they must be replicated in other samples. Also, we measured only identification with American Indian and White American ethnicities. Although these adequately reflected the range of ethnic identities at our research site, other primary care settings may involve providers with still other ethnic backgrounds, including international medical graduates. Our results allow no conclusions about patients’ perceptions of affective interaction when providers identify with neither White nor American Indian identities. Finally, because we examined only patients’ evaluations of interactions rather than actual quality of care, readers should not conclude that reduced ratings reflect providers’ differential treatment; they might reflect patients’ culturally variant patterns of interpreting affective cues.

Conclusion

Older ethnic minority patients occupy an intersection of vulnerable statuses, both of which leave them at high risk for disrupted medical interactions. Consistent with CHC theory’s argument for the differential distribution of resources for negotiating medical encounters, our analyses associated older American Indian patients’ ethnic characteristics with ratings of providers’ affective behaviors. Moreover, our efforts to approach CHC theory through current models of ethnic identity extend this theory in two ways. First, complementing mainly qualitative work on medical interaction with American Indians, our results suggested that patients’ experience of medical interaction were linked to identification with both majority and minority cultures. Second, our results emphasized the importance of providers’ cultural characteristics. They suggested a specific pathway by which ethnicity may become important to care by demonstrating that provider–patient ethnic discordance on both American Indian and White American ethnicity was important to patients’ experience of affective interactions.

Footnotes

Authors’ Note

The authors gratefully acknowledge the guidance of the Cherokee Nation IRB, especially Sohail Khan; the research assistance of David Scott, Amanda Bighorse Dominick, Onial Garroutte and Patricia Garroutte; and the technical advice of Drs. Spero Manson, Dedra Buchwald, and Jack Goldberg. The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the NIH, the Cherokee Nation, or Boston College.

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 disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Data collection was funded by a grant under the Resource Centers for Minority Aging Research program sponsored by the National Institute on Aging (S. Manson, P30AG/15297); the Agency for Healthcare Research and Quality (S. Manson, grant number P01 HS10854), and the National Center for Minority Health and Health Disparities [S. Manson, grant number P60MD000507]; and by a research expense grant from Boston College. Data analysis was supported by the National Institute on Aging [E. Garroutte, grant number 1K01 AG022434-01A2].

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