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Journal of Counseling Psychology Racial/Ethnic Disparities in Therapist Effectiveness: A Conceptualization and Initial Study of Cultural Competence Zac E. Imel, Scott Baldwin, David C. Atkins, Jesse Owen, Tim Baardseth, and Bruce E. Wampold Online First Publication, May 2, 2011. doi: 10.1037/a0023284

CITATION Imel, Z. E., Baldwin, S., Atkins, D. C., Owen, J., Baardseth, T., & Wampold, B. E. (2011, May 2). Racial/Ethnic Disparities in Therapist Effectiveness: A Conceptualization and Initial Study of Cultural Competence. Journal of Counseling Psychology. Advance online publication. doi: 10.1037/a0023284

Journal of Counseling Psychology 2011, Vol. ●●, No. ●, 000 – 000

© 2011 American Psychological Association 0022-0167/11/$12.00 DOI: 10.1037/a0023284

Racial/Ethnic Disparities in Therapist Effectiveness: A Conceptualization and Initial Study of Cultural Competence Zac E. Imel

Scott Baldwin

University of Wisconsin—Madison

Brigham Young University

David C. Atkins

Jesse Owen

University of Washington

University of Louisville

Tim Baardseth and Bruce E. Wampold University of Wisconsin—Madison As a result of mental health disparities between White and racial/ethnic minority clients, researchers have argued that some therapists may be generally competent to provide effective services but lack cultural competence. This distinction assumes that client racial/ethnic background is a source of variability in therapist effectiveness. However, there have been no direct tests of the therapist as a source of health disparities. We provided an initial test of the distinction between general and cultural competence by examining client racial/ethnic background as a source of variability in therapist effectiveness. We analyzed cannabis use outcomes from a psychotherapy trial (N ⫽ 582) for adolescent cannabis abuse and dependence using Bayesian multilevel models for count outcomes. We first tested whether therapists differed in their effectiveness and then tested whether disparities in treatment outcomes varied across therapist caseloads. Results suggested that therapists differed in their effectiveness in general and that effectiveness varied according to client racial/ethnic background. Therapist effectiveness may depend partially on client racial/ethnic minority background, providing evidence that it is valid to distinguish between general and cultural competence. Keywords: cultural competence, health disparities, multilevel modeling, therapist effects

order to decrease health disparities (Beach et al., 2006; Brach & Fraser, 2000). Several researchers have suggested that culturally competent treatment is an ethical imperative (see Arredondo & Toporek, 2004; Ridley, 1985). There have also been several calls and professional mandates for attention to cultural competence in applied psychology (American Psychological Association, 2003, 2006; Arredondo, Toporek, Brown, & Jones, 1996; D. W. Sue, 1982; D. W. Sue, Arredondo, & McDavis, 1992; D. W. Sue et al., 1998). Cultural competence is typically distinguished from a therapist’s general competence to provide quality care and is operationalized as the therapist’s effectiveness in treating racial/ethnic minority clients (American Psychological Association, 2003; Kumas¸-Tan, Beagan, Loppie, MacLeod, & Frank, 2007; D. W. Sue, 1982; S. Sue, Zane, Nagayama Hall, & Berger, 2009). D. W. Sue (1982) provided the tripartite framework of cultural competence, in which it is defined as the therapist integrating the necessary awareness of his or her own and the client’s cultural values and attitudes, knowledge about diverse groups of people, and skills necessary to provide adequate services to culturally diverse clients. Fischer, Jome, and Atkinson’s (1998) common factors model of cultural competence emphasized (a) the therapeutic relationship, (b) a shared worldview between clients and counselors, (c) clients’ expectations, and (d) culturally appropriate interventions that are acceptable to both the counselor and client. Constantine and Lada-

Disparities in mental health care for racial/ethnic minority clients are well documented (Harris, Edlund, & Larson, 2005; Institute of Medicine, 2002; Kales, Blow, Bingham, Copeland, & Mellow, 2000; U.S. Surgeon General, 2001). However, relatively little is known regarding why racial/ethnic minority clients receive poor quality of care relative to White clients. Researchers, educators, and administrators have identified therapists as a potential source of variability (van Ryn, 2002; van Ryn & Fu, 2003) and have advocated for increasing therapists’ cultural competence in

Zac E. Imel, Tim Baardseth, and Bruce E. Wampold, Department of Counseling Psychology, University of Wisconsin—Madison; Scott Baldwin, Department of Psychology, Brigham Young University; David C. Atkins, Department of Psychiatry and Behavioral Sciences, University of Washington; Jesse Owen, Department of Counseling Psychology, University of Louisville. Portions of this research were presented at the annual meeting of the Society for Psychotherapy Research, Barcelona, Spain, in 2008. We would like to thank Gregory E. Simon for his helpful comments on an earlier version of this article. Correspondence concerning this article should be addressed to Zac E. Imel, who is now at S-116-MHC, Veterans Affairs Puget Sound Health Care System—Seattle Division, 1660 South Columbian Way, Seattle, WA 98116. E-mail: [email protected] 1

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ny’s (2001) model included (a) counselor self-awareness, (b) knowledge about multicultural issues, (c) understanding unique client variables, and (d) general multicultural counseling skills. More broadly, Lo´pez (1997) argued that cultural competence includes “the ability of the therapist to move between two cultural perspectives in understanding the culturally based meaning of clients from diverse cultural backgrounds” (p. 573). The validity of distinguishing between general and cultural competence has not been clearly established. A qualitative study of 135 primary care patients’ preferences for culturally sensitive health care revealed several differences among preferences across African American, Latino American, and European American patients, including characteristics of the physical environment (e.g., art) and behaviors of check-in staff. However, all patients preferred providers who had interpersonal skills, were technically competent, and provided individualized treatment (Tucker et al., 2003). Clinical and analogue studies of therapists demonstrated that correlations among ratings of cultural and general competence ranged from .50 to .80, suggesting substantial overlap among the two constructs (e.g., Coleman, 1998; Constantine, 2007). For example, in an analogue study, Coleman (1998) found that counselors who were trained to present as “culturally neutral” were

rated less culturally and generally competent. Moreover, ratings of both cultural competence and general competence have been found to predict clients’ satisfaction with services (Constantine, 2007). However, client ratings of cultural competence predicted client satisfaction with treatment above and beyond client ratings of general competence (Constantine, 2002). Researchers have not investigated differences between therapists in clinical outcomes with racial/ethnic minority and White clients, which is an essential issue when examining cultural competence (S. Sue et al., 2009; van Ryn & Fu, 2003; Worthington, Soth-McNett, & Moreno, 2007). The distinction between general and cultural competence can be defined in terms of differential effectiveness of therapists. Some therapists may be relatively less effective with racial/ethnic minority clients than they are with White clients, whereas other therapists may be more effective with racial/ethnic minority clients than with White clients. The four panels in Figure 1 illustrate how disparities in clinical outcomes might differ within and between therapist caseloads, assuming cannabis use as the outcome. We assume that some therapists consistently achieve better outcomes than others, an assumption supported by substantial evidence (e.g., CritsChristoph & Mintz, 1991; Kim, Wampold, & Bolt, 2006; Najavits

Figure 1. Four potential patterns of therapist variability with White and racial/ethnic minority clients. Each set of bars represents one therapist. Therapist effectiveness with White clients is shown in dark gray, and effectiveness with racial/ethnic minority clients is shown in light gray.

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& Weiss, 1994). In Panel 1, there is variability among therapists in terms of outcome, but all therapists are equally effective with White and racial/ethnic minority clients. In this scenario, there are no racial/ethnic disparities, as White and minority clients achieve equal outcomes within and between therapists. In Panel 3, there is a racial/ethnic disparity (racial/ethnic minority clients have poorer outcomes—i.e., more days of cannabis use), but the disparity is constant across therapists. That is, the racial/ethnic disparity is not due to the fact that some therapists demonstrated smaller or larger differences in outcomes between racial/ethnic minority and White clients. In Panels 2 and 4, there is variability across therapist caseloads in the difference in White and minority clients’ clinical outcomes. In Panel 2, racial/ethnic minority and White clients achieve equal outcomes overall, but some therapists achieve better outcomes with racial/ethnic minority clients than with White clients, whereas other therapists show the opposite pattern. In Panel 4, White clients generally achieve better outcomes, but some therapists are relatively more effective with minority clients than with White clients. A test of distinguishing between cultural and general therapist competence is found by comparing the left and right panels—in the right-hand panels, some therapists demonstrate greater differences in outcomes between White and racial/ethnic minority clients than do other therapists. Note that an overall mean difference between White and racial/ethnic minority clients is not required to establish therapist variability related to client racial/ethnic minority status (compare Panel 1 with Panel 2). Specifically, if some therapists with good outcomes demonstrate equivalent results across White and racial/ethnic minority clients (see Therapist 1, Panel 4), and other therapists demonstrate good outcomes with White clients but poorer outcomes with racial/ethnic minority clients (or vice versa; see Therapist 1, Panel 2), this would be consistent with distinguishing between cultural and general competence. In contrast, if therapists vary in their outcomes but disparities between White and racial/ethnic minority clients are equally distributed across therapists, then the effects of the therapists are more consistent with the notion of general competence than cultural competence (see Panels 3 and 4). In regards to Panel 3, this would point to other sources of disparities that do not vary across therapists (e.g., contextual/site factors, access, and cultural appropriateness of specific treatments offered in a clinic). The aim of the current study was to test the theorized distinction between cultural and general therapist competence by examining variability in therapist outcomes with their clients. We used data from the Cannabis Youth Treatment (CYT) Study—a large multisite psychotherapy trial with a significant and relatively balanced racial/ethnic minority representation (Dennis et al., 2002)—to test two hypotheses regarding therapist differences in a primary study outcome: days of cannabis use. Specifically, we used multilevel modeling techniques to distinguish between therapist general competence (viz., did some therapists achieve better outcomes than others) and cultural competence (did differences between White and racial/ethnic minority clients vary across therapists). First, we tested the hypothesis that therapists would vary in the days of cannabis use their clients reported regardless of clients’ racial/ ethnic minority status (Hypothesis 1). In a second model, we allowed the effect of client racial/ethnic minority status on cannabis use to vary across therapists, which allows a statistical test of the hypothesis that disparities between racial/ethnic minority and

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White clients in days of cannabis use vary across therapists (Hypothesis 2). To our knowledge, researchers have not yet extended the use of multilevel models to test this key assumption of the cultural competence construct—that differences in outcomes among White and racial/ethnic minority clients vary across therapists.

Method Study Design The CYT Study is a large multisite psychotherapy trial with a significant and relatively balanced racial/ethnic minority representation (Dennis et al., 2002). The CYT Study compared the relative effectiveness and cost-effectiveness of five treatments in general practice conditions. The target population was adolescents with cannabis-related disorders who would be appropriate for outpatient treatment. The study consisted of two treatment trials in which 600 adolescents were recruited over a 2-year period and randomized to one of five treatments from sequential admissions at four treatment sites. Treatments included motivational enhancement therapy (MET) combined with group-based cognitive behavioral therapy (CBT; two sessions of MET then followed by either three or 10 sessions of group CBT), family support network plus MET/CBT (12 sessions), adolescent community reinforcement approach (14 sessions), or multidimensional family therapy (12 sessions). Primary analyses did not reveal evidence of differences in efficacy across treatment conditions, and thus we did not include treatment modality in our analyses. Previous reports have provided extensive descriptions of the participants, design, procedure, and treatments effects in the CYT Study (Dennis et al., 2002, 2004). To be included in the CYT Study, clients were required to be 12 to 18 years old, report one or more cannabis abuse or dependence criteria (according to the 4th ed. Diagnostic and Statistical Manual of Mental Disorders; American Psychiatric Association, 1994) in the past 90 days, and speak English sufficiently to participate in treatment. The present analyses focused on 582 clients who completed treatment and provided both pre- and posttreatment scores and the 13 therapists who treated at least one White and one racial/ethnic minority client. The sample was 82% male and 18% female. The mean age was 15.75 years (SD ⫽ 1.22; range: 13–18), and the sample was 62% White (n ⫽ 359) and 38% racial/ethnic minority (n ⫽ 223). Specifically, 29% (n ⫽ 170) of the sample was African American, and the remaining racial/ethnic minority clients identified as Hispanic/Latino (4%, n ⫽ 21), other (5%, n ⫽ 30), and Asian (⬍1%, n ⫽ 2). Given the low representation of specific racial/ethnic minority groups, we dichotomized the racial/ethnic minority variable and thus estimated differences between White and racial/ethnic minority clients. There were baseline differences between racial/ethnic minority and White clients in baseline cannabis use and age (see Table 1). Therapists had a range of educational backgrounds (20% doctorate, 50% master’s and 30% bachelor’s degrees). Years of experience ranged from under 1 year to 23 years, with a mean of 7 years of experience doing substance abuse counseling and work with adolescents. The mean caseload of a therapist was approximately 44.77 clients (SD ⫽ 20.01). Of the 13 therapists, 10 self-identified as White, two as African American, and one as

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4 Table 1 Baseline Characteristics of Clients Variable

White (n ⫽ 359)

Age in years Gender (male) Days of cannabis use (past 90 days)

15.65 (SD ⫽ 1.19) 81.3% (n ⫽ 292) 34.02 (SD ⫽ 28.14)

Note.

Racial/ethnic minority (n ⫽ 223) 15.91 (SD ⫽ 1.24) 84.3% (n ⫽ 188) 39.3 (SD ⫽ 29.3)

t/␹2

p

2.54 0.84 2.16

.01 .21 .03

Baseline characteristics of White and racial/ethnic minority clients.

Latin American. Therapists were predominately female (72%) and their ages ranged from 24 to 55 years (M ⫽ 37 years). Therapists received training in the interventions they were to provide. After training, session tapes were reviewed by a clinical coordinator until the therapist was certified as proficient in the intervention. Therapists also received weekly supervision throughout the study that included tape review and adherence monitoring in order to provide therapists with feedback and prevent drift. We are not aware of any information regarding therapist completion of cultural competence trainings (Dennis et al., 2002, 2004).

Measure: Days of Cannabis Use In the current analysis we focused on a primary outcome measure, days of cannabis use in last 90 days, prior to treatment, and at posttreatment (3 months after baseline; Dennis et al., 2002). Substance use data were obtained from semistructured interviews conducted by research staff with the Global Assessment of Individual Needs (GAIN; Dennis, 1999).

Statistical Analyses We used Bayesian multilevel models to test whether differences between minority and White clients varied by therapist. Multilevel models are appropriate when data are clustered (clients within therapists) and are useful when the research question focuses on variability among the clusters (e.g., therapists; Raudenbush & Bryk, 2002). Because days of cannabis use is a highly skewed count variable, we fit a multilevel overdispersed Poisson model (see Atkins & Gallop, 2007, for an overview of count regression methods and Gelman & Hill, 2007, for Bayesian applications to multilevel data). Maximum likelihood is typically used to estimate the parameters in multilevel models. However, maximum likelihood can encounter difficulties when the number of clusters (i.e., therapists) is small, as was the case in the current study (13 therapists), and with nonnormal outcomes (e.g., count data), as is typical in substance abuse studies. In contrast, Bayesian methods, which use prior distributions for all parameters and simulationbased Markov chain Monte Carlo estimation methods, can provide accurate variance estimates even when the number of clusters is small (Spiegelhalter, Best, Carlin, & van der Linde, 2002). Noninformative prior distributions are often used in Bayesian analyses. Noninformative priors provide weak constraints on permissible values for fixed and random effects but do not strongly influence the substantive results. An inverse-Wishart prior distribution was used for the therapist and residual variance components, and a multivariate normal distribution was used for other effects. Three separate iteration chains (i.e., simulations) were run for each model, and convergence was assessed via plots of autocorrelation

and trace plots, as well as the Gelman-Rubin statistic that compares between- to within-chain variance (Gelman & Hill, 2007). We used the mode and the 95% credible interval (CI) of the simulated posterior distribution to estimate fixed and random effects, as is standard in Bayesian models. The deviance information criterion (DIC) was also used to compare models (where smaller values indicate better fitting models). All models were fit using the MCMCglmm package in R (Version 2.11.1; Hadfield, 2009; R Core Development Team, 2009). We estimated two models predicting days of cannabis use. In both models, the expected days of cannabis use at posttreatment (␭ij) was modeled using a Poisson probability distribution and log link: ␩ ij ⫽ log 共␭ ij兲, where ␩ ij is the expected log count for cannabis use for client i treated by therapist j. The log count was modeled as a function of racial/ethnic minority status (0 ⫽ White; 1 ⫽ racial/ethnic minority [R/EM]), baseline cannabis use (BCS), age (grand-mean centered), and gender (0 ⫽ girls; 1 ⫽ boys). The Poisson model assumes that the mean and the variance of the count variable are identical. The variance of the count variable in our sample exceeded the mean. Consequently, we included an overdispersion parameter that allowed the variance to exceed the mean. The Level 1 model was: ␩ ij ⫽ ␤ 0j ⫹ ␤ 1jR/EM ⫹ ␤ 2jBCS ⫹ ␤ 3jAGE ⫹ ␤ 4jGENDER ⫹ e ij. Additionally, we included a random effect for therapist (i.e., a random intercept; u0j), which allowed posttreatment cannabis use, conditional on the covariates, to vary across therapists. Thus, the Level 2 model was: ␤ 0j ⫽ ␥ 00 ⫹ u0j ␤ 1j ⫽ ␥ 10 ␤ 2j ⫽ ␥ 20 ␤ 3j ⫽ ␥ 30 ␤ 4j ⫽ ␥ 40 . The random intercept (u0j) is assumed to be normally distributed 2 with a mean of 0 and a variance component of ␴ therapist . The 2 variance component for therapist (␴ therapist ) is interpreted as the variability across therapists in log count for cannabis use at posttreatment, controlling for the covariates in the model.

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Model 2 was identical to Model 1 except we included a second random effect that allowed the relationship between minority status and outcome to vary across therapists (i.e., a random slope; u1j). Thus, the Level 2 equation for ␤ 1j is: ␤ 1j ⫽ ␥ 10 ⫹ u1j. The random slope (u1j) is assumed to be normally distributed with 2 a mean of 0 and a variance component of ␴ R/EM . The variance component for racial/ethnic minority status indexed how much the difference in outcomes between racial/ethnic minority and White clients varied across therapists. We also allowed the random slopes to correlate with the random intercepts.

Results Table 2 presents the results of the multilevel models. In Models 1 and 2, client race, gender, and age were not significant predictors of treatment outcome. The posterior mode for the fixed effect for race/ethnic minority status was ␥ 10 ⫽ 0.09, 95% CI [– 0.28, 0.42], indicating that on average the difference in outcome between racial/ethnic minority clients and White clients was not reliably different from zero. Only pretreatment cannabis use was associated with posttreatment outcome. In Poisson regression a natural log link function is used to connect the predictors to the outcome. Consequently, transforming the fixed effects aids interpretation. The value 共e␥ 20 ⫺ 1兲 ⫻ 100 provides the percent change in posttreatment cannabis use associated with a 1-day change in pretreatment use (where ␥ 20 is the pretreatment coefficient). Use decreased from pre- to posttreatment. However, greater pretreatment use was associated with greater use at posttreatment. Models 1 and 2 suggest that each additional day of cannabis use at pretreatment is associated with a 2% increase in the number of days using cannabis posttreatment, holding other variables constant. The mode of the posterior distribution for the variance component among the random intercepts for the therapist in Model 1 was 2 ␴ therapist ⫽ 0.16, 95% CI [0.09, 0.58]. This indicates that clients’ posttreatment cannabis use varied across therapists and established therapist variability in outcomes.

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Model 2 included a random slope for racial/ethnic minority status to test whether disparities in outcome varied across therapists. The inclusion of a random slope improved overall fit of the model slightly (Model 1 DIC ⫽ 3,265.22; Model 2 DIC ⫽ 3,263.5). The variance of racial differences across therapists had a 2 posterior mode of ␴ R/EM ⫽ 0.27, 95% CI [0.15, 1.05], indicating that racial differences in outcome varied across therapists. The correlation between the random intercept and slope had a posterior mode of r ⫽ –.16, 95% CI [–.67, .49], indicating that therapists’ effectiveness with White clients had little relationship with differences between White and racial/ethnic minority clients within their caseload. Specifically, the most effective therapists with White clients did not necessarily demonstrate equal outcomes with racial/ ethnic minority clients. Figure 2 presents the predicted days of cannabis use for clients at the average of baseline use and age for each of 13 therapists in the study ranked according to how they performed with minority clients. Consistent with the notion of cultural competence, differences between White and racial/ethnic minority clients varied across therapists, with some therapists having similar outcomes for minority and White clients, some having better outcomes with White than minority clients (i.e., Therapist 4), and some having better outcomes with minority clients than White clients (i.e., Therapist 13). In addition to providing information about the 13 specific therapists in the study, we can use the estimate of therapist variance to consider the distribution of (similarly selected) therapists in the population. With respect to differences among racial/ethnic minority and White clients, this distribution has a mean equal to the fixed effect for client race/ethnicity and a variance equal to the variance component for race/ethnicity. Therapists above the mean have a larger expected difference between White and racial/ethnic minority clients than do therapists below the mean. To illustrate the importance of this variability, we computed the expected percentage change in outcome when a therapist sees a racial/ethnic minority client compared with a White client for therapists at the 5th, 25th, 50th, 75th, and 95th percentiles of the distribution of slopes (see Figure 3). Positive percentage change indicates that therapist performance will be poorer with racial/ethnic minority clients than

Table 2 Bayesian Multilevel Overdispersed Poisson Model Predicting Cannabis Use Model 1 Variable Fixed effects Intercept Pretreatment cannabis use Age Gender Race Random parameters Intercept Race Overdispersion DIC

Model 2

Coefficient

95% CI

Coefficient

95% CI

1.88 0.02 0.11 0.24 0.09

[1.42, 2.36] [0.02, 0.03] [⫺0.01, 0.24] [⫺0.22, 0.58] [⫺0.28, 0.42]

1.82 0.02 0.12 0.23 0.09

[1.36, 2.41] [0.02, 0.03] [⫺0.01, 0.24] [⫺0.23, 0.58] [⫺0.44, 0.59]

0.16

[0.09, 0.58]

0.27 0.28 2.98

[0.14, 0.90] [0.15, 1.05] [2.57, 3.52]

2.98

[2.53, 3.47] 3,265.22

3,263.50

Note. Coefficients are the mode of the posterior distribution. 95% CI ⫽ 95% credible interval; DIC ⫽ deviance information criterion.

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Figure 2. Variability across therapists in differences between racial/ethnic minority and White clients in percentage of days of cannabis for each therapist after controlling for age, gender, and baseline days of cannabis use (residualized gain scores).

White clients. Negative percentage change indicates the opposite. An advantage of the Bayesian model is that the uncertainty in the mean and variance of this distribution is taken into account when computing the expected percent change, and thus the 95% CIs are quite accurate (Gelman & Hill, 2007). Figure 3 indicates that therapists in the 5th percentile and lower are expected to consistently have better outcomes with racial/ethnic minority clients than White clients, whereas therapists in the 75th percentile and higher are expected to consistently have worse outcomes with racial/ ethnic minority clients than White clients. The middle 50% of therapists would have roughly equivalent outcomes with racial/ ethnic minority and White clients. For example, the outcomes of racial/ethnic minority clients of a therapist in the 95th percentile would be expected to be almost 200% worse than those of that therapist’s White clients.

Discussion Similar to other studies (e.g., Crits-Christoph & Mintz, 1991; Kim et al., 2006), our analyses provided evidence of differential effectiveness of therapists. That is, after controlling for initial severity and demographic covariates, some therapists consistently

attained better outcomes with their clients than did others. However, there was also evidence to suggest that therapist effectiveness varied across White and racial/ethnic minority clients within their caseload. Some therapists had superior outcomes with White clients, whereas others had superior outcomes with racial/ethnic minority clients. Accordingly, results of the multilevel models are consistent with the distinction of general and cultural competence. That is, some therapists are more effective than others, and some therapists are also relatively more effective with racial/ethnic minority clients. Averaging across all therapists, differences in posttreatment cannabis use between White and racial/ethnic minority clients were negligible. Thus, using the standard definition of health disparities, it could be said that there were no disparities in this study. However, for individual therapists there were disparities, as some therapists were relatively more skilled in treating White clients than racial/ethnic minority clients, and some therapists were more skilled in treating racial/ethnic minority clients than White clients. Indeed, an examination of the distribution of therapists suggests that disparities can be quite dramatic (see Figure 3). This suggests that the absence of general differences between White and racial/ethnic minority clients, although encour-

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Figure 3. Point estimates and 95% credible intervals for the expected percentage change in outcome when a therapist sees a minority client compared with a White client for therapists at the 5th, 25th, 50th, 75th, and 95th percentiles of the distribution of slopes. Positive percentage change indicates that therapist performance will be poorer with minority clients than White clients. Negative percentage change indicates the opposite.

aging, may mask disparities within therapist caseloads. Accordingly, a simple distinction between general and cultural competence as a deficit in performance with minority groups may be an oversimplification, as therapists may be more effective with certain clients (White or racial/ethnic minority) than others. Variability in disparities across therapists suggest some were more “culturally competent” than others. It is tempting to speculate that the therapist who achieves as good or better outcomes with racial/ethnic minority clients compared with White clients is by definition culturally competent. However, upon closer inspection, our findings raise important questions in regards to which therapists should be considered generally and/or culturally competent. For instance, a therapist might demonstrate no disparity between White and racial/ethnic minority clients but be relatively ineffective with both—that is, they may be “incompetent” indiscriminately. Alternatively, a therapist may have large disparities between White and racial/ethnic minority clients but demonstrate better outcomes (in absolute terms) with both racial/ethnic minority and White clients than other therapists. Perhaps a culturally competent therapist is one who achieves equivalent outcomes with White and minority clients while also meeting some absolute standard of effectiveness (Owen, Leach, Wampold, & Rodolfa, 2011). Large disparities in outcome might indicate concerns about therapists’ cultural competence no matter the absolute level of effectiveness. In the current analysis with 13 therapists, there was no evidence of a relationship between a therapist’s effectiveness with White clients and the difference between White and minority clients in their caseload. However, this relationship should be explored in larger data sets, as previous analyses of cultural com-

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petence measures suggest a strong relationship between general and cultural competence (e.g., Coleman, 1998). A primary limitation of this initial study that is typical of large psychotherapy trials is that the number of therapists was relatively small (n ⫽ 13). Accordingly, the current findings need to be replicated in larger data sets with a larger and more diverse sample of clients and therapists. Large data sets will facilitate more detailed analyses of specific client groups and allow for analyses that seek to explain therapist variation. To accommodate the limited sample we used a Bayesian approach that can provide accurate estimates even when the number of therapists is small. Despite the limited sample of therapists, the relatively large client sample and representation of racial/ethnic minority clients made the CYT data well positioned to provide an initial test of the effects of client race/ethnic status on therapist effectiveness. As a limited sample of therapists is likely to be a general limitation of clinical trial data, the current analytic strategy may hold particular value when extended to the evaluation of therapists via administrative data obtained from naturalistic settings where both therapist and client samples may be quite large. Our findings do not confirm or disconfirm any particular model of cultural competence currently discussed in the literature (American Psychological Association, 2003; D. W. Sue et al., 1992). It will be important for future studies to determine if specific observer- or client-rated measures of cultural competence predict disparities in therapist caseloads. The source of therapist discrepancies remains an important area for future research. For example, it may be that therapists with small disparities use specific skills that are not used by therapists with large disparities. Given the small number of therapists and the complex design (i.e., therapists were both nested and crossed with treatments, in that some therapists provided one treatment and others provided multiple treatments), it was not feasible to test if the type of treatment provided by a therapist predicted disparities between White and racial/ethnic minority clients. However, it is important for future research to address if the type of treatment provided either facilitates or detracts from the performance of the therapist with racial/ethnic minority clients. Moreover, the results of our study may apply only to the cultural groups tested (i.e., individuals who speak English and are primarily White, African American, and Latino). Future research should examine other specific racial/ethnic minority clients including non-English-speaking clients. Additional factors, including race of the therapist, could not be addressed with the current data, although evidence for the importance of client–therapist racial/ ethnic matching is mixed (Halliday-Boykins, Schoenwald, & Letourneau, 2005; Maramba & Nagayama Hall, 2002). As there were only three racial/ethnic minority therapists, an adequate test of client/therapist matching was not feasible with the current data. This is an important area for future research in larger data sets with sufficient numbers of racial/ethnic minority therapists. It is also important to note the limitations of racial/ethnic minority status as a client indicator related to cultural competence. Specifically, we have examined a particular understanding of cultural competence, namely, that cultural competence is found in a therapist’s effectiveness in treating different demographic groups. However, culture is more complex than group membership, and our analysis does not point to the source of any discrepancy in outcomes between White and racial/ethnic minorities in

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therapists’ caseloads. We do not know if, how, or what cultural factors were addressed in treatment. Accordingly, demographic variables should not be confused with the psychological constructs they are intended to represent, including past exposure to discrimination, racism, or culturally based misunderstandings. Future analyses of cultural competence would benefit from examining posttreatment symptoms in relation to how cultural issues were or were not addressed in the interaction between the client and therapist. Despite the limitations involved in this initial study, we have presented evidence that discrepancies in treatment effects between White and racial/ethnic minority clients may vary across therapist caseloads. This finding is consistent with the notion that the management of cultural factors in the therapeutic encounter is an important facet of the treatment process (Owen, Tao, Leach, & Rodolfa, in press). Further, results suggest that investigating the differences between general and cultural competence by estimating differences in therapist effectiveness is feasible and that multilevel modeling strategies may prove useful in understanding the source of disparities in mental health. Researchers may examine the validity of extant cultural competence measures by determining which client-rated measures of therapist cultural competence predict treatment disparities within therapist caseloads. More broadly, the examination of therapist outcomes offers a promising strategy for researchers to examine the source of disparities across a variety of outcomes and indicators of cultural background (e.g., gender identity, socioeconomic status, dropout, school achievement/retention, vocational outcomes, and various medical outcomes).

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Received November 1, 2010 Revision received February 2, 2011 Accepted February 2, 2011 䡲