Abstract Aims Determine if the language in which brief intervention (BI) is delivered influences drinking outcomes among Mexican-origin young adults in the emergency department when controlling for ethnic matching. Short Summary Aim of study was to determine if a patient’s preferred language of intervention influences drinking outcomes among Mexican-origin young adults in the emergency department. Results indicate no significant differences in drinking outcomes among those who received BI in Spanish and BI in English. Methods This is a secondary data analysis on data from 310 patients randomized to receive a BI completed in Spanish (BI-S) or English (BI-E), with 3- and 12-month follow-up. Outcome measures of interest were drinking days per week, drinks per drinking day, maximum drinks in a day and negative consequences of drinking. Results There were no significant differences in drinking outcomes among those who received BI in Spanish and BI in English. Conclusions Reduced drinking outcomes following BI among Mexican-origin young adults in the emergency department may not have been due to the language used to deliver intervention. Thus, our results provide evidence that language of intervention is not a crucial factor to achieve cultural congruence. In addition, our findings suggest that receiving the intervention is beneficial regardless of language, thus, facilitating real-world implementation. Mexican-origin Hispanics are the largest subgroup of Hispanics in the USA (United States Bureau, 2012). As this population continues to increase, it is important to investigate the factors that contribute to positive health outcomes. One such area that is of importance is the interaction of culture and culturally sensitive interventions as factors that influence drinking behaviors among people of Mexican-origin. Studies have compared alcohol use and negative consequences of drinking among national subgroups of Hispanics and found that Hispanics of Mexican origin have among the highest rates of heavy drinking, alcohol-related problems including driving under the influence of alcohol, arrests for driving while intoxicated, alcohol abuse and alcohol dependence (Caetano, 1988; Caetano and Galvan, 2001; Caetano et al., 2006; Caetano et al., 2008). Mexican-origin people represent over half of those living in the 24 US counties bordering Mexico (United States Census Bureau, 2012). Researchers throughout the years have found that individuals residing at the USA–Mexico border are especially vulnerable to alcohol misuse and problems (Lawrence, 1998; Wallisch and Spence, 2006; Caetano et al., 2013). For example, border residents have been found to report higher rates of heavy and problem-related drinking compared to those living elsewhere in the USA (Vaeth et al., 2012). A recent study by Mills et al. (2014) found that despite having similar beliefs, attitudes, norms and motives for use related to alcohol, those living in the border attended more bars and drank more in comparison to their non-border counterparts. Thus, Mills et al. (2014) concluded that individuals residing near the border may have more opportunities to drink due to the increased availability and visibility of alcohol in both the USA and Mexico. Despite the high prevalence of heavy drinking and reports of alcohol-related problems in the Hispanic population, Hispanics are less likely to seek and receive specialized treatment for alcohol problems in comparison to non-Hispanics (Chartier and Caetano, 2010). The use of brief interventions (BIs) for alcohol use has shown to be effective at producing positive drinking outcomes and reducing negative consequences related to drinking. The parent study from which data for this analysis was obtained (Cherpitel et al., 2016), a randomized controlled clinical trial of BI in the emergency department (ED), demonstrated that Mexican-origin patients who received a BI had significantly improved drinking outcomes and reported less negative consequences related to drinking in comparison to those who did not receive an intervention. Similarly, Field et al. (2010) also demonstrated the efficacy of BIs for reducing alcohol consumption among Hispanic ED patients. Given the evidence by Field et al. (2010) which found that BI was more beneficial among Hispanics in comparison to Blacks and Whites, subsequent analyses examined the potential influence of cultural congruence such as ethnic matching between the patient and provider. The results from Field and Caetano (2010) indicated that when patients of Hispanic origin were matched with providers of Hispanic origin, drinking outcomes improved. The authors concluded that ethnic matching may have positively influenced therapeutic alliance and resulted in improved drinking outcomes. However, in that study, in many cases of ethnic matching the preferred language was Spanish, confounding the effect of ethnic matching with the potential influence of client’s language preference. Cultural congruence between client and provider can improve mental health services for minorities (Smith, 2010). One way to achieve cultural congruence between client and provider is to make adaptations to interventions, such as facilitating the intervention in the client’s preferred language. Language matching in treatments is an important element to consider since a shared language between client and provider may facilitate communication and may allow researchers to successfully recruit and engage Hispanics (Miranda et al., 2005; Whaley and Davis, 2007). Grewal and Ritchie (2006) argue that respondents need to be able to share their experiences and the researcher needs to be able to hear and respond to those accounts. Thus, this can be best achieved if both parties are able to (literally) understand each other. More importantly, language matching has been shown to produce positive outcomes for minorities such as lowering the risk of dropping out of treatment, increasing the number of treatment sessions and lower use of emergency care services (Flaskerud and Liu, 1991; Sue et al., 1991; Snowden et al., 1995). The parent study from which data for this analysis was obtained (Cherpitel et al., 2016) took several steps to ensure ethnic matching by selecting Mexican-origin, Hispanic, bilingual community based interventionists who were licensed community health workers (‘promotores’) and were equally conversant to deliver the BI in Spanish and English. Because all interventions in the current study involved Mexican-origin patients and Mexican-origin promotores, the primary aim of this secondary data analysis was to examine if facilitating a BI in a patient’s preferred language impacts subsequent alcohol consumption and negative consequences of drinking among Mexican-origin young adults in the ED. METHODS Patient screening, eligibility, recruitment and randomization All patients were recruited from the ED of a public county hospital. Patients who self-identified as Mexican-origin and were between the ages of 18 and 35 were eligible to be screened for alcohol use. Eligibility was based on questions about quantity and frequency (Q–F) of drinking as a measure of at-risk drinking and the Rapid Alcohol Problems Screen (RAPS4; Cherpitel, 2000) as a measure of alcohol dependence. Patients were eligible for the study if they reported 15 or more drinks per week for males (eight or more for females) or 5 or more drinks in a day for males (four or more for females), or were positive on any one of the RAPS4 questions. Exclusion criteria included being admitted to the hospital for inpatient treatment, in police custody, currently being in alcohol treatment, or planning to leave the El Paso metropolitan area. Eligible patients agreeing to participate in the study were provided written informed consent and randomized to the screened-only, assessment or intervention condition. Given that our primary aim was to test if the language in which BI is delivered influences drinking outcomes and negative consequences of drinking, we report only on the patients who were in the intervention condition, agreed to have their BI session recorded, and completed 3- and 12-month follow-up. Methods have been reported in more detail elsewhere (Cherpitel et al., 2016). Participants The analyses for the present study consisted of patients who were recruited from University Medical Center in El Paso, Texas. All patients who were over the age of 18 (M = 23.61 ± 3.47) and met all criteria for inclusion were included in the analysis. At baseline, 310 patients were randomized to the intervention condition. Overall, 129 of them were females and all of the patients self-identified as being of Mexican-origin. Demographic data for one participant were missing. Intervention Patients randomized to the intervention condition received a BI in the language of their choice (English or Spanish) by promotores who had been trained using a Brief Negotiation Interview (BNI) protocol (Bernstein et al., 1997). The BNI is a specialized BI for the medical setting includes the following components: Engaging the patient and obtaining permission to discuss drinking, feedback, providing the patient with information about drinking norms, asking patient to discuss the pros and cons of their drinking, assessing the patient’s readiness to change their drinking, providing patient a list of options regarding making a change to their drinking, and negotiating with the patient their goal for reducing drinking and strategies for achieving this goal. The intervention was conducted in a private area near the waiting room while the patient was waiting for medical treatment, or in the treatment area. Following the intervention, a list of AA groups, general resources, and specialized services for alcohol treatment and counseling was provided to all patients. Promotores This study recruited Mexican-origin, bilingual, community-based, state-licensed community health workers (locally known as promotores) to deliver the intervention. Promotores received 3 days of BNI training conducted in both English and Spanish. The training was provided on-site by study staff and included mock intervention practice sessions with patients in the ED monitored by study staff. Additionally, promotores were provided two booster trainings. Fidelity of treatment To assess fidelity of treatment, interventions were initially observed by supervisory staff with a patient’s consent. Promotores received immediate feedback from supervisory staff. Interventions were taped and if any violation of the intervention protocol was observed, supervisory staff discussed this with the promotores. Additionally, at the end of the intervention, brief exit interviews were conducted to assess whether the promotor completed the following during the intervention: (a) talked to patient about their drinking and (b) asked patient about their satisfaction with the intervention and whether a contract agreement was reached. Baseline assessment Patients completed a baseline assessment that asked questions regarding the reason for the ED visit, self-reported drinking within 6 h prior to the event bringing them to the ED, at-risk drinking, symptoms of alcohol dependence, drinking outcomes, negative consequences of drinking, and risk taking/impulsivity and sensation seeking disposition. For the present study, we only report on results of drinking outcomes and negative consequences of drinking. A brief description of the measures that were used to assess drinking outcomes and negative consequences of drinking can be found below. The Timeline Followback (Sobell and Sobell, 1992) was used to assess drinking over the last 28 days. This measure of quantity and frequency has shown to be a reliable assessment for obtaining retrospective daily estimates of alcohol consumption. Using self-reported data from this measure collected at baseline, 3 months and 12 months, three dependent variables were operationalized—drinking days per week, drinks per drinking day and maximum drinks in a day. The Short Inventory of Problems (SIPs + 6) (Miller et al., 1995), a brief version of the Drinking Inventory of Consequences developed by project MATCH, was used to assess negative consequences related to drinking over the last 3 months and includes consequences related to physical, social responsibility, intrapersonal, impulse control and interpersonal domains, with six additional questions from the original instrument on injury and driving after drinking. The instruments were translated into Spanish and verified by back-translation (Breslin, 1986). Patients could choose whether they preferred to receive intervention in English (BI-E) or in Spanish (BI-S). The study community was largely bilingual, with many residents who speak both Spanish and some English. Given that the study was conducted in a bilingual city, interventions conducted in both Spanish and English were excluded from the analysis (15%). Three- and 12-month follow-up assessment Those in the assessment and intervention conditions were assessed at 3 months and all three groups were assessed at 12 months via telephone by an interviewer who was blind to group status. There were no differences in follow-up rates between BI-S and BI-E. More specifically, of patients eligible, 223 (66% BI-E patients) completed 3-month follow-up and 231 (68% BI-E) completed 12-month follow-up. Data analysis Descriptive statistics for all demographics were conducted as means and standard deviations (SD); discrete variables were presented as frequencies (n) and proportions (%). Drinking outcome variables were log transformed due to non-normality. A series of piecewise growth models were estimated using group membership (i.e. BI-E and BI-S) as the moderator and drinking days per week, drinks per drinking day, maximum drinks in a day and negative consequences of drinking as dependent variables. To model individual variability in mean levels and change in drinking outcomes throughout the two follow-up periods (i.e. 3 and 12 months) by preferred intervention language, we constructed a piecewise latent growth curve model (LGC) using Mplus statistical software version 7 (Muthén and Muthén, 1998-2012). In other words, patient’s preferred language (BI-E and BI-S) was used to assess changes in alcohol use from baseline, 3 months and 12 months. The piecewise technique is relevant for capturing different growth patterns across different time points (Duncan and Duncan, 2009). Notably, piecewise approach is commonly applied in intervention studies and longitudinal studies of alcohol use (White et al., 2007). For each outcome of interest, the piecewise growth model was set to produce three latent factors: an intercept factor and two growth (or slope) factors representing linear growth during follow ups (Time 1 to Time 2 and Time 2 to Time 3). The intercept factor is a constant and represents information pertaining to the overall mean and variance of the intercepts across the three points for any given individual in the sample. The intercept was set to Time 1, representing the baseline and the initial status at the first time point. Each growth factor (or slope) extracts individual variability in linear change over time. The mean of the slope factor is the average change in alcohol use per time interval (follow-up), and the variance of the slope factor represents individual differences in linear change in alcohol use (i.e. some participants change more than others). Should there be variability in the latent intercept factor and latent growth factors (null model), a variable denoting group membership would be used to predict the variability in these factors (model 1). In this sense, group membership would be said to moderate the growth factors, as change would depend on group membership. RESULTS Demographic and baseline differences by language of intervention At baseline, there were no significant difference between patients who preferred the intervention in English and those who preferred Spanish for gender, age and drinking before the event. However, there was a significant difference in nativity between patients who preferred the intervention in English and those who preferred Spanish (X2(1) = 34.39, P<0.001). Approximately 86.8% of patients in the English intervention were born in the United States. In comparison, only 56.7% of the participants in the Spanish intervention indicated being born in the USA (see Supplementary Table S1). Piecewise growth analysis and language effects of alcohol use change A two‐piece growth analysis was conducted to compare the long‐term change in alcohol use across individuals in the BI-E and BI-S intervention groups. A summary of the maximum likelihood estimates of the piecewise growth model of drinking outcomes are shown in Table 1. On average, all alcohol use outcomes showed an initial decrease between baseline and the 3‐month assessment, subsequently followed by a smaller increase from the 3‐month to the 12‐month assessment (Figs 1–4). Table 1. Maximum likelihood estimates of the piecewise growth models of drinking outcomes Change characteristics No. of drinking days per week last 28 days Drinks per drinking day last 28 days Maximum drinks last 28 days SIPs + 6 count last 3 months T1-intercept β (SE) −0.096 (0.045)* 0.031 (0.033) 0.021 (0.037) 0.124 (0.519) T2-slope 1 β (SE) 0.062 (0.063) 0.004 (0.052) −0.013 (0.058) 0.295 (0.514) T3-slope 2 β (SE) 0.010 (0.075) −0.023 (0.051) −0.003 (0.060) −0.366 (0.333) Change characteristics No. of drinking days per week last 28 days Drinks per drinking day last 28 days Maximum drinks last 28 days SIPs + 6 count last 3 months T1-intercept β (SE) −0.096 (0.045)* 0.031 (0.033) 0.021 (0.037) 0.124 (0.519) T2-slope 1 β (SE) 0.062 (0.063) 0.004 (0.052) −0.013 (0.058) 0.295 (0.514) T3-slope 2 β (SE) 0.010 (0.075) −0.023 (0.051) −0.003 (0.060) −0.366 (0.333) *P < 0.05. Table 1. Maximum likelihood estimates of the piecewise growth models of drinking outcomes Change characteristics No. of drinking days per week last 28 days Drinks per drinking day last 28 days Maximum drinks last 28 days SIPs + 6 count last 3 months T1-intercept β (SE) −0.096 (0.045)* 0.031 (0.033) 0.021 (0.037) 0.124 (0.519) T2-slope 1 β (SE) 0.062 (0.063) 0.004 (0.052) −0.013 (0.058) 0.295 (0.514) T3-slope 2 β (SE) 0.010 (0.075) −0.023 (0.051) −0.003 (0.060) −0.366 (0.333) Change characteristics No. of drinking days per week last 28 days Drinks per drinking day last 28 days Maximum drinks last 28 days SIPs + 6 count last 3 months T1-intercept β (SE) −0.096 (0.045)* 0.031 (0.033) 0.021 (0.037) 0.124 (0.519) T2-slope 1 β (SE) 0.062 (0.063) 0.004 (0.052) −0.013 (0.058) 0.295 (0.514) T3-slope 2 β (SE) 0.010 (0.075) −0.023 (0.051) −0.003 (0.060) −0.366 (0.333) *P < 0.05. Fig. 1. View largeDownload slide Drinking days per week by language of intervention (BI-E and BI-S) at baseline, 3- and 12-month follow-up. Fig. 1. View largeDownload slide Drinking days per week by language of intervention (BI-E and BI-S) at baseline, 3- and 12-month follow-up. Fig. 2. View largeDownload slide Drinks per drinking day by language of intervention (BI-E and BI-S) at baseline, 3- and 12-month follow-up. Fig. 2. View largeDownload slide Drinks per drinking day by language of intervention (BI-E and BI-S) at baseline, 3- and 12-month follow-up. Fig. 3. View largeDownload slide Maximum number of drinks per day by language of intervention (BI-E and BI-S) at baseline, 3- and 12-month follow-up. Fig. 3. View largeDownload slide Maximum number of drinks per day by language of intervention (BI-E and BI-S) at baseline, 3- and 12-month follow-up. Fig. 4. View largeDownload slide Negative consequences of drinking by language of intervention (BI-E and BI-S) at baseline, 3- and 12-month follow-up. Fig. 4. View largeDownload slide Negative consequences of drinking by language of intervention (BI-E and BI-S) at baseline, 3- and 12-month follow-up. Stability and change of drinking days per week Results from the null model demonstrated significant variability in the latent intercept factor and latent growth factors (see Supplementary Table S2). Given that the variance in the latent intercept factor and latent growth factors was significant, group membership was introduced as a predictor. Group membership was found to influence intercept differences in drinking days per week (t(290) = −2.110, SE = 0.045, P = 0.035). At baseline, patients in the BI-E condition reported more drinking days per week (M = 4.49, SD = 4.96) compared to patients in the BI-S condition (M = 3.26, SD = 2.70). Group membership, however, did not have an effect on the slopes, as there were no between-group differences in drinking days per week between BI-E and BI-S groups at 3-month or (t(290) = 0.987, SE = 0.063, P = 0.323) or at 12-month follow-up (t(290) = 0.139, SE = 0.075, P = 0.889; Fig. 1). Stability and change of drinks per drinking day Results from the null model indicated significant variability in the latent intercept factor and latent growth factors (see Supplementary Table S2). Given that the variance in the latent intercept factor and latent growth factors was significant, group membership was introduced as a predictor. However, group membership did not predict intercept differences in drinks per drinking day (t(290) = 0.931, SE = 0.033, P = 0.352), meaning that at baseline both BI-E (M = 5.31, SD = 3.51) and BI-S (M = 6.29, SD = 6.36) groups reported similar drinks per drinking day. Similarly, group membership did not have an effect on slope differences such that there were no between-group differences in drinks per drinking day at 3-month (t(290) = 0.080, SE = 0.052, P = 0.936) or at 12-month follow-up (t(290) = −0.445, SE = 0.051, P = 0.657; see Fig. 2). Stability and change of maximum drinks in a day Results from the null model revealed significant variability in the latent intercept factor and latent growth factors (see Supplementary Table S2). Given that the variance in the latent intercept factor and latent growth factors was significant, group membership was introduced as a predictor. However, group membership did not predict intercept differences in maximum drinks in a day (t(290) = 0.559, SE = 0.037, P = 0.576). In other words, at baseline, both BI-E (M = 7.40, SD = 5.44) and BI-S (M = 8.69, SD = 7.71) groups reported similar maximum drinks in a day. Likewise, group membership did not impact differences in slopes. In other words, there were no between-group differences in maximum drinks in a day at 3-month (t(290) = −0.216, SE = 0.058, P = 0.829) or at 12-month follow-up (t(290) = −0.048, SE = 0.060, P = 0.961; Fig. 3). Stability and change of negative consequences of drinking Results from the null model revealed significant variability in the latent intercept factor and latent growth factors (see Supplementary Table S2). Given that the variance in the latent intercept factor and latent growth factors was significant, group membership was introduced as a predictor. Findings indicate that group membership did not predict intercept differences in negative consequences of drinking (t(290) = 0.238, SE = 0.519, P = 0.812) which suggests that at baseline both BI-E (M = 3.15, SD = 3.85) and BI-S (M = 3.11, SD = 4.04) groups reported experiencing a similar number of negative consequences of drinking. Similarly to the other drinking outcomes, group membership had no impact on slopes such that there were no between-group differences in negative consequences of drinking at 3-month (t(290) = 0.574, SE = 0.514, P = 0.566) or at 12-month follow-up (t(290) = −1.102, SE = 0.333, P = 0.271; Fig. 4). DISCUSSION Given the growth of the Hispanic population and their increased risk for displaying negative consequences of drinking, it is essential that we continue to evaluate the effectiveness of treatments such as brief alcohol interventions. In the parent study, patients who received a BI showed significant reductions on the maximum number of drinks per occasion at 3 months and in all outcomes of alcohol consumption at 12 months, in comparison to those who did not receive a BI (Cherpitel et al., 2016). However, the parent study was not able to determine if providing the patient with an intervention in their preferred language a pivotal role in the results. The present study sought to investigate the extent to which administering a BI in the patient’s preferred language can partially account for the noted reductions in drinking outcomes in the parent study (Cherpitel et al., 2016). Specifically, the primary aim of this secondary data analysis was to examine if providing a BI in a patient’s preferred language positively impacts subsequent alcohol consumption and negative consequences of drinking among Mexican-origin young adults in the ED. Our results revealed that there were no between-group differences in all of the drinking outcomes and negative consequences of drinking between those who received the intervention in Spanish or those who received the intervention in English at 3- and 12-month follow-up. It is important to note that because patients were not randomized to receive the BI in their preferred language versus not in their preferred language, we cannot make any strong conclusions about the impact of patient’s preferred language on the efficacy of the intervention. However, because all patients were ethnically matched with an interventionist our findings provide further support for the importance of ethnic matching in BIs. This would suggest that some advantage is conferred by shared cultural values and experiences between patient and provider, which may positively influence the therapeutic alliance, particularly in opportunistic alcohol interventions involving one session. Furthermore, previous studies indicate that interventions among Hispanics are more likely to be effective when patients are matched with interventionists with similar ethnic background (Casas et al., 2002; Vasquez, 2007). Interventionists of the same ethnicity of the patient may be more likely to comprehend and respond to culture-specific values, norms and attitudes held by the patient. As a result, patients may experience greater therapeutic alliance, which has been shown to result in positive outcomes (Vasquez, 2007). Given that there were no between-group differences our results suggest that facilitating a BI in a patient’s preferred language does not provide any statistically significant benefits to the intervention. However, future studies should focus on investigating the interaction of patient’s preferred language and ethnic matching to better understand if these two factors are important contributors to the efficacy of a BI. Limitations There were several limitations in the present study. First, patients were only randomized to the following conditions: screened only, assessed and intervention. Patients were not randomized into a language condition but rather selected what language they preferred to receive the BI. Thus, the present study adopted a quasi-experimental design. Consequently, it is difficult to draw conclusions about the causal implications related to the language of intervention delivery. However, despite this limitation, the present study introduces a new approach to evaluate BIs and provides future directions in intervention research. Second, the frequency of drinking among patients in this study may not be representative of other populations. For example, patients reported drinking on average ~4 days a week. However, patients reported consuming 6 drinks per drinking day and over 8 drinks on a maximum drinking occasion, reflecting a pattern of heavy episodic drinking characteristic of the typical drinking of young Mexican-origin Hispanic population. The findings, based on patients with this drinking pattern, may not be generalizable to other cultures or populations with a greater prevalence of alcohol use disorders and related problems. Third, certain restrictions were applied to the statistical model in order to allow the model to converge. Last, self-reported ethnicity of patient and promotor were used as proxies of ethnic matching. Future directions Despite these limitations, this is a novel contribution to understanding factors contributing to the efficacy of BI for alcohol use among young Mexican-origin Hispanics in a border region. For example, the parent study was the first randomized controlled clinical trial of BI that used promotores. Even though ED staff have been found to be effective providers of BI (D’Onofrio et al., 2012) they may have time constraints and other competing priorities, which may create barriers to the proper implementation of BI. Present results and results from the parent study (Cherpitel et al., 2016) both support the use of promotores in delivering BI to young adult Mexican-origin patients in the ED. Santa Ana et al. (2009) demonstrated that bilingual Spanish-speaking therapists from the community can be trained to implement therapies with adequate fidelity and skill using an intensive multisite training and supervision model. The promotores from the present study were bilingual, peer educators, licensed as community health workers, who live in a bilingual city. As a result, the promotores were well informed on how to assist Hispanics and were more likely to share cultural values and experiences with the patients recruited in this study. Thus, researchers may want to further explore the benefits of having promotores be the ‘messengers’ delivering the interventions, as studies have shown that the use of promotores has reached widespread success as a low-cost, culturally appropriate prevention model in clinical and community settings among ethnic populations (Warrick et al., 1992; Williams, 1992; Ramos and Ferreira-Pinto, 2006; Ramos et al., 2006; Balcázar et al., 2009). Present findings suggest that facilitating a BI in a patient’s preferred language did not add any statistically significant benefits to the BI’; therefore, this allows us to conclude that promotores can be successful at providing interventions in either language without jeopardizing the fidelity of an intervention. Moreover, our hope is that the findings of the present study can assist researchers, clinicians and stakeholders as they continue to develop evidence-informed services for minority populations that are culturally sensitive and increase positive outcomes for Mexican-origin Hispanic individuals. For example, it is highly improbable that real-world settings will have the capacity and resources to match all patients and interventionists on the basis of ethnicity or preferred language; thus, researchers and providers may want to explore the use of other skills that can enhance cultural congruence between patient and providers. Smith (2010) argues that researchers can promote cultural congruence by providing services in clients’ preferred language, modifying the length/frequency of sessions, using culturally congruent terminology and concepts, incorporating family members or friends, and or consulting with individuals who are familiar with a patient’s culture to facilitate accurate understanding. However, researchers should keep in mind that the simple addition of one of these elements may not be sufficient to achieve cultural congruence. Thus, future studies may want to explore not only the unique effect added by each of these elements, but rather focus on the interaction of these effects and how they may help improve cultural congruence between patient and provider and ultimately improve health outcomes in underserved populations such as young adults of Mexican-origin living on the USA–Mexico border. 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Alcohol and Alcoholism – Oxford University Press
Published: Nov 1, 2018
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