Emotional intelligence and the counselor: examining the relationship of trait emotional intelligence to counselor burnout.
Counselor burnout is a negative state characterized by physical, psychological and emotional strain (Lee et al., 2007; Maslach, 2003). When counselors experience burnout they demonstrate fatigue and emotional exhaustion, and depersonalize or devalue clients (Lee et al., 2007; Maslach, 2003). Research suggests that counselors experiencing burnout have significant difficulty competently performing their duties (Lawson et al., 2007; Lee et al., 2007). Therefore, counseling scholars have consistently reported on the importance of using self-care strategies to prevent counselor burnout (Cummins, Massey, & Jones, 2007; Roach & Young, 2007; Smith, Robinson, & Young, 2007; Witmer & Young, 1996; Yager & Tovar-I31ank, 2007; Young & Lambie, 2007).
Research suggests that emotional intelligence (El) could play a role in buffering counselor burnout. EI consists of one's ability to identify, regulate and use emotions effectively (Davey, 2005). Salovey and Mayer (1990) were the first to formalize the definition of EI, stating that it entails: (a) the appraisal and expression of emotion, (b) emotional regulation, and (c) the utilization of emotions. However, since their original publication the definition of EI has expanded to include a constellation of personality traits, abilities, and behavioral dispositions, such as adaptability, self-awareness, and relationship skills (Cherniss, Extein, Goleman, & Weissberg, 2006; Goleman, 1995; Matthews, Roberts & Zeidner, 2004; Mayer, Salovey, & Caruso, 2008; Petrides & Furnham, 2001). In an attempt to create a comprehensive and measurable definition for EI, Petrides and Furnham (2001) divide EI into two categories: trait EI and ability EI. Trait EI encompasses an array of behavioral dispositions and self-perceived abilities that are measured through self-report; whereas ability EI is performance-based and is more consistent with a measure of achievement, cognitive ability, and information processing. The wider and more comprehensive definition of EI is trait EI; however, it would be erroneous to suggest that these two constructs are mutually exclusive (Petrides & Furnham, 2001).
Studies have shown that higher EI is associated with less occupational burnout in nursing, teaching and with resident doctors (Gorgens-Ekermans & Brand, 2012; Pishghadam & Sahebjam, 2012; Swami & Mathur, 2013) and less anxiety (Lizeretti & Extremera, 2011; Yip & Cote, 2013). Salovey, Bedell, Detweiler, and Mayer (1999) argued that because coping inevitably involves managing emotions it is not surprising that EI would be associated with an increased ability to manage distressing emotions. In addition, scholars propose that EI not only helps to regulate negative emotions but also facilitates coping through the management of positive emotions (Goleman, 1995, Salovey et al., 1999). Therefore, increased EI could facilitate the coping process, strengthen wellness, and aid in preventing burnout. However, research on EI and burnout is still relatively young (Cherniss et al., 2006; ; and to our knowledge, no study exists examining the influence of EI on counselor burnout.
The purpose of this study is to examine the relationship between EI and counselor burnout. Given the relationship that EI has with stress, anxiety, and burnout in other professions, it is logical that EI is associated with counselor burnout. If so, counselors, counseling supervisors, and counselor educators could focus on strategies that increase EI to mitigate the deleterious effect of counselor burnout. This study aims to answer the following research question: What is the relationship between trait emotional intelligence and burnout in a sample of practicing professional counselors?
Procedures and Participants
The sample included 539 practicing counselors from a Southern state in the United States. Prior to the collection of data, approval for this investigation was obtained from the lead author's Institutional Review Board. This study utilized simple random sampling of licensed mental health counselors and licensed marriage and family therapists accessed with permission from the state's department of health. A total of 8,935 individuals were invited to participate in this investigation through three email invitations that adhered to Dillman, Smyth, and Christian's (2009) tailored design method protocol for email surveys. Twenty-seven participants responded by email indicating they were no longer practicing and therefore did not qualify for the investigation. The series of invitations resulted in 539 respondents (6.05% usable response rate), which is a large sample size as compared to other published studies examining this population (e.g., Lanham, Rye, Rimsky, & Weill, 2012; Thompson, Amatea, & Thompson, 2014; Williams, Helm, & Clemens, 2012). While this response rate is low, an accurate return rate for online email-based surveys is difficult to discern because researchers have a limited knowledge of whether the email addresses acquired for invitation are working and active for the potential respondents (Granello & Wheaton, 2004). Therefore, it is likely that the true response rate for this survey is higher than what resulted in this study due to the chance that participants did not receive the opportunity to complete the as a result of email addresses that are no longer used.
The participants' (N = 539) age ranged from 25 to 87 years (M = 53.26, SD = 12.26) with 430 women (79.8%), 108 (20.0%) men, and one (0.2%) respondent selecting "Other" as the gender identifier. The ethnicity of the sample was 78.3% Caucasian (n = 422), 13.0% Hispanic (n = 70), 5.2% (n = 28) African American, 3.0% (n = 16) Other Ethnicity, 0.4% (n = 2) Asian American, and 0.2% (n = 1) Pacific/Islander. The professional identify of the participants included 388 (72.0%) mental health counselors, 92 (17.1%) marriage and family therapists, and 59 (10.9%) who identified with some other profession. Participants' years of experience ranged from 2 to 50 years (M = 19.61, SD = 10.38). The characteristics of the participants in this investigation are similar to a study that used the same sample and data collection methods (Mullen, Lambie, & Conley, 2014) and with other studies that surveyed mental health counseling practitioners using different data collection methods (e.g., Thompson et al., 2014; Williams et al., 2012); thus, the representation of counselors is similar to previous studies.
Counselor Burnout Inventory. The Counselor Burnout Inventory (CBI; Lee et al., 2007) is a 20-item self-report measure that is designed to examine counselors' burnout. Specifically, the CBI intends to measure five domains of burnout that include: (a) Exhaustion, (b) Incompetence, (c) Negative Work Environment, (d) Devaluing Client, and (e) Deterioration in Personal Life. Items include a 5-point numerical response scale (1 = never true, 5 = always true) that assesses respondents' agreement with feelings and behaviors associated with various aspects of burnout. Higher scores on the CBI indicate a higher degree of symptomology for overall burnout or the given subscale. A sample item from the CBI is "I feel I am an incompetent counselor." The internal consistency of the CBI was good with alpha coefficients of .85 for the Exhaustion scale, .83 for the Negative Work Environment scale, .80 for the Devaluing Client scale, .73 for the Incompetence scale, and .78 for the Deterioration in Personal Life scale (Lee et al., 2010). Construct validity was supported through an exploratory factor analysis that produced a five-factor solution along with a confirmatory factor analysis that indicated adequate fit with the data (Lee et al., 2007). The Cronbach's alphas for the scales on the CBI in the present investigation indicated acceptable (Streiner, 2003) internal consistency with scores of .87 for Exhaustion, .72 for Incompetence, .88 for Negative Work Environment, .72 for Devaluing Client, and .72 for Deterioration in Personal Life.
Trait Emotional Intelligence Questionnaire-Short Form. The Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF; Petrides & Furnham, 2001) is a 30-item self-report measure that is designed to examine participants' global trait EI. The TEIQue-SF is derived from the longer, full version Trait EI Questionnaire (TEIQue; Petrides & Furnham, 2001) that covers 15 facets of EI. The TEIQue-SF provides subscale scores based on the long form version of the TEIQue that include the following domains: (a) Well Being, (b) Self-Control, (c) Emotionality, (d) Sociability, and (e) Global Trait (Petrides, 2006). However, the instrument developer encourages researchers to use the TEIQue-SF as a measure of global trait EI because the subscales have not been validated through research (Petrides, 2006). Items include a 7-point Likert response scale (1 = completely disagree, 5 = completely agree) that assesses respondents' agreement with feelings and behaviors associated with various aspects of global trait EI. Higher scores on the overall TEIQue-SF indicate an increased degree of overall trait EI and higher scores on the TEIQue-SF subscales indicate an increased degree for the domain of each given subscale. A sample item from the TEIQue-SF is "I usually find it difficult to regulate my emotions." The internal consistency of the TEIQue-SF has been found to be strong with alpha coefficients ranging from .87 to .89 (Cooper & Petrides, 2010). Similarly, the internal consistency of the TEIQue-SF with this study was strong with a Cronbach's alpha of .88.
Prior to analysis, the data were screened. Initially, the data were examined for extreme outliers by identifying variables three standard deviations from the mean (Osborne, 2012). Nineteen cases had at least one variable with an extreme outlier. We decided to address extreme outliers because structural equation modeling (SEM) is sensitive to the impact influential data points have on means, standard deviations, and correlation coefficients (Schumacher & Lomax, 2010). A Windsorized mean was calculated to replace the extreme outlier variable based on adjacent data points (Barnett & Lewis, 1994; Osborne & Overbay, 2004). Next, the fit between the distribution of the variance and the assumptions for the statistical analysis employed (i.e., normality, homogeneity of variance, linearity, and multicollinearity; Hair, Black, Babin, Anderson, & Tatham, 2006; Tabachnick & Fidell, 2007) were analyzed. The assumption of normality was violated as indicated by absolute skewness values (Curran, West, & Finch, 1996; Kline, 2011); however, no other violations of statistical assumptions were identified. The treatment of non-normal data is discussed in the data analysis section.
This investigation employed a two-step SEM approach (Kline, 2011) to test the research hypothesis using AMOS (version 21; Arbuckle, 2012). software. In the first step we conducted a confirmatory factor analysis (CFA) to inspect the measurement model's fit with the data. Next, we developed and tested the hypothesized structural model that was formulated based on the measurement models. Both the measurement and structural models were evaluated with model fit indices, standardized residual covariances, standardized factorial loadings, and standardized regression estimates and modifications were made if needed (Byrne, 2010; Kline, 2011).
As noted, some data in this study were non-normal. We decided to use a maximum likelihood (ML) estimation technique because researchers have noted that ML may be robust to non-normal data given sufficient sample sizes (> 100; Chou & Bentler, 1995; Lei & Lomax, 2005).
To evaluate the goodness of fit of the hypothesized measurement and structural models we used a diverse selection of endorsed fit indices (e.g., Hu & Bentler, 1999; Kline, 2011; Weston & Gore, 2006). The fit indices we consulted include: (a) chi-square, (b) comparative fit index (CFI), (c) goodness-of-fit (GFI) standardized root mean square residual (SRMSR), and (d) root mean square error of approximation (RMSEA). Additionally, the Normed Fit Index (NFI) and Non-normed Fit Index (NNFI) were consulted because they are more robust to non-normal data as compared to other indices (Lei & Lomax, 2005).
The means and standard deviation were calculated for the CBI (Lee et al., 2007) subscales. The subscale with the highest degree of symptomology were Exhaustion (M = 2.39, SD = .83), Deterioration in Personal Life (M = 2.08, SD = .68), and Negative Work Environment (M = 2.03, SD = .95) with Incompetence (M = 1.90, SD = .61) and Devaluing Client (M = 1.34, SD = .43) as the lower-scoring subscales. These results are similar to other studies using the CBI (e.g., Lee et al., 2007; Lee et al., 2010; Puig, Yoon, Callueng, An, & Lee, 2014) and indicates that the respondents indicated moderate to low levels of burnout across the five domains examined in the CBI.
Counselor Emotional Intelligence
The means and standard deviation were calculated for the TEIQue-SF total score and subscales. Participants (N = 539) reported an average total score on the TEIQue-SF of 5.59 (SD = 0.51). These findings from counselors are higher as compared to prior research using the TEIQue-SF with multiple samples from the general population (e.g., means ranging from 4.94 to 5.18; Cooper & Petrides, 2010), which indicates that the participants in this study on average had a higher degree of trait EI as compared to a general sample of individuals.
This investigation used SEM analysis to examine the relationship of practicing counselors' EI to their degree of burnout. The manifest variables were measures of counselor EI (as indicated by the TEIQue-SF total score; Petrides & Furnham, 2001) and burnout (as indicated by the CBI subscales; Lee et al., 2007). The latent variables were counselor EI and burnout. A post hoc power analysis using [G.sup.*]Power (Faul, Erdfelder, Lang, & Buchner, 2007) indicated that the observed statistical power was 1.0.
Measurement model. A CFA was conducted to examine the measurement model for the TEIQue-SF and CBI. The initial standardized factor loadings for the measurement model produced scores that ranged from .47 to .80 for the CBI (Lee et al, 2007). All CBI factor loadings were acceptable (i.e., > .40; Comrey & Lee, 1992; Stevens, 1992; Tahachnick & Fidell, 2007). Moreover, all fit indices for the measurement model indicated a poor-fitting model, [chi square] (df = 9, N = 539) = 125.67, p < .001; GFI = .92; CFI = .87; RMSEA = .16; SRMR = .07; NFI = .87; NNFI = .79. Therefore, the modification indices and standardized residual covariance matrix of the CBI measurement model were consulted, which suggested that the error terms for the subscales Exhaustion and Deterioration in Personal Life be correlated. In consideration of the theoretical nature of these constructs, we deemed it justifiable to correlate the errors. However, this modification may be a limitation of this measurement model. After correlating the errors of the subscales Exhaustion and Deterioration in Personal Life, all fit indices for the measurement model indicated an acceptable fitting model except for chi-square, [chi square] (df = 8, N = 539) = 64.84, p < .001; GFI = .96; CFI = .94; RMSEA = .11; SRMR = .05; NFI = .93; NNFI = .88. The cutoff range for the chi square fit index, RMSEA, and NNFI were not met; however, we used a diverse selection of indices to obtain a diverse perspective regarding how the data fit the model (Hu & Bentler, 1999), which indicated an overall acceptable-fitting measurement model. The modification resulted in new standardized factor loadings that ranged from .56 to .68 for the CBI. The measurement model was deemed appropriate for use in the structural model with these data.
Structural model. The structural model (see Figure 1) was hypothesized and developed based on a thorough review of the literature regarding a potential relationship between EI (as measured by the TEIQue-SF; Petrides & Furnham, 2001) and counselor burnout (as measured by the CBI; Lee et ah, 2007). EI was identified as the exogenous variable and counselor burnout was identified as the endogenous variable. A review of the structural model indicated an acceptable goodness-of-fit across all fit indices except for chi-square, RMSEA, and NNFI, [chi square] (df= 8, N = 539) = 64.84, p < .001; GFI = .96; CFI = .94; RMSEA = .11; SRMR = .05; NFI = .93; NNFI = .88. Examination of the standardized regression weights acknowledged that counselors' TEIQue-SF scores contributed to 38% (([beta] = -.62, p < .001; large effect size; Sink & Stroh, 2006) of the variance in their CBI scores. The TEIQue-SF made a statistically significant contribution (p < .001) to EI.. All of the CBI subscales made a statistically significant contribution (p < .001) to counselor burnout. The CBI subscales Incompetence ([beta] = .68), Exhaustion ([beta] = .65), and Negative Work Environment ([beta] = .61) were the strongest contributors to burnout with Devaluating Client ([beta] = .56) and Deterioration in Personal Life ([beta] = .56) making the weakest contribution. This structural model indicates that a higher degree of global trait EI contributes to a decreased level of burnout in counselors.
In the current study, we endeavored to understand better the relationship between EI and counselor burnout, and to assess the contribution of practicing mental health counselors' EI to their degree of burnout. Using structural equation modeling, we are able to conclude that, in this sample of professional counselors, EI does have a significant relationship with counselor burnout. Moreover, we determined that in our sample, EI accounted for 38% of the variance in counselor burnout, and that individuals with higher EI had lower levels of counselor burnout. Given that little is known regarding the role of EI in counselors and that, to our knowledge, no study exists examining the role of EI in counselors' self-care, we believe these findings provide a significant contribution to the research literature and present several avenues for future research.
Further examination of the structural model (Figure 1) provides more findings that are derived from this investigation. Specifically, counselors' incompetence, exhaustion, and negative work environment were the strongest predictors of their burnout as measured by the CBI (Lee et ah, 2007). These findings are not consistent with the descriptive statistics that found exhaustion, deterioration in personal life, and negative work environment to have the highest mean score for these participants with incompetence and devaluing client returning the lowest mean scores. An additional noteworthy finding was that the data necessitated we correlate the error terms of exhaustion and deterioration in personal life, which indicates these factors may overlap in terms of the construct they measure. Overall, the results from this study provide a novel look into counselors' degree of EI and burnout.
Implications for Mental Health Counseling
Given the myriad of stressors encountered in counseling, it is crucial that counselors find methods for preventing counselor burnout. As previously noted, a high level of burnout is considered a factor relating to relate to poor job performance for counselors (Lawson et ah, 2007; Lee et ah, 2007). When counselors experience burnout they are likely to de-personalize their clients (Maslach, 2003) limiting their ability to develop an effective therapeutic alliance. Building an effective therapeutic alliance is crucial to being an effective counselor (Rogers, 1957; Young, 2013), and accounts for the largest portion of client outcomes (Norcross, 2009). When counselors are unable to build an alliance counseling could easily turn caustic. Additionally, helping professionals experiencing burnout are likely to harm clients with unprofessional behavior and a lack of empathy (Dyrbye et ah, 2010).
In addition to psychologically and emotionally detaching from clients (Maslach, 2003), counselors who experience burnout often experience depression and a decreased satisfaction with life (Suner-Soler et ah, 2013; Takai et ah, 2009). When a counselor is experiencing unmanaged levels of psychological distress, such as depression, the counselor could unintentionally inflict harm on clients; therefore, the American Counseling Association (2014) Code of Ethics states that it is a counselor's responsibility to monitor his or her own risk for impairment and limit, lessen, or suspend services accordingly (Section C.2.g). In other words, it is the ethical responsibility of counselors to monitor their risk of burnout, and in order to do such they need emotional self-management and regulation skills (Venart, Vassos, & Pitcher-Heft, 2009). These skills are core characteristics of EI (Cherniss & Coleman, 2001; Chernis et ah, 2006; Coleman, 1995). Thus, EI can help improve the ethical considerations of counselors.
Lee et al (2007) noted that counselors have the responsibility to provide the highest quality of care, and burned-out counselors are not capable of doing such. In a qualitative study of 28 rural and urban substance abuse counselors, Oser, Biebel, Pullen, and Harp (2013) explored the clinician's experiences of burnout. Whereas, there were several differences in subthemes between these two populations, several general themes emerged, one of them being that burnout resulted in poor quality clinical care, and another being that self-care was essential in preventing burnout. Self-care can be challenging, especially when one considers the way the perpetual cycle of caring and empathie attachments that counselors make quickly depletes their internal resources (Skovholt, Grier, & Hanson, 2001). However, if counselors fail to practice self-care and incorporate wellness, they risk becoming impaired practitioners (Witmer & Young, 1996; Young & Lambie, 2007). Thus, developing strategies that can prevent counselor burnout is crucial for effective counseling practice.
El Strategies for Mental Health Counselors
Traditionally, scholars have suggested using wellness strategies to avoid counselor impairment (Cummins et ah, 2007; Roach & Young, 2007). However, the findings of the current study illustrate that increasing EI may also contribute to reduced counselor burnout. Petrides and Furnham (2001) and Petrides (2006) reported that EI consisted of cognitive-emotional ability and behavioral dispositions, such as emotional appraisal, emotion management, emotion regulation, and social competence. While wellness strategies aim at decreasing the effects of stress and emotional fatigue, EI helps by equipping and strengthening a counselor's ability to self-regulate and manage stress and emotions as they are occurring in session or in the work environment. Our aim in this study is not to diminish the benefit of wellness strategies, but rather to illustrate that increasing EI can also be beneficial to counselors. One practical implication of the current study for counselors is to simply note that EI is a useful addition to their arsenal against counselor burnout. We suggest that, as a part of their continuing education and self-care practices, professional counselors engage in strategies that cultivate their emotional intelligence.
Several researchers have employed psycho-educational strategies to increase EI (Nelis et ah, 2011; Nellis, Quoidbach, Mikolajczak, & Hansenne, 2009). These researchers created brief training workshops on the topic of understanding emotions, identifying emotions, expressing and using emotions, and managing emotions. During the workshop facilitators used a variety of techniques, including dramatization, experiential activities, and lectures based on the latest research findings on emotion. In both studies, findings indicated that participants did increase in El. Moreover, Nelis and colleagues (2011) reported that changes remained significant at six months after the training sessions. Counselors should attend and even consider facilitating similar workshops for counseling professionals in their area.
Researchers have also found that meditation practice can increase El (Chu, 2009; Lomas, Edgington, Cartwright, & Ridge, 2013; Perelman et ah, 2012). Schutte and Malouff, (2011) sought to determine if there was a correlation with mindfulness, a positive affect scale, a life satisfaction scale, and EI (N = 125). Results indicated that greater mindfulness was associated with higher El (r = .66, p < .001). Likewise, Perelman and colleagues (2012) conducted a longitudinal investigation to assess if prisoners in an Alabama state prison who participated in a meditation group would have higher levels of El. Their findings indicated that those who participated in the meditation treatment had significantly higher levels of EI than those in the comparison group (p < .01). Meditation is a simple and effective tool that counselors can implement in their daily routine free of charge. The meditation practice used in the previous studies is mindfulness meditation. Mindfulness meditation trainings are widely available throughout the United States. We recommend that counselors employ meditation as a part of their daily self-care.
In sum, previous research has indicated that EI can be increased through interventions. Given the benefits of EI to counselors, they would be wise to consider incorporating EI interventions into their professional lives and professional development. Counselors can begin by taking small steps, such as reading the latest research on emotions or beginning a brief meditation practice that they can employ before seeing clients. Counselors (especially beginning counselors) should also consider expanding their understanding of the way emotions influence their health (i.e., increasing their cognitive-emotional aptitude), in addition to employing a wellness plan.
Limitations and Future Research Directions
As with all research, this investigation has its limitations. First, due to the cross-sectional design of this study, it is not possible to suggest causation. In order to make such claims, we would need longitudinal data and a sufficient control group. Additionally, although we have a substantial sample size (N = 539) the data for the current study were collected from only one region of the United States. Although we believe our findings provide a significant contribution to the literature, further research is needed to determine if our findings are generalizable to other groups of counselors. These findings provide us with preliminary data to justify a larger-scale investigation. However, we can not state that the findings are generalizable to the greater population of counselors because the data were only collected from a single geographic location. Another limitation includes the low response rate for this investigation. Despite these limitations, this investigation provides new and relevant insight into the emotional intelligence and burnout of practicing counselors.
This study is part of emerging research that examines the relationship of El to counselor qualities (Easton, Martin, & Wilson, 2008; Martin, Easton, Wilson, Takemoto, & Sullivan, 2004) and therefore provides some directions for future research. First, research is needed to replicate these findings with larger samples from different regions to support the generalizability of the results. Researchers can consider the use of different data collection methods to obtain a better response rate. Furthermore, research is needed to examine strategies for increasing EI in counselors. Previous research (Nelis et ah, 2009) has examined interventions with university students, and we believe it would be advantageous to examine these interventions within counselor preparation programs. Additional research can examine the relationship between EI and other counselor characteristics not explored in this study such as clinical competency and empathy. Given that some scholars suggest that EI is associated with relationship enhancement skills (Cherniss et ah, 2006; Goleman, 1995) it is possible that EI could benefit counselors' effectiveness in session and that measures of EI could be used to assess student counselors' preparation for clinical practice. Finally, little is known about how EI develops over the course of counselor training programs. Therefore, future research should examine the development of EI on student counselors at key points during their preparation (i.e., through a longitudinal study). More research on EI may illuminate its relationship to key counselor characteristics and El's implications for the counseling profession.
In summary, this investigation examined the contribution of EI to counselor burnout in a sample of professional counselors. The findings indicate that participants' level of EI negatively predicted their degree of burnout. Given that counseling can be as difficult as it is rewarding, it is important that counselors find effective ways of keeping themselves fit for the profession and develop an understanding of what factors may prevent burnout. The findings from this study indicate that EI may prove to be a helpful tool in burnout prevention.
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University of North Carolina at Charlotte
Patrick R. Mullen
East Carolina University
Daniel Gutierrez, Department of Counseling, University of North Carolina at Charlotte; Patrick R. Mullen, Department of Interdisciplinary Professions, East Carolina University, Greenville.
Patrick R. Mullen is now at Department of School Psychology and Counselor Education, College of William and Mary. Correspondence concerning this article should be sent to Daniel Gutierrez, Department of Counseling, University of North Carolina, Charlotte, 9201 University City Blvd., Charlotte NC, 28223. E-mail: email@example.com
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|Author:||Gutierrez, Daniel; Mullen, Patrick R.|
|Publication:||Journal of Mental Health Counseling|
|Date:||Jul 1, 2016|
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