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A Typology of Burnout in Professional Counselors
Lee, Sang Min
; Cho, Seong Ho
; Kissinger, Daniel
; Ogle, Nick T. Journal of Counseling and Development : JCD 88.2 (Spring 2010): 131-138.
Abstract
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The authors used a cluster analysis procedure and the Counselor Burnout Inventory (S. M. Lee et al., 2007) to identify professional counselors’ burnout types. Three clusters were identified: well-adjusted, persevering, and disconnected counselors. The results also indicated that counselors’ job satisfaction and self-esteem were good discriminators between the 3 clusters. Implications for counselors are discussed. [PUBLICATION ABSTRACT]
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Headnote
The authors used a cluster analysis procedure and the Counselor Burnout Inventory (S. M. Lee et al., 2007) to identify professional counselors’ burnout types. Three clusters were identified: well-adjusted, persevering, and disconnected counselors. The results also indicated that counselors’ job satisfaction and self-esteem were good discriminators between the 3 clusters. Implications for counselors are discussed.
There are many issues that could strain a counselor’s physical and psychological resources – managed care constraints, increased budget cuts, and burgeoning caseloads – and the counselor is expected to maintain a sense of equilibrium in facing these issues while still providing quality therapeutic services (Kesler, 1990; O’Halloran & Linton, 2000). If not closely monitored, counselor burnout, conceptualized as a combination of multiple emotional and physical ailments manifesting cognitively or within the workplace, could ensue and jeopardize both the counselor’s well-being and treatment efficacy.
Several descriptions of burnout are found in the professional literature. Maslach and Jackson (1981) described burnout as a syndrome distinguished by emotional exhaustion, depersonalization, and a lack of personal accomplishment. Meir (1983) described burnout as a “state in which individuals expect little reward and considerable punishment from work because of a lack of valued reinforcement, controllable outcomes, or personal competence” (p. 899). More recently, Osborn (2004) noted similar elements when describing burnout as “the process of physical and emotional depletion resulting from conditions at work or, more concisely, prolonged job stress” (p. 319). Given the myriad stressors inherent in any therapeutic endeavor, it is not surprising that the phenomenon of burnout has generated considerable interest among counseling researchers within the past decade (Leiter & Harvie, 1996; Vredenburgh, Carlozzi, & Stein, 1999). Nor does it seem unusual to find empirical evidence that burnout exists among professional counselors (Leiter & Harvie, 1996) or studies that explore the emotional, physical, and occupational implications of counselors who are vulnerable to burnout (e.g., Osborn, 2004; Thompson, 1999).
Although most burnout studies concerning counselors have focused primarily on correlates such as characteristics, work environments, and client attributes (e.g., Vredenburgh et al., 1999), research examining specific burnout typologies experienced among professional counselors remains limited. More specifically, earlier studies tended to consider counselor burnout as a homogenous phenomenon with a predictable and consistent set of symptoms.
Recently, Loo (2004) used the multidimensional construct of burnout to examine burnout types in police officers. Loo’s study, the first to use a clustering methodology to identify burnout types, yielded three different patterns: laissez-faire managers, well-adjusted managers, and distressed managers. Results from this study revealed three specific burnout types among police officers, opening the door to the development of specialized policies and programs that address the unique burnout patterns among individual officers. In a similar way, a system of classifying burnout patterns among professional counselors could generate specific subtypes of burnout that indicate counselors at risk for burnout. Such a classification system could also serve as the catalyst for programs aimed at both the prevention of burnout and, when necessary, the alleviation of burnout among professional counselors.
The majority of the current research on burnout uses the Maslach Burnout Inventory-Human Services Survey (MBIHSS; Maslach & Jackson, 1984). Although the MBI-HSS provides some insight regarding counselor burnout, it falls short in accurately assessing burnout specifically related to counselors. Therefore, the present study assesses counselor burnout with the newly developed Counselor Burnout Inventory (CBI; Lee et al., 2007). In this study, MBI-HSS was used to compare the patterns derived from the CBI. The CBI provides norm-referenced measures of a counselor’s burnout syndrome on five factorially derived burnout dimensions: Exhaustion, Incompetence, Negative Work Environment, Devaluing Client, and Deterioration of Personal Life. Unique to this inventory is its focus on the counselor’s work environment. This unique component corresponds with recent counseling burnout literature that accentuates the role one’s workplace environment plays in promoting burnout (Azar, 2000; Maslach, 2005; Osborn, 2004; Savicki & Cooley, 1981; Thompson, 1999).
To date, the interpretation of a CBI score has been based on the elevations of individual subscales. However, pattern-based interpretation with cluster analysis may increase the utility of CBI scores by capturing potential interactive effects inherent in score patterns. For example, counselors experiencing exhaustion or who have begun devaluing the client as a defensive measure against burnout may, in fact, differ in their ability to manage their overall burnout levels on the basis of how receptive or negative they perceive their work environment to be. Thus, the CBI’s ability to provide more detailed burnout profiles could facilitate the development of preventative programming or current interventions that could more quickly and accurately address the counselor’s needs. If these different clustering and counselor burnout patterns are found such information could be used by support services to help professional counselors cope effectively with the occupational stresses of counseling.
*Purpose of Study
The purpose of the present study is to determine specific burnout typologies among professional counselors. First, because burnout is a multidimensional construct, it is posited that counselor burnout would display a multiple-cluster structure rather than a single clustering or type. Second for cross-validation purposes, we hypothesized that the identified clusters would be consistent with the existing burnout measure, that is, the MBI-HSS. Third because there are several known demographic variables linked to burnout, we categorized demographic variables that appear to best ^scriminate between the clusters. Finally, because previous studies (Cordes & Dougherty, 1993; Gold & Michael, 1985; Vredenburgh et al., 1999) have related burnout to other psychological variables such as job satisfaction, self-esteem, and locus of control, we have identified the psychological variables that best contributed to the cluster differences.
* Method
Participants
Convenience sampling procedures were used to distribute 1 70 research packets at a state counseling association conference in the southeastern region of the United States. After excluding incomplete packets, 1 32 of 1 70 research packets were included in the statistical analysis. The sample included counselors with a wide range of specialties. Nine percent were family counselors, 43.2% were school counselors, 25.3% self-identified as mental health counselors, 7.6% were college counselors, 4.1% were rehabilitation counselors, 1.5% self-identified as career counselors, and 9.3% provided multiple responses. The years of experience ranged from 1 year to 33 years (M = 11.31,5!D= 8.37). Women made up the majority of the sample (83.3% women vs. 16.7% men). Regarding ethnicity, 94.7% of the participants were Caucasian, 3% were African American, 1.5% were Hispanic, and 0.8% provided multiple responses. Counselors’ ages ranged from 25 years to 67 years (M= 46.20, SD= 11.37).
Measures
CBI. The CBI consists of 20 items that are divided into five subscales: Exhaustion (e.g., “I feel exhausted due to my job as a counselor”), Incompetence (e.g., “I do not feel like I am making a change in my clients”), Negative Work Environment (e.g., “I feel frustrated with the system in my workplace”), Devaluing Client (e.g., “I am not interested in my clients and their problems”), Deterioration in Personal Life (e.g., “My relationships with family members have been negatively impacted by my work as a counselor”). Each item has a 5point response scale (1 = never true, 5 = always true). The CBI contains items reflecting characteristics of feelings and behaviors that would indicate various levels of burnout. Lee et al. (2007) reported that alpha coefficients of scores were .80 for the Exhaustion, .83 for the Negative Work Environment, .83 for the Devaluing Client, .81 for the Incompetence, and .84 for the Deterioration in Personal Life subscales. Support for construct validity was obtained through exploratory factor analysis that identified a five-factor solution and confirmatory factor analysis with all goodness-of-fit indexes also indicating an adequate fit to the data (Lee et al., 2007). In the present study, Cronbach’s alpha coefficients of scores were .85 for the Exhaustion, .83 for the Negative Work Environment, .80 for the Devaluing Client, .73 for the Incompetence, and .78 for the Deterioration in Personal life subscales.
MBI-HSS. The MBI-HSS (Maslach & Jackson, 1981) was designed to measure hypothesized aspects of the burnout syndrome. The MBI contains 22 statements of job-related feelings and asks participants to rate the frequency of the statements (0 = never, 6 = every day). The MBI consists of three subscales: Emotional Exhaustion (e.g., “I feel used up at the end of the workday”), Depersonalization (e.g., “I do not really care what happens to some recipients”), and Personal Accomplishment (e.g., “I have accomplished many worthwhile things in this job”). The reliability and validity of the MBI-HSS are well established (Maslach, Jackson, & Leiter, 1996). According to Maslach et al. (1996), reliability coefficients for each of the subscale scores are .90 for Emotional Exhaustion, .79 for Depersonalization, and .71 for Personal Accomplishment. Convergent validity has been established using three sets of correlations (Maslach et al., 1996). In the present study, reliability coefficients for each of the subscale scores were .89 for Emotional Exhaustion, .69 for Depersonalization, and .75 for Personal Accomplishment.
Job satisfaction. Seven items of job satisfaction that were identified in the National Educational Longitudinal Study (see National Center for Educational Statistics [NCES], 2002) were used in this study. A seven-item scale of job satisfaction, derived from items identified in the National Educational Study, was used in this study. The scale also measured satisfaction with fringe benefits, opportunities for further training, job security, opportunities for promotion, opportunities to use past training, importance and challenge of the work, and payment (NCES, 2002). According to Nguyen, Taylor, and Bradley (2003), the logit regression indicated that overall job satisfaction was highly significantly related to all individual domains of job satisfaction. Participants were asked to rate, using a Likert-type scale ( 1 = very dissatisfied, 5 = very satisfied), how satisfied they were with their jobs. In the present study, internal consistency for the scores of all seven items was .81, which suggests a high degree of consistency across items.
Self-esteem. The Rosenberg Self-Esteem Scale (Rosenberg, 1 965) was developed in an attempt to achieve a unidimensional measure of global self-esteem. Even though the scale was developed 40 years ago, continued use of this scale provides evidence of its reliability and validity (Vacha-Hasse, Kogan, & Thompson, 2000). According to Owens (2001), the Rosenberg Self-Esteem Scale is the most widely used measure of selfesteem. Items on the scale are rated on a 5-point Likert scale ranging from strongly disagree to strongly agree. Originally developed for the adolescent population, the scale has a Guttman scale reliability coefficient of .92 among youth. It has, however, been useful for assessing self-esteem in a variety of other groups (Mental Health Statistics Improvement Program, 1 996), with test-retest correlations in the range of .82 to .88 and Cronbach’s alphas for various samples in the range of .77 to .88 (Rosenberg, 1986). In the present study, internal consistency for the scores of all seven items was .73, suggesting a moderately high degree of consistency across items.
Locus of control. Locus of control represents the extent to which students feel they have control over their life (Rotter, 1966). A person with a high (internal) locus of control feels that he or she makes things happen in life, whereas a person with a low locus of control believes that luck or someone or something else is responsible for what happens to him or her. Items on the scale are rated on a 4-point Likert-type scale that ranges from strongly agree (1) to strongly disagree (4). Rotter’s (1966) Locus of Control Scale (LOC) has demonstrated adequate test-retest reliability (ranging from .49 to .83) and coefficient alphas (ranging from .67 to .87) in various studies (Marsh & Richards, 1986; Phillips & Gully, 1997; Rotter, 1966). Construct validity for the LOC has also been established in a variety of studies (Collins, 1974; Marsh & Richards, 1987; Rotter, 1966). Internal consistency of LOC scale scores in the present study was .68, which suggests a moderate degree of consistency across items.
*Results
Cluster Analysis
First, the five CBI subscale scores were standardized (T score; M= 50, SD = 1 0). Using a hierarchical agglomerative method with Ward’s minimum variance approach and a line chart from the coefficients of the agglomeration schedule table, we identified the optimal number of clusters. This three-cluster solution was most representative of this sample because of the meaningful interpretability of the clusters and the clear separation of the group centroids on the CBI subscales. As shown in Figure 1, the first cluster (n = 51, 38.6%) was characterized by low scores on all subscales (Exhaustion, Incompetence, Negative Work Environment, Devaluing Client, and Deterioration in Personal Life). Graphically, the shape is best described as a flat line below the means. This cluster was labeled well-adjusted counselors (WAC) because of the counselors’ low scores on all burnout subscales.
The second type of cluster was characterized by medium scores on the Exhaustion, Negative Work Environment, and Deterioration in Personal Life subscales, with relatively high Incompetence and high Devaluing Client scores. Most notable was the Devaluing Client score, which was a full standard deviation above the mean. About 33% (n = 44) of the cases fit this cluster pattern. Counselors grouped in this cluster seemed to be disconnected counselors (DC), that is, counselors appeared to be not particularly exhausted, but to have depersonalized from their clients and were unresponsive to their clients’ needs (Savicki & Cooley, 1981). The third cluster type consisted of high Exhaustion, Negative Work Environment, and Deterioration in Personal Life scores and moderate to low scores on the Incompetence and Devaluing Client subscales. This resulted in a W-shaped configuration with 37 (28.0%) of the cases. This cluster was labeled persevering counselor (PC) because these counselors had the highest Exhaustion, Negative Work Environment, and Deterioration in Personal Life scores, while reporting moderate to low Incompetence and Devaluing Client scores. In other words, PCs appeared to be flexible and responsive to client needs even when they experienced emotional and physical exhaustion in their workplace and personal life.
Relationship Among the MBI-HSS, Demographic Variables, and Outcome Variables
Table 1 lists the means and standard deviations for each cluster group on MBI-HSS subscales, demographic variables (i.e., gender, annual income, years of counseling experience, marital status, and age), and outcome variables (job satisfaction, selfesteem, and locus of control). Descriptive discriminant analysis (DDA) was used to identify the variables that contribute to group separation. In examining the canonical discriminant functions, there was a large canonical correlation (.677) on Function 1 with an effect size of R^sup 2^= 45.8%. There was a second large canonical discriminant (.495) on Function 2 with an effect size of R2 = 24.5%. Both the full model test of Function 1 to 2 (Wilks’s lambda = .409, χ^sup 2^^sub (22)^ = 88.56,/? < .00 1) and the test of Function 2 (Wilks’s lambda= .755, χ^sup 2^^sub (10)^ = 27.86,/? < .001) were statistically significant. Standardized discriminant function coefficients and structure coefficients were examined to determine what variables contributed to the group differences. Table 1 represents both sets of coefficients for all analyses.
First, in light of the quantity of research that has been done on the MBI-HSS, the CBI clusters were compared with the MBIHSS subscales. Although the Emotional Exhaustion subscale of the MBI-HSS was primarily responsible for group differences on Function 1 , the Depersonalization and Personal Accomplishment subscales of the MBI-HSS were primarily responsible for group differences on Function 2, with Depersonalization being negatively related to Personal Accomplishment. Regarding the group centroids (see Table 2), it appears that on Function 1, Cluster 1 had the lowest centroids (-1 .03), followed by Cluster 2 (0.20) and Cluster 3(1.17). This indicated that counselors who belonged to Cluster 1 (WACs) were less exhausted than Cluster 2 counselors (DCs) and even more so when compared with Cluster 3 counselors (PCs). On Function 2, Cluster 2 had the lowest centroids (-.81), followed by Cluster 1 (.29) and Cluster 3 (.50). This indicated that counselors who belonged to Cluster 2 (DCs) were more depersonalized and less accomplished than Cluster 1 counselors (WACs) and even more so when compared with Cluster 3 counselors (PCs).
Next, we examined whether the three counselor burnout types were related to known demographic determinants (gender, annual income, years of counseling experience, marital status, and age). The ethnicity variable was not included because of the low rate of minority counselor responses (5.3%). As shown in Table 1 , although no demographic variables were found to be responsible for group differences on Function 1, the annual income variable was somewhat responsible for group differences on Function 2 ( 1 8.9%). Consistent with the results from group centroids (see Table 2), the W-shaped Cluster 3 (PCs) reported the highest income ($45,771) followed by WACs ($43,693) and DCs ($39,074). Even though small group differences (7.7%) were found for years of counseling experience, it warrants mentioning that PCs (M= 13.15, SD = 7.73) were more experienced than WACs (M= 9.89, SD = 8.95) and DCs (M= 10.31, SD = 8.13).
Finally, we examined if the three counselor burnout types were associated with the three psychological variables of job satisfaction, self-esteem, and locus of control. DDA revealed that job satisfaction (20.3%) was primarily responsible for group differences on Function 1 , and self-esteem (30.5%) was primarily responsible for group differences on Function 2. The Function 1 at group centroids indicated that Cluster 1 counselors (WACs) were more satisfied with their job than were DCs (Cluster 2) and even more so when compared with PCs (Cluster 3). In contrast, the results revealed that DCs (Cluster 2) had lower scores on self-esteem than did Cluster 1 counselors (WACs) and even lower scores than Cluster 3 (PCs) participants had.
*Discussion
Although previous research has tended to consider counselor burnout as a homogenous phenomenon with unitary and global terms (Farber, 1998), new research (Loo, 2004) indicates the value of using a system of classifying burnout that is based on the patterns (types or profiles) that reflect the more consistent elements of burnout. According to Loo (2004), researchers can use the patterns derived from a cluster analysis to determine a treatment plan to prevent burnout. On the basis of our research, we identified distinct patterns of counselor burnout that differentially influence counselor’s self-esteem, job satisfaction, and locus of control.
The most common CBI type was indicated by a relatively flat profile that was characterized by low scores on all subscales (Exhaustion, Incompetence, Negative Work Environment, Devaluing Client, and Deterioration in Personal Life). In light of both its form and its frequency, this is probably best thought of as a common profile. This cluster was labeled WACs because of these individuals’ low scores on all burnout subscales. Specifically, counselors fitting this profile scored the lowest on the Depersonalization and Emotional Exhaustion subscales of the MBI-HSS and received the highest scores on the Personal Accomplishment scale of that measure. They also reported the second highest income ($43,693).This group of counselors reported the highest job satisfaction. They also reported more positive self-esteem than did counselors in Cluster 2. This finding was consistent with previous studies that support the effect of burnout in helping professions (Osborn, 2004; Thompson, 1999). Consistent with the findings of previous studies (e.g., Maslach & Jackson, 1 98 1 ), the results of the present study indicated that counselors who self-identified as not experiencing burnout were found to have the highest scores on job satisfaction and higher positive self-esteem.
The second profile (Cluster 2) was the most distinctive and was characterized by counselors’ medium scores on subscales assessing exhaustion, negative work environment, and deterioration in personal life with relatively high Incompetence and high Devaluing Client scores. The Devaluing Client score was almost a full standard deviation above the mean. Counselors in this burnout profile seemed to be DCs. Consistent with this notion is the finding that the counselors in this profile had higher scores on the Depersonalization scale of the MBI-HSS than did counselors in Cluster 2 (WACs) and Cluster 3 (PCs). It is also interesting to note that DCs also reported the lowest income ($39,074), lowest job satisfaction, and the worst self-esteem.
The final cluster was a W-shaped profile characterized by high Exhaustion, Negative Work Environment, and Deterioration in Personal Life scores and moderate to low scores on the Incompetence and Devaluing Client subscales. This cluster was labeled PC because these counselors had the highest Exhaustion, Negative Work Environment, and Deterioration in Personal Life scores, but reported moderate to low Incompetence and Devaluing Client scores. Counselors with this profile tended to be flexible and responsive to the clients’ needs, even when reporting emotional and physical exhaustion in their workplace and personal life. It is also intriguing to note that the PCs also reported the highest income, more counseling experience, and the most positive self-esteem even though they seemed dissatisfied with their current job. This is consistent with Lee et al.’s (2007) recent finding that indicates the Incompetence subscale of the CBI is a better predictor of self-esteem than are other subscales of the CBI.
Implications for Counselors
The burnout typologies identified in the present study extend the notion of burnout as a multidimensional construct (Friedman, 1996). Awareness of a counselor’s unique burnout profile could offer significant assistance in uncovering both individual and environmental contributors to burnout and offer assistance in devising specific preventative strategies. The CBI typologies could then be used to develop dedicated preventative strategies or, in the cases of existing burnout symptoms, aid in the development of personalized interventions that align with the contours of the counselor’s unique burnout profile. Additionally, targeted interventions that are based on the counselor’s unique CBI profile could facilitate the development of “a setting in which the needs of the caregiver are as carefully nurtured as those of the recipients” (Färber, 1998, p. 13). In short, insights gleaned from CBI profiles could be used strategically and/or longitudinally as a means for developing specific interventions as well as dedicated support service programs targeting the counselor’s total wellness. In doing so, counselors are better able to maintain their focus on the client and ultimately, provide opportunities for positive therapeutic outcomes.
The CBI typologies could also be of significant pedagogical value. For example, the WAC profile, the most commonly found counselor burnout type, provides a composite sketch of the interplay between personal and professional variables among counselors not experiencing burnout. In effect, the WAC profile is indicative of counselors who have acquired the skills that allow them to balance a range of personal and professional issues while remaining attentive to client needs. Additionally, building on Pines and Aronson’s (1988) suggestion of becoming “aware of the problem” (p. 27) leading to burnout, WACs could facilitate informal discussions (e.g., brown bag lunches) with colleagues that could prove advantageous to preventing or alleviating burnout symptoms. Insights shared by the WAC may offer a more pragmatic alternative to structured workshops or seminars that, while important, likely lack insight into the nuances of individual agency cultures and/or interpersonal relationships. Essentially, the WAC and other CBI typologies could help address particular organizational and/or interpersonal conflict, thereby extending Pines and Aronson’s notion of enhancing one’s “degree of cognitive complexity” (p. 27) of their organization and using support groups to alleviate stress that could lead to counselor burnout (Brashear, 1987; Spicuzza & DeVoe, 1982).
The CBI profiles of DCs and PCs also appear to have considerable professional, personal, and instructional value for counselors, supervisors, and organizations. For instance, DCs in this study did not appear to be experiencing excessive exhaustion or a significant deterioration in their personal life. However, elevated levels of perceived incompetence and a distressingly high level of devaluing clients are also seen in this DC profile. This pattern of devaluing clients could be symptomatic of compassion fatigue, defined historically as secondary traumatic stress disorder (Figley, 1995). In other words, the high degree of empathy provided by counselors within the context of the therapeutic environment may increase their vulnerability to compassion fatigue. As a defense, counselors may devalue parts, or in more severe cases much, of the client’s story. This weakened response to client needs could weaken the therapeutic alliance and may also diminish treatment efficacy.
Conversely, the DC profile also offers a unique opportunity to address a number of variables that could lead a counselor to adopt a distant or self-protective stance with clients. Interventions could be devised to address personal, professional, and environmental stressors that, either individually or together, impede the counselor’s ability to remain present with the client. For example, counselors may not immediately recognize the disconnect with a client’s story that could accompany devaluing the client’s story, especially if their conceptualization of burnout is focused on monitoring variables such as emotional or physical exhaustion or stressors connected to their personal lives. In such cases, the DC profile offers a link between several key dimensions of burnout while connecting these symptoms to the counselor’s ability to remain attentive to the client (Yu, Lee, & Lee, 2007).
In contrast to both the DC and the WAC profiles, PCs reported the highest degree of exhaustion, deterioration in their personal life, and negative work environment. It is compelling to note, however, that their scores on the Devaluing the Client subscale are consistent with the WAC counselor profile. Thus, the PC appears to have an aptitude for maintaining a solid therapeutic presence while remaining resolute in the face of myriad personal, professional, and environmental stressors. On the other hand the PC should continually assess this discrepancy to maintain personal health and appropriate professional boundaries. As is the case with the DC, specific interventions may address areas where it is clear that there is strain. However, PCs could also serve as a resource for colleagues, especially given their ability to provide effective therapeutic services even when experiencing significant occupational and personal stress.
Clinical supervisors and administrators could also benefit from the use of the CBI. Supervisors, for example, may be the first to hear the unabridged feelings or concerns of stressed counselors. In cases where scores reveal a DC or PC burnout pattern, personalized strategies could be developed. Supervisors could use the CBI as a supplemental supervisory tool to help address unresolved issues manifesting in supervision that could potentially result in professional impairment (see Standard C.2.g.; American Counseling Association, 2005). Thus, periodic use of the CBI by supervisors or administrators could be a valuable supplemental supervisory tool that could lead to productive discourse within the supervisory relationship (i.e., individual, triadic, or group).
Data gathered from the CBI may also provide a template for administrators to use when assessing the sense of well-being of their employees. Effective use of the CBI profiles could range from organizing professional seminars or workshops to employee recognition programs (i.e., programs dedicated to facilitating a supportive and psychologically healthy work environment). Overall, the CBI profiles could be integrated into a comprehensive supervisory and administrative stratagem for improving the professional experiences of counselors.
Limitations and Suggestions for Future Studies
Certain limitations inherent in the present research may have affected the outcome of this study. First, all measures were obtained by self-report questionnaires and participants were anonymous and self-selected. Counselors who may have experienced high levels of burnout might have been less motivated to participate in this study to avoid painful issues. Conversely, counselors who identified no burnout experience may have judged their participation in this study to have little impact and may have chosen not to participate. Future studies could use multiple measures (e.g., direct observation) to assess the burnout variable, thereby giving a clearer picture of the longterm effect of burnout in professional counselors.
Second by including only counselors from a geographically limited convenience sample, the conclusions are limited and may not be generalized to all counselors in the United States. Further research with counselors in other regions would allow the researchers to determine if significant similarities or differences exist in the counselor burnout profiles of the sample of the present study and future study populations. Despite these limitations, the approach of counselor burnout profiles, regardless of the modality, could help counselors increase their awareness of burnout and ultimately, its impact on their sense of personal well-being and professional efficacy. In this instance, the multidimensional burnout profiles broaden the understanding of counselor burnout, potentially leading to both an increase in counselor self-awareness and the opportunity to design interventions dedicated to the prevention and alleviation of counselor burnout.
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AuthorAffiliation
Sang Min Lee, Department of Education, Korea University, Seoul, Korea; Seong Ho Cho, Department of Psychology, The Catholic University of Korea, Seoul, Korea; Daniel Kissinger, Department of Rehabilitation, Human Resources and Communication Disorders, University of Arkansas; Nick T. Ogle, Department of Bible, John Brown University. The second, third, and fourth authors contributed equally to this article. Correspondence concerning this article should be addressed to Sang Min Lee, Department of Education, College of Education, Korea University, Anam-dong, Seongbuk-gu, Seoul, Korea (e-mail: leesang@korea.ac.kr).
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