Individual and Relational Predictors of Compassion Fatigue Among Counselors-in-Training

Nesime Can, Joshua C. Watson


Scholars have described compassion fatigue as the result of chronic exposure to clients’ suffering and traumatic stories. Counselors can struggle when they experience compassion fatigue because of various reasons. As such, an exploration of factors predictive of compassion fatigue may help counselors and supervisors buffer adverse effects. Utilizing a hierarchical linear regression analysis, we examined the association between wellness, resilience, supervisory working alliance, empathy, and compassion fatigue among 86 counselors-in-training (CITs). The research findings revealed that resilience and wellness were significant predictors of compassion fatigue among CITs, whereas empathy and supervisory working alliance were not. Based on our findings, counselor educators might consider enhancing their current training programs by including discussion topics about wellness and resilience, while supervisors consider practicing wellness and resilience strategies in supervision and developing interventions designed to prevent compassion fatigue.


Keywords: compassion fatigue, counselors-in-training, wellness, resilience, supervisory working alliance



Balancing self-care and client care can be a challenge for many counselors. When counselors neglect self-care, they can become vulnerable to several issues, including increased anxiety, distress, burnout, and compassion fatigue (Ray, Wong, White, & Heaslip, 2013). Counselors might be especially prone to experiencing compassion fatigue because they repeatedly hear traumatic stories and clients’ suffering in sessions (Skovholt & Trotter-Mathison, 2016). This phenomenon is likely pronounced among counselors-in-training (CITs), as lack of experience, skillset, knowledge, and support can lead to struggles when working with clients (Skovholt & Trotter-Mathison, 2016). Coupled with the increased anxiety, distress, and disappointment, CITs can experience compassion fatigue early in their career development, which can lead to exhaustion, disengagement, and a decline in therapeutic effectiveness (Rønnestad & Skovholt, 2013). At this developmental stage, negative experiences can lead to feelings of doubt and a lack of confidence among CITs and potentially lead to career dissatisfaction. Therefore, it is essential and necessary to better understand the predictive factors of compassion fatigue among CITs to prevent its early onset.


Compassion Fatigue in Counseling


Counselors listening to their clients’ fear, pain, and suffering can feel similar emotions. Figley (1995) defined this experience as compassion fatigue; it also can be defined as the cost of caring (Figley, 2002). Whether working in mental health agencies, schools, or hospital settings, counselors experience compassion fatigue because of exposure to large caseloads, painful stories, and lack of support and resources (Skovholt & Trotter-Mathison, 2016). Despite this exposure, counselors are expected to place their personal feelings aside and provide the best treatment possible in response to the presenting issues and needs of their clients (Figley, 2002; Ray et al., 2013; Turgoose, Glover, Barker, & Maddox, 2017). Maintaining this sense of detached professionalism has its costs, as a number of counselors find themselves at risk for experiencing physical, mental, and emotional exhaustion, as well as feelings of helplessness, isolation, and confusion—a situation collectively referred to as compassion fatigue (Eastwood & Ecklund, 2008; Thompson, Amatea, & Thompson, 2014).


Merriman (2015b) stated that ongoing compassion fatigue negatively impacts counselors’ health as well as their relationships with others. Additionally, compassion fatigue can lead to a lack of empathy toward clients, decrease in motivation, and performance drop in effectiveness, making even the smallest tasks seem overwhelming (Merriman, 2015b). When this occurs, counselors can project their anger on others, develop trust issues, and experience feelings of loneliness (Harr, 2013). Therefore, the demands of the counseling profession can affect many counselors’ wellness and potentially could hurt the quality of client care provided (Lawson, Venart, Hazler, & Kottler, 2007; Merriman, 2015a). Further, counselors experiencing compassion fatigue might have difficulties making effective clinical decisions and potentially be at risk for harming clients (Eastwood & Ecklund, 2008). Consequently, scholars appear to agree that compassion fatigue is an occupational hazard that mental health care professionals need to address (Figley, 2002; Merriman, 2015a).


Factors Associated With Compassion Fatigue


Many researchers have studied the relationships between compassion fatigue and various constructs, such as empathy, gender, mindfulness, support, and wellness (e.g., Beaumont, Durkin, Martin, & Carson, 2016; Caringi et al., 2016; Ray et al., 2013; Sprang, Clark, & Whitt-Woosley, 2007; Turgoose et al., 2017). Researchers conducted most of these studies among novice and veteran mental health professionals. Scant research among CITs exists. Our research attempts to fill this gap by exploring factors affecting CITs given their unique position as both students and emerging professionals. The following review of the literature supports the inclusion of predictor variables used in this study.


Empathy and Compassion Fatigue

One of the most widely studied concepts across various cultures is empathy, as it has been determined to be one of the major precipitants of compassion fatigue (Figley, 1995). However, findings in the literature regarding the association between compassion fatigue and empathy remain mixed (e.g., MacRitchie & Leibowitz, 2010; O’Brien & Haaga, 2015; Wagaman, Geiger, Shockley, & Segal, 2015). For instance, O’Brien and Haaga (2015) compared trait empathy and empathic accuracy with compassion fatigue after showing a videotaped trauma self-disclosure among therapist trainees (a combined group of advanced and novice graduate students) and non-therapists. The results indicated that there was no significant association between participants’ levels of compassion fatigue and empathy scores. However, MacRitchie and Leibowitz (2010) found a significant relationship between compassion fatigue and empathy after exploring the relation of these variables on trauma workers whose clients were survivors of violent crimes. The mixed results of these previous studies suggest further research is needed to understand better the relationship between empathy and compassion fatigue and how this relationship impacts counseling practice.


Supervisory Working Alliance and Compassion Fatigue

Although reviewed literature addressed studies suggesting supervision and support are related factors to compassion fatigue, research on this relationship is still insufficient. Kapoulitsas and Corcoran (2015) conducted a study and found that a positive supervisory relationship has a significant role in developing resilience and reducing compassion fatigue among counselors. Knight (2010) also found that students uncomfortable talking with their supervisor reported a higher risk for developing compassion fatigue. Additionally, organizational support appears to reduce compassion fatigue, whereas an absence of support increases practitioners’ and interns’ risk of developing compassion fatigue symptoms (Bride, Jones, & MacMaster, 2007). Given the intense need for support and guidance CITs need during their initial work with clients, it is expected that those students who do not actively work with their supervisors can struggle and be more vulnerable for compassion fatigue.


Wellness, Resilience, and Compassion Fatigue

Although counselors are encouraged to practice self-care activities to continue to enhance personal well-being (American Counseling Association [ACA], 2014; Coaston, 2017; H. L. Smith, Robinson, & Young, 2008), not all CITs can balance caring for self and others. When CITs do not receive training in the protective factors for compassion fatigue, they risk becoming more vulnerable to violating the ACA code of ethics (Merriman, 2015a; Merriman, 2015b). Kapoulitsas and Corcoran (2015) and Skovholt and Trotter-Mathison (2016) highlighted the importance of resilience and self-care activities as protective factors for compassion fatigue. Wood et al. (2017) evaluated the effectiveness of a mobile application called Provider Resilience to reduce compassion fatigue scores of mental health professionals. After a month of utilization, the results indicated that the application was effective in reducing compassion fatigue. Additionally, Lawson and Myers (2011) conducted a study with professional counselors to examine counselor wellness about compassion fatigue and found a negative correlation between total wellness scores and compassion fatigue scores. As CITs balance academic, family, and work demands, the probability of decreased wellness and a corresponding increase in compassion fatigue exists.


Compassion Fatigue Among CITs


Most CITs are often unable to master all counselor competencies (Rønnestad & Skovholt, 2013), and therefore they might not know how to deal with possible stressors and the emotional burden of their work (Star, 2013). Although they are learning counseling skills to provide the best care possible to clients, CITs may find themselves working with seriously troubled or traumatized clients without obtaining quality supervision and support (Skovholt & Trotter-Mathison, 2016). Lack of skills and resources increases the likelihood of CITs developing compassion fatigue (Merriman, 2015b). However, there is a lack of focus in compassion fatigue education on preparing CITs to manage compassion fatigue symptoms (Merriman, 2015a). Although scholars have examined compassion fatigue among counselors, there is still a dearth of studies investigating the level of compassion fatigue among CITs and addressing its protective factors within this population (Beaumont et al., 2016; Blount, Bjornsen, & Moore, 2018; Thompson et al., 2014). Subsequently, further research is needed to understand better potential protective factors that can be enhanced to offset the negative impact of compassion fatigue on CITs and the counseling process. Thus, with this study, we aimed at assessing the relationship between resilience, wellness, supervisory working alliance, empathy, and compassion fatigue among CITs in the United States. To accomplish this goal, we sought to answer the following research questions: (1) What is the prevalence of compassion fatigue among CITs? and (2) Do empathy, supervisory working alliance, resilience, and wellness significantly predict levels of compassion fatigue among CITs?





Participants recruited for this study consisted of master’s-level counseling students who are at least 18 years of age and enrolled in an internship course in the United States through mostly professional listservs (e.g., Counselor Education and Supervision Network Listserv [CESNET-L], Texas Association for Counselor Education and Supervision Network Listserv [TACESNET-L], Counseling Graduate Student Network [COUNSGRADS]). Because of the impossibility of knowing how many individuals received the email invitation, we were unable to calculate and determine a response rate. Accordingly, a total of 114 CITs initially agreed to participate in this study. Before data analysis, we inspected the data set for possible entry errors and missing data. After the inspection, we excluded 28 participants from all subsequent data analyses resulting in the reduced sample of 86 CITs used to address our research questions. Overall, the sample consisted of 78 female (90.7%) and eight male (9.3%) participants, and the mean age of the participants was 32.89 years (SD = 9.72) with participants’ ages ranging between 21 and 62 years. Participants were from diverse ethnic and racial backgrounds, with the sample consisting of White (n = 48, 55.8%), Hispanic/Latino (n = 18, 20.9%), Black/African American (n = 12, 14.0%), and Asian (n = 5, 5.8%) CITs. Three participants (3.5%) listed their ethnicities as “other” when providing demographic information. Participants reported their program enrollment as follows: clinical mental health counseling program (n = 47, 54.7%); school counseling program (n = 23, 26.7%); marriage, couple, and family counseling program (n = 4, 4.7%); college counseling and student affairs program (n = 3, 3.5%); addiction counseling program (n = 2, 2.3%); and other programs (n = 7, 8.1%). Additionally, most of the participants (n = 73, 84.9%) reported enrollment in a CACREP-accredited program with the remaining participants (n = 13, 15.1%) enrolled in a non–CACREP-accredited program.



Upon receiving institutional review board approval, we recruited participants from different institutions with the primary researcher contacting professional colleagues at various departments to disseminate the online survey link to potential participants during the 2017 summer and fall semesters. We also recruited participants through professional listservs (e.g., CESNET-L, TACESNET-L, COUNSGRAD), with listserv participants being provided the same informed consent and survey link through a secure website. The survey completion process took approximately 15–20 minutes.



We used the following self-administered survey questionnaires and a separate demographic data sheet in our data collection.


     Professional Quality of Life Scale (ProQOL). This scale is designed to measure the mental and emotional consequences of working with individuals who experienced trauma or painful events (Stamm, 2010). The ProQOL includes two main traits, Compassion Satisfaction (positive) and Compassion Fatigue (negative). Compassion Satisfaction is related to the joy individuals develop when they do their work well (Stamm, 2010). Compassion Fatigue consists of two subscales: Secondary Traumatic Stress (STS) and Burnout. Scholars have defined STS as an emotional state that occurs when an individual becomes upset or traumatized as a result of their exposure to victim experiences (Figley, 2002). The second part of Compassion Fatigue is Burnout, which is a multidimensional syndrome related to the social work environment. Burnout could be related to work overload, lack of control, insufficient rewards, unfairness, and value conflict at a workplace (Skovholt & Trotter-Mathison, 2016).


The ProQOL is a 30-item Likert-type self-report assessment with responses of never, rarely, sometimes, often, and very often for each item. A sample item is “I feel depressed because of the traumatic experiences of the people I [help].” This assessment has 10 questions per each of three main scales measuring separate constructs. However, the Compassion Fatigue scale includes two of these constructs, which are the Burnout and the STS scales. According to Stamm (2010), the ProQOL has good construct validity, as researchers have noted its efficacy in over 200 published articles. Finally, alpha coefficient values for the Burnout and STS scales were .75 and .81, respectively (Stamm, 2010), and are similar (.72 and .79) to the Cronbach’s alpha values from the current study presented in Table 1.


Table 1

Descriptive Statistics of the Study Variables (N = 86)

Variable M SD Min Max Skew α
Compassion Fatigue 41.48 8.03 22 60    .19
BO 21.34 4.38 12 32 .72
STS 20.14 4.96 10 38 .79
Empathy 21.86 4.12   9 28   -.51 .80
Supervisory Working Alliance   5.82   .97   2.16   7  1.26
CF   6.65 1.30   2.17   8.17 .90
R   5.80   .96   2.33   7 .93
Resilience   3.43   .79   1   4.67   -.74 .89
Wellness 47.58 6.23 27 56 -1.39 .86

Note. BO = Burnout; STS = Secondary Traumatic Stress; CF = Client Focus; R = Rapport



     Interpersonal Reactivity Index (IRI). Davis (1983) developed the IRI to measure the reactions of a person to other individuals’ observed experiences. The 28-item instrument has four subscales: Empathic Concern, Perspective Taking, Fantasy, and Personal Distress (Davis, 1983). Researchers report separate subscale scores, as a total score for the instrument has not been recommended (Davis, 1983). In this study, we only used the Empathic Concern subscale to collect data regarding empathy scores of CITs.


Davis (1983) described empathic concern as an emotional response, such as compassion and sympathy, to someone else in need. The 7-item subscale is a self-report assessment with a 5-point Likert-type scale, ranging from Does not describe me well to Describes me very well. A sample item is “I am often quite touched by things that I see happen.” An alpha coefficient of .77 has been reported for the Empathic Concern subscale (Péloquin & Lafontaine, 2010), while the Cronbach’s alpha value of the IRI in the current study was .80.


     Supervisory Working Alliance Inventory: Trainee Form (SWAI-T). Efstation, Patton, and Kardash (1990) developed this inventory to measure supervisees’ perceptions about the effectiveness of the working relationship with their supervisors, and we used the SWAI-T to measure the construct of the supervisory working alliance. With a total of 19 items, the self-report assessment includes a 7-point Likert-type scale with responses ranging from almost never to almost always. A sample item is “When correcting my errors with the client, my supervisor offers alternative ways of intervening with the client.” The SWAI-T has two subscales—Client Focus and Rapport—and the Cronbach alpha coefficients of these scales were .77 and .90, respectively (Efstation et al., 1990). For the current study, we calculated Cronbach alpha values of .90 for the Client Focus subscale and .93 for the Rapport subscale. Because some researchers have found high correlations between these two subscales, they decided to combine them in their studies (e.g., Ganske, 2007; White & Queener, 2003). Therefore, in this study, after conducting a correlation analysis with the subscale scores, we also chose to combine subscales as the results of subscale scores were highly correlated.


     Brief Resilience Scale (BRS). The BRS was developed to measure a person’s ability to recover from stress and cope with challenging situations (B. W. Smith et al., 2008). The BRS is used to measure the construct of resilience. As a 6-item self-report assessment, the BRS includes a 5-point Likert-type scale with responses ranging from strongly disagree to strongly agree. A sample item is “I usually come through difficult times with little trouble.” B. W. Smith and colleagues (2008) reported that the Cronbach’s alpha values of the BRS range from .80 to .91, and we calculated a Cronbach alpha of .89 for the current study.


     Flourishing Scale (FS). The FS was designed to measure individuals’ self-perceived success in areas like optimism and relationships (Diener et al., 2010) and used to measure the construct of wellness in this study. The FS is an 8-item self-report assessment with a 7-point Likert-type scale with responses ranging from strongly disagree to strongly agree (Diener et al., 2010). A sample item is “I lead a purposeful and meaningful life.” Diener and colleagues (2010) reported moderately high reliability with a .87 Cronbach’s alpha coefficient, and in the current study, the FS had a Cronbach alpha of .86.


Data Analysis

     Statistical power analysis. We used an a priori type of the G*Power to set the minimum number of participants needed to detect statistical power for this research design. Based on an alpha of .05, a power level of .90, and four predictors (Faul, Erdfelder, Buchner, & Lang, 2009), the computation results suggested that a minimum of 73 participants was required to detect statistical significance with at least a moderate size effect (.15). We had 86 participants, suggesting adequate power.


     Preliminary analyses. We analyzed all data using the Statistical Package for the Social Sciences, Version 20 (SPSS; IBM Corporation, 2011). Before addressing our stated research questions, we cleaned the dataset and addressed missing data. We did not observe any pattern between missing data points. Therefore, the type of missing data was completely random, which was addressed using the series of mean function within the SPSS. Next, we calculated descriptive statistics and alpha coefficients for each scale used in the study (see Table 1). Before performing hierarchical regression analyses, we tested all associated model assumptions. First, we examined study variables based on their types and concluded each utilized a continuous scale. We then assessed normality with the Shapiro-Wilk test of normality (W > .05), indicating data was normally distributed for the dependent variable. To identify outliers, we examined boxplots. Although there were a few mild outliers, no extreme scores were detected. We assessed linearity and homoscedasticity through inspection of standardized residual plots. To assess for the assumption of multicollinearity, we examined the correlation matrix of study variables to determine if any correlated highly. According to Field (2013), correlations above .80 are considered high and may indicate the presence of multicollinearity. In the present study, none of the correlation coefficients were above .50 (see Table 2). Collectively, these findings indicated no evidence suggesting any of the model assumptions had been violated. As a result, the dataset was deemed appropriate for analysis using a hierarchical regression design.


     Primary analysis. Descriptive statistics were calculated to organize the data by producing means, mode, median, standard deviations, and minimum and maximum scores for the study variables (Field, 2013). Individually, we reviewed descriptive statistics for the compassion fatigue variable, and results were reported to address the first research question. Next, we performed a three-step hierarchical linear regression to address the second research question.


Table 2


Intercorrelations for Scores on the Study Variables

Variable 1 2 3 4 5
1. ProQOL-CF
2. SWAIT-T   .04
3. IRI-EC  -.06  .04
4. BRS    -.47** -.09 -.11
5. FS    -.45**  .12    .25* .35**

Note. N = 86; ProQOL = Professional Quality of Life (Compassion Fatigue [CF] subscale score is presented); IRI = Interpersonal Reactivity Index (Empathic Concern [EC] subscale score is presented); SWAI-T = Supervisory Working Alliance Inventory: Trainee Form; BRS = Brief Resilience Scale; FS = Flourishing Scale.

*p < .05.  **p < .01.






Compassion fatigue scores of CITs represent the sum of scores of all items on the STS and Burnout subscales. According to the ProQOL administration manual (Stamm, 2010), individuals scoring below 22 may indicate little or no issues with Burnout and STS, while scores between 23 and 41 indicate moderate levels of Burnout and STS, and scores above 42 indicate higher levels of Burnout and STS. For this sample, participants’ Burnout scores ranged from 12 to 32 with a mean of 21.34 (SD = 4.38), and STS scores ranged from 10 to 38 with a mean of 20.14 (SD = 4.96). These results indicated a low risk of both Burnout and STS among CITs.


To address the second research question, we performed a three-step hierarchical linear regression analysis. With this analysis, we aimed to assess the association between resilience, wellness, supervisory working alliance, empathy, and compassion fatigue. We chose to implement a hierarchical multiple regression analysis because scholars previously have highlighted the essential relationship between empathy, supervision, and compassion fatigue (Figley, 2002; MacRitchie & Leibowitz, 2010). In the first step, empathy scores entered the model as a predictor variable, as Figley (1995) stated that empathy is one of the main factors contributing to compassion fatigue. However, among this sample, we found that empathy was not a significant predictor of compassion fatigue: F(1, 84) = .2, p = .66 , R2 = .002 (adjusted R2 = -.01). Then, we added supervisory working alliance scores to the model in the second step, as both Knight (2010) and Miller and Sprang (2017) emphasized the importance of supervisory support for mental health practitioners. Results revealed that the supervisory working alliance variable also was not a significant predictor of compassion fatigue: F(2, 83) = .16, p = .85, R2 = .004 (adjusted R2 = -.02). In the third step, resilience and wellness scores were entered into the model to determine whether these variables significantly improved the amount of explained variance in compassion fatigue. Results showed that this combination of variables significantly predicted 26% of the variance in compassion fatigue: F(4, 81) = 8.57, p < .001, R2 = .30. Therefore, it was concluded that CITs with greater wellness and resilience reported developing less compassion fatigue (see Table 3).


Table 3

Hierarchical Regression Analysis Results for Variables Predicting Compassion Fatigue

Variables B SEB β R2 ΔR2
Step 1 .002 -.01
Empathy   -.09 .21 -.05
Step 2 .004 -.02
Empathy   -.10 .21 -.05
SWA    .33 .91  .04
Step 3 .30*  .26
Empathy   -.03 .19 -.02
SWA    .36 .78  .04
Wellness   -.39 .14  -.30*
Resilience  -3.66     1.05  -.36*

Note. SWA = Supervisory Working Alliance

*p < .05.





In this study, CITs reported having a low risk of compassion fatigue. When we examined the Burnout and STS scores separately, the main contributors of compassion fatigue (Stamm, 2010), both subscale scores indicated participants having a low risk for STS and Burnout. This finding is similar to results found by Beaumont and colleagues (2016) in their study of compassion fatigue, burnout, self-compassion, and well-being relationships among student counselors and student cognitive behavioral psychotherapists. According to their research findings, a total of 54 student participants reported high scores on self-compassion and well-being and reported less compassion fatigue and burnout (Beaumont et al., 2016).


One of the goals of this study was to seek understanding of whether wellness and resilience explain a statistically significant amount of variance in compassion fatigue among CITs after accounting for empathy and supervisory working alliance. The results indicated that empathy and supervisory working alliance were not significant predictors of compassion fatigue. Regarding empathy and compassion fatigue relation results, the findings of this study did not support Figley’s (1995) assumption of empathy as one of the main contributors to compassion fatigue. This result also is inconsistent with Wagaman and colleagues’ (2015) results indicating a significant association between empathy and compassion fatigue among social workers. However, current results aligned with those studies that found no correlation between empathy and compassion fatigue (e.g., O’Brien & Haaga, 2015; Thomas & Otis, 2010). An explanation of the variability between this inquiry and previous studies might lie with the difference between participants’ field of study and measurement differences. Also, none of the previous studies used CITs solely as their sample, nor used a similar way to measure the construct of empathy. Additionally, CITs would have less experience working with clients compared to experienced counselors, and thus less time for feelings of compassion fatigue to build.


Although scholars addressed the importance of supervision and supervisory working alliance to help prevent compassion fatigue (Kapoulitsas & Corcoran, 2015; Merriman, 2015a), this study’s results indicated supervisory working alliance was not a significant predictor of compassion fatigue among CITs. Like current results, Ivicic and Motta (2017) and Williams, Helm, and Clemens (2012) found no statistically significant association between supervisory working alliance and compassion fatigue among mental health practitioners. It is noteworthy that these studies highlighting the importance of supervision and the supervisory relationship are qualitative in design, and participants did not consist solely of CITs. Additionally, their results emphasized the importance of supervision as support to counter the negative impact of trauma exposure (Kapoulitsas & Corcoran, 2015; Ling, Hunter, & Maple, 2014). According to the current study results, CITs did not report experiencing a high level of compassion fatigue. This finding could be interpreted as CITs not yet feeling the need for supervisory support to help with compassion fatigue.


Results also indicated that resilience and wellness were significant predictors of compassion fatigue among CITs. In other words, when reflecting on both the regression and correlation results, CITs with greater resilience and wellness reported lower scores of compassion fatigue and these results were consistent with Tosone, Minami, Bettmann, and Jasperson’s (2010) research findings. Regarding a wellness and compassion fatigue relationship, Beaumont and colleagues (2016) conducted a study with student counselors and student cognitive behavioral psychotherapists. The results of Beaumont et al.’s study revealed that individuals with high scores of self-compassion and well-being reported having less compassion fatigue and burnout. Thomas and Morris (2017) also highlighted the significance of self-care and well-being not only for preventing and helping to manage the potentially damaging impact of practice, but also for facilitating the counselor’s personal and professional growth.


Implications for Counselor Educators and Supervisors


The research findings provide data-driven results regarding compassion fatigue among CITs that have meaningful implications for counselor educators and supervisors. Present study results revealed that CITs indicated experiencing a low risk of compassion fatigue. However, raising awareness on this issue may still help CITs as a preventative measure to cope with possible compassion fatigue experience in the future. To address this issue, counselor educators may consider raising awareness on this topic by reviewing current counseling program curricula to add discussion questions related to compassion fatigue and its empirically predictive factors—wellness and resilience. Roach and Young (2007) stated that students in counseling programs reported group counseling, counseling techniques, legal and ethical issues, practicum, and wellness courses as contributing most to their knowledge and skills regarding wellness. Therefore, counselor educators might use different assignments, including group discussions, projects, and role-playing exercises, to open a discussion about the compassion fatigue phenomenon and the relation with its predictive factors and these courses. Counselor educators may also use the ProQOL scale as an assignment in an assessment and testing course to inform CITs about how to use this instrument as a self-monitoring aid. For example, professional counselors may feel overwhelmed because of working with trauma survivors after graduation and start noticing compassion fatigue symptoms in themselves. These individuals may self-administer the ProQOL scale to determine whether they have developed compassion fatigue. Additionally, in a practicum or an internship course, CITs may fill out the ProQOL as part of their continuing personal wellness plan by comparing personal results over time and sharing their thoughts and reflections about the results.


Supervisors need to find ways to raise awareness of compassion fatigue and its protective factors with CITs. For instance, during internship experience, supervisors may develop a site training including compassion fatigue awareness for CITs, as CITs should be prepared for the possible emotional and psychological consequences in working with trauma survivors. Student counselors also should be encouraged to advocate for themselves when they notice symptoms of compassion fatigue. Supervisors might consider the administration of the ProQOL scale regularly to assess both organizational and individual risks (Newell & MacNeil, 2010). Additionally, supervisors can use the ProQOL scale with their supervisees to start a conversation about compassion fatigue. Although the ProQOL is not a diagnostic test, the 30-item self-report scale can be utilized readily as a conversation starter in supervision sessions.


The results suggested that empathy and supervisory working alliance did not predict CITs’ compassion fatigue level. However, wellness and resilience are significantly related to contributing to it. Therefore, both counselor educators and supervisors might consider enhancing CITs’ resilience and wellness a worthwhile endeavor. For example, Miller and Sprang (2017) developed a component-based practice and supervision model to reduce compassion fatigue for use in training, supervision, and clinical practice. A tool like this one can be added to existing training curricula and supervision practice to improve CITs’ resilience and wellness.



The results of this study aim to provide greater clarity regarding the predictive factors of compassion fatigue among CITs. However, interpretation of results should take into consideration the limitations that emerged because of uncontrollable influences and choices we made. The study was limited in its ability to represent all CITs throughout the United States, as we utilized a convenience sampling approach. Additionally, we gathered data through self-report questionnaires, which introduce the possibility of response bias in the findings. Although we assumed participants answered each question honestly, they might not have been honest in their responses because of the fear of being perceived as weak or less competent. It is important to note that being in an internship class might also increase participants’ interest in the profession as they currently are engaged in the practice of counseling. Therefore, participants might have had a higher level of enthusiasm and reported less compassion fatigue. Also, individuals who suffer from compassion fatigue might have preferred not to respond to these items. Finally, although participants were enrolled in an internship class, each participant may have different numbers of hours of client experience.


Future Directions for Research

Additional research should be conducted to expand and clarify the current research findings of compassion fatigue among CITs. A phenomenological study using a qualitative research method is recommended to expand the findings of this current study. Future researchers may use the ProQOL scale to assess CITs’ level of compassion fatigue and then conduct interviews with the volunteer participants reporting a higher level of compassion fatigue to better understand CITs’ experience with compassion fatigue and its contributing factors. The data collected through a qualitative study may provide greater insight into the phenomenon of compassion fatigue among CITs. Additionally, researchers can replicate the present study with early-career counselors who have recently graduated, because of the noted intensity of those first years after graduation (Skovholt & Trotter-Mathison, 2016). Therefore, future researchers exploring novice counselors’ experiences with compassion fatigue will help counselor educators and supervisors better understand when counselors may start developing compassion fatigue symptoms, as well as how they cope with the symptoms.




CITs may struggle when they continuously hear painful stories of clients because of a lack of experience, skillset, or support (Skovholt & Trotter-Mathison, 2016). Researchers have described this experience as compassion fatigue. With this study, we aimed to provide a better understanding of the predictive factors of compassion fatigue among CITs. Using data-driven research results to determine ways to work with CITs on compassion fatigue and its predictive factors can be beneficial in preventing compassion fatigue symptoms from an early onset. CITs may take precautionary measures to ensure they remain enthusiastic and energized by the work they do. Further, implications of the current study may help CITs start their professional careers better prepared to provide their clients with the optimal care needed throughout the counseling relationship by minimizing compassion fatigue.



Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.






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Nesime Can is an instructor at Ankara University in Turkey. Joshua C. Watson, NCC, is a professor and department chair at Texas A&M University–Corpus Christi. Correspondence can be addressed to Nesime Can, Ankara University Faculty of Educational Sciences, Department of Educational Sciences, Program of Counseling and Guidance, Office 3111, Çankaya, Ankara, Turkey 06590,