School Counselors’ Emotional Intelligence and Comprehensive School Counseling Program Implementation: The Mediating Role of Transformational Leadership

Derron Hilts, Yanhong Liu, Melissa Luke

The authors examined whether school counselors’ emotional intelligence predicted their comprehensive school counseling program (CSCP) implementation and whether engagement in transformational leadership practices mediated the relationship between emotional intelligence and CSCP implementation. The sample for the study consisted of 792 school counselors nationwide. The findings demonstrated the significant mediating role of transformational leadership on the relationship between emotional intelligence and CSCP implementation. Implications for the counseling profession are discussed.

Keywords: emotional intelligence, school counselors, transformational leadership, comprehensive school counseling program, implementation

School counselors have been called upon to design and implement culturally responsive comprehensive school counseling programs (CSCPs) that have a deliberate and systemic focus on facilitating optimal student outcomes and development (American School Counselor Association [ASCA], 2017, 2019b). To this end, school counselors are expected to align their activities with the ASCA National Model (ASCA, 2019b) with an aim toward facilitating students’ knowledge, attitudes, skills, and behaviors to be academically and socially/emotionally successful and preparing students for college and career (ASCA, 2021). Relatedly, ASCA (2019a) urges school counselors to apply and enact a model of leadership in the process of program implementation. Several studies (e.g., Mason, 2010; Mullen et al., 2019; Shillingford & Lambie, 2010) have provided empirical evidence that supports the predictive role of school counselors’ leadership on their program implementation outcomes. Still, little is known about the relationship between school counselors’ program implementation and their leadership practices grounded in a specific model such as transformational leadership (Bolman & Deal, 1997; Kouzes & Posner, 1995). Understanding this relationship may allow school counselors to better align their practices within a specific leadership framework consistent with best practice (ASCA, 2019a).

Although leadership has been broadly established as a macro-level capability, emotional intelligence has started to gain interest in recent literature, as intra- and interpersonal competencies are central to school counselors’ practice (Hilts et al., 2019; Hilts, Liu, et al., 2022; Mullen et al., 2018). For instance, school counselors must be emotionally attuned to themselves and others to more effectively navigate the complexities of systems in which they operate (Mullen et al., 2018). One way to achieve such emotional attunement may be by respecting and validating others’ perspectives and providing emotional support to enact interpersonal influence aimed at facilitating educational partners’ keenness toward programmatic efforts (Hilts et al., 2019; Hilts, Liu, et al., 2022; Jordan & Lawrence, 2009). The purpose of the current study is to examine the mechanisms between school counselors’ emotional intelligence, transformational leadership, and CSCP implementation.

Comprehensive School Counseling Programs
     Although school counseling programs will vary in structure based on the unique needs of school and community partners (Mason, 2010), programs should be comprehensive in scope, preventative by design, and developmental in nature (ASCA, 2017). CSCP implementation, which comprises a core component of school counseling practice, involves multilevel services (e.g., instruction, consultation, collaboration) and assessments (e.g., program assessments, annual results reports). The functioning of these services and assessments is further defined and managed within the broader school community by the CSCP (Duquette, 2021). Moreover, CSCPs are generally aligned with the ASCA National Model (ASCA, 2019b) to create a shared vision among school counselors to have a more deliberate and systemic focus on facilitating optimal student outcomes and development.

Over the past 20 years, researchers have consistently found positive relationships between CSCP implementation and student achievement reflected through course grades and graduation/retention rates (Sink et al., 2008) and achievement-related outcomes such as behavioral issues and attendance (Akos et al., 2019). Students who attend schools with more well established and fully implemented CSCPs are more likely to perform well academically and behaviorally (Akos et al., 2019). Additionally, researchers have found that school counselors who engage in multilevel services associated with a CSCP are more likely to have higher levels of wellness functioning compared to those who are less engaged in delivering these services (Randick et al., 2018). As such, CSCP implementation seems to not only be positively related to student development and achievement but also the overall well-being of school counselors.

Designing and implementing a culturally responsive CSCP demands a collaborative effort between both school counselors and educational partners to create and sustain an environment that is responsive to students’ diverse needs (ASCA, 2017). This ongoing and iterative process requires school counselors to be emotionally attuned with school, family, and community partners to co-construct, facilitate, and lead initiatives to more efficaciously implement equitable services within their programs (ASCA, 2019b; Bryan et al., 2017). School counselors must engage in leadership and be attentive toward their self- and other-awareness and management to traverse diverse contexts involving differences in personalities, values and goals, and ideologies (Mullen et al., 2018). Although researchers have reported that school counselors’ CSCP implementation is positively related to their leadership (e.g., Mason, 2010), no studies have investigated the relationship between emotional intelligence and CSCP implementation.

Emotional Intelligence
     Emotional intelligence generally refers to the ability to recognize, comprehend, and manage the emotions of oneself and others to accomplish individual and shared goals (Kim & Kim, 2017). Scholars have purported that emotional intelligence can be subsumed into two overarching forms: trait emotional intelligence and ability emotional intelligence (Petrides & Furnham, 2000a, 2000b, 2001). Trait emotional intelligence, also known as trait emotional self-efficacy, involves “a constellation of behavioral dispositions and self-perceptions concerning one’s ability to recognize, process, and utilize emotional-laden information” (Petrides et al., 2004, p. 278). Ability emotional intelligence, also referred to as cognitive-emotional ability, concerns an individual’s emotion-related cognitive abilities (Petrides & Furnham, 2000b). Said differently, trait emotional intelligence is in the realm of an individual’s personality (e.g., social awareness), whereas ability emotional intelligence denotes an individual’s actual capabilities to perceive, understand, and respond to emotionally charged situations.

Over the past two decades, scholars have expanded the scope of emotional intelligence to have a deliberate focus on how emotional intelligence occurs within teams or groups in the workforce context (Jordan et al., 2002; Jordan & Lawrence, 2009). Given the salience of emotions in various professional and work contexts (e.g., Jordan & Troth, 2004), Jordan and colleagues’ (2002) Workgroup Emotional Intelligence Profile (WEIP) facilitates a better understanding of how emotional intelligence manifests in teams. The WEIP centralizes emotional intelligence around the “understanding of emotional processes” (Jordan et al., 2002, p. 197). Using the WEIP, researchers revealed that higher emotional intelligence scores are positively related to job satisfaction, organizational citizenship (e.g., performing competently under pressure), organizational commitment, and school and work performance (Miao et al., 2017a, 2017b; Van Rooy & Viswesvaran, 2004). Conversely, higher scores of emotional intelligence were negatively associated with turnover intentions and counterproductive behavior (Miao et al., 2017a, 2017b).

Emotional intelligence has also gained increased attention in the counseling literature. For example, Easton et al. (2008) found emotional intelligence as a significant predictor of counseling self-efficacy in the areas of attending to the counseling process and dealing with difficult client behavior. Following a two-phase investigation, Easton and colleagues demonstrated the stability of emotional intelligence during a 9-month timeframe in both groups of professional counselors and counselors-in-training; thus, the researchers argued that emotional intelligence may be an inherent characteristic associated with the career choice of counseling. In an earlier study with a sample with 108 school counselors, emotional intelligence was found to be significantly and uniquely related to school counselors’ multicultural counseling competence (Constantine & Gainor, 2001). More recently, school counselors’ emotional intelligence was found to be positively related to leadership self-efficacy and experience (Mullen et al., 2018).

School Counseling Leadership Practice
     Leadership practice is a dynamic, interpersonal phenomenon within which school counselors engage in behaviors that mobilize support from educational partners to achieve programmatic and organizational objectives aimed at promoting student achievement and development (Hilts, Peters, et al., 2022). The focus on leadership practice entails an emphasis on the actual behavior of the individual, which scholars have contended is a byproduct of both individual and contextual factors in which these behaviors occur (Hilts, Liu, et al., 2022; Mischel & Shoda, 1998; Scarborough & Luke, 2008). For instance, school counselors’ support from other school partners (Dollarhide et al., 2008; Robinson et al., 2019) and previous leadership experience (Hilts, Liu, et al., 2022; Lowe et al., 2017) have been found to influence school counselors’ engagement in leadership. Hilts, Liu, and colleagues (2022) found that intra- and interpersonal factors significantly predicted school counselors’ engagement in leadership such as multicultural competence, leadership self-efficacy, and psychological empowerment. Across several models of leadership (e.g., Bolman & Deal, 1997; Kouzes & Posner, 1995), transformational leadership has been situated in the context of school counseling (Gibson et al., 2018).

Transformational School Counseling Leadership
     Transformational leadership is described as behaviors aimed at encouraging others to enact leadership, challenge the status quo, and actively pursue learning and development to achieve higher performance (Bolman & Deal, 1997; Kouzes & Posner, 1995). Individuals employing transformational leadership foster a climate of trust and respect and inspire motivation among others by facilitating emotional attachments and commitment to others and the organization’s mission. More recently, Gibson et al. (2018) constructed and validated the School Counseling Transformational Leadership Inventory (SCTLI) in an effort to support school counselors in conceptualizing and informing their approach to leadership. The SCTLI (Gibson et al., 2018)—grounded in the ASCA National Model (ASCA, 2012) and the general transformational leadership literature (e.g., Avolio et al., 1991)—offers a framework to support engagement in leadership within a school context. For example, school counselors build partnerships with important decision-makers in the school and community and empower educational partners to act to improve the program and the school. School counselors engaging in transformational leadership ascribe to an egalitarian structure in which they engage in shared decision-making, promote a united vision, and inspire others to work toward positive change among students and the broader school community (Lowe et al., 2017). Beyond being studied as an outcome variable itself (Hilts, Liu, et al., 2022), school counselors’ enactment of leadership has also been found to be positively associated with their outcomes of CSCP implementation (Mason, 2010; Mullen et al., 2019).

Emotional Intelligence and the Mediating Role of Transformational Leadership
     Over the past several decades, emotional intelligence has been increasingly attributed as a critical trait and ability of individuals employing effective leadership (Kim & Kim, 2017). For instance, Gray (2009) asserted that effective school leaders are able to perceive, understand, and monitor their own and others’ internal states and use this information to guide the thinking and actions of themselves and others. Mullen and colleagues (2018) found that, among a sample of 389 school counselors, domains of emotional intelligence (Jordan & Lawrence, 2009) were significant predictors of leadership self-efficacy and leadership experience. Specifically, Mullen et al.’s (2018) results showed that (a) awareness of own emotions and management of own and others’ emotions were positively related to leadership self-efficacy; (b) management of own and others’ emotions significantly predicted leadership experience; and (c) awareness and management of others’ emotions was positively associated with self-leadership.

Moreover, initial research has revealed that not only is emotional intelligence an antecedent of leadership (Barbuto et al., 2014; Harms & Credé, 2010; Mullen et al., 2018), but that leadership, particularly transformational leadership, mediates the relationship between emotional intelligence and job-related behavior such as job performance (Hur et al., 2011; Hussein & Yesiltas, 2020; Rahman & Ferdausy, 2014). For example, Hussein and Yesiltas’s (2020) results indicated that not only were higher scores of emotional intelligence positively associated with organizational commitment, but that transformational leadership partially mediated the relationship between emotional intelligence and organizational commitment. In another study, Hur and colleagues (2011) sought to examine whether transformational leadership mediated the link between emotional intelligence and multiple outcomes among 859 public employees across 55 teams. The researchers’ results showed that transformational leadership mediated the relationship between emotional intelligence and service climate, as well as between emotional intelligence and leadership effectiveness. Scholars have explained this relationship as the ability of individuals employing transformational leadership to inspire and motivate others to accomplish beyond self- and organizational expectations and redirect feelings of frustration from setbacks to constructive solutions (Hur et al., 2011; Hussein & Yesiltas, 2020).

Purpose of the Study
     Taken together, emotional intelligence has been identified in the counseling literature as a significant predictor of counseling self-efficacy and competence (Constantine & Gainor, 2001; Easton et al., 2008). It has also been well established in the workforce literature as being positively related to job performance and leadership outcomes (Hussein & Yesiltas, 2020; Kim & Kim, 2017). The broader leadership literature also comprises evidence in support of the mediating role of transformational leadership between emotional intelligence and performance outcomes (Hur et al., 2011; Hussein & Yesiltas, 2020; Rahman & Ferdausy, 2014). Emotional intelligence has not been examined in relation to school counselors’ CSCP implementation and service outcomes, although CSCP implementation has been widely embraced as a core of the ASCA National Model. Likewise, although emotional intelligence has been studied with counseling practice and leadership separately, we identified no empirical research that has examined the mechanisms between school counselors’ emotional intelligence, transformational leadership practice, and outcomes of program implementation. The present study seeks to address these gaps. Thus, the two research questions that guided our study were: (a) Does school counselors’ emotional intelligence predict their CSCP implementation? and (b) Does engagement in transformational leadership practice mediate the relationship between emotional intelligence and CSCP implementation? Given the synergistic focus on collaboration (or teamwork) shared by the school and workforce contexts coupled with previous empirical evidence, we hypothesized that (a) school counselors’ emotional intelligence predicts their CSCP implementation, and (b) transformational leadership practice mediates the relationship between emotional intelligence and CSCP implementation.

Method

Research Design
     In the present study, we utilized a correlational, cross-sectional survey design. We used the Statistical Package for Social Sciences (SPSS, version 27). To test our hypotheses, we performed a mediation analysis using Hayes’s PROCESS in order to establish the extent of influence of an independent variable on an outcome variable (through a mediator; Hayes, 2012). Mediation analysis answered how an effect occurred between variables and is based on the prerequisite that the independent variable/predictor is often considered the “causal antecedent” to the outcome variable of interest (Hayes, 2012, p. 3). Furthermore, we expected that the effects of school counselors’ emotional intelligence on their CSCP implementation would be partly explained by the effects of their engagement in transformational leadership.

Participants
     Participants included for final analysis were 792 practicing school counselors in the United States, 94.6% (n = 749) of which reported to be certified/licensed as school counselors and 5.4% (n = 43) indicated to be either not certified/licensed or “unsure.” The sample’s geographic location was mostly suburban (n = 399, 50.4%), followed by rural (n = 195, 24.6%) and urban (n = 184, 23.2%); and 1.8% of participants (n = 14) did not disclose their setting. Public schools accounted for 86.2% (n = 683) of participants’ work settings, followed by charter (n = 42, 5.3%) and private (n = 40, 5.1%), while 3.4% (n = 27) of participants indicated “other” or did not disclose. For grade levels served by participants, 13% (n = 103) worked at the PK–4 level, 20.8% (n = 165) at the 5–8 level, 28.4% (n = 225) at the 9–12 level, and 37.8% (n = 299) worked at the combined K–12 level. Participants’ race/ethnicity included Asian/Native Hawaiian/Pacific Islander (n = 26, 3.3%), Multiracial (n = 47, 5.9%), Black/African American   (n = 56, 7.1%), Hispanic/Latino (n = 70, 8.8%), and White (n = 593, 74.9%). Lastly, participants’ mean age was 43, ranging from 23 to 77 years of age. Of the 792 participants, 82.4% (n = 653) identified as cisgender female, 11.0% (n = 88) as cisgender male, 0.3% (n = 2) as transgender female, 0.3% (n = 2) as transgender male, 3.8% (n = 30) chose “prefer to self-identify,” and 2.2% (n = 17) chose “not to answer.” Our sample was representative of the larger population based on the results of a recent nationwide study by ASCA (2021), in which approximately 7,000 school counselors were surveyed; demographic statistics from that study similar to ours included 88% of participants working in public, non-charter schools; 19% working at the middle school level; and 24% working in urban schools..

Procedures and Data Collection
     Prior to engaging in data collection, we received approval from our university’s IRB. According to our a priori power analysis conducted using G*Power 3.1 Software (Faul et al., 2007), a sample size of 558 participants would be considered sufficient for the current study, assuming a small effect size ( f 2 = 0.1); therefore, we attempted to achieve a nationally representative sample through a variety of recruitment methods. In efforts to represent the target population, non-probability sampling methods (Balkin & Kleist, 2016) were used and included either sending, posting, or requesting dissemination of a research recruitment message and survey link to (a) school counselors of current or former Recognized ASCA Model Program (RAMP)-designated school counseling programs, (b) state school counseling associations, (c) several closed groups on Facebook for school counselors, (d) the ASCA Scene online discussion forum, and (e) the university’s school counselor listserv. In addition, similar to recruitment methods used by Hilts and colleagues (2019) in previous school counseling research, we emailed ASCA members directly with an invitation to participate. We shared one to two follow-up announcements through these same methods between 2 to 4 weeks after the initial recruitment message.

The link within the research recruitment announcement directed participants to an informed consent page. After indicating their willingness to participate in the study, participants were then directed to the online survey managed by the Qualtrics platform. On average, the survey took approximately 15 minutes to complete.

Instrumentation
Demographic Questionnaire
     The demographic questionnaire consisted of 18 questions asked of all eligible participants. The demographic form included questions about participants’ school level, geographic location, school type, and student caseload. We also asked participants about other demographic information including race/ethnicity, gender, age, and years of experience. 

Workgroup Emotional Intelligence Profile
     The Workgroup Emotional Intelligence Profile-Short Version (WEIP-S; Jordan & Lawrence, 2009), a shortened version of the WEIP (Jordan et al., 2002) and the WEIP-6 (Jordan & Troth, 2004), is a 16-item, self-report scale that measures participants’ emotional intelligence within a team context. Jordan and Lawrence (2009) selected just 25 behaviorally based items from the 30-item WEIP-6 (Jordan & Troth, 2004). Through confirmatory factor analyses (CFA) to achieve the best fit model, the final WEIP-S measure consisted of 16 items with four factors, each of which had good internal consistency reliability in the sample: awareness of own emotions (4 items, ⍺ = .85), management of own emotions (4 items, ⍺ = .77), awareness of others’ emotions (4 items, ⍺ = .88), and management of others’ emotions (4 items, ⍺ = .77). To enhance construct validity of the WEIP-S, Jordan and Lawrence employed model replication analyses and test-retest stability across three time periods. Examples of items from each dimension are (a) “I can explain the emotions I feel to team members” (awareness of own emotions); (b) “When I am frustrated with fellow team members, I can overcome my frustration” (management of own emotions); (c) “I can read fellow team members ‘true’ feelings, even if they try to hide them” (awareness of others’ emotions); and (d) “I can provide the ‘spark’ to get fellow team members enthusiastic” (management of others’ emotions). The items are measured on a Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). For analyses, we summed scores of all dimensions, with higher scores indicating a greater amount of emotional intelligence. Cronbach’s ⍺ and McDonald’s omega (ω) for the WEIP-S were both .93, which indicated good internal consistency.

School Counseling Transformational Leadership Inventory
     The SCTLI (Gibson et al., 2018) is a 15-item, self-report inventory that measures the leadership practices of school counselors. The items are measured on a Likert-type scale ranging from 1 (never) to 5 (always or almost always) and a total score indicates the self-reported level of engagement in overall leadership practices. Sample items on the SCTLI include “I have empowered parents and colleagues to act to improve the program and the school” and “I have used persuasion with decision-makers to accomplish school counseling goals.” Findings from Gibson et al.’s (2018) exploratory factor analyses (EFAs) and CFAs revealed a one-factor model of transformational leadership practices based on transformational leadership theory and responsibilities as described within the ASCA National Model (ASCA, 2019b; CFI = .94, TLI = .93, RMSEA = .08). Through Pearson’s correlation, the researchers revealed that concurrent validity was significant (r = .68, p < .01). Additionally, in their sample, Gibson et al. reported strong internal consistency reliability with a Cronbach’s α = .94. In the current study, Cronbach’s α and McDonald’s (ω) for the SCTLI were .93 and .94, respectively.

School Counseling Program Implementation
     The School Counseling Program Implementation Survey-Revised (SCPIS-R; Clemens et al., 2010; Fye et al., 2020) is a self-report survey that measures school counselors’ level of CSCP implementation. The SCPIS-R (Fye et al., 2020), used in the current study, is a 14-item Likert-type scale ranging from 1 (not present) to 4 (fully implemented). The factor structure was established through two studies that utilized EFA (Clemens et al., 2010) and CFA (Fye et al., 2020) to test the factor structure. The data from the original study (Clemens et al., 2010) yielded a three-factor model structure of the SCPIS, which includes programmatic orientation (7 items, α = .79), school counselors’ use of computer software (3 items, α = .83), and school counseling services (7 items, α =. 81), and a total SCPIS of α = .87. That said, Fye et al.’s (2020) CFA findings suggested a modified two-factor model was a more appropriate fit; thus, the modified two-factor model structure of the SCPIS includes only programmatic orientation (7 items, α = .86) and school counseling services (7 items, α = .83) and a total SCPIS of α = .90. Examples from each factor are (a) needs assessments are completed regularly and guide program planning (programmatic orientation) and (b) services are organized so that all students are well served and have access to them (school counseling services). We calculated participants’ total SCPIS scores with higher scores indicating greater CSCP implementation (Mason, 2010; Mullen et al., 2019). In the present study, the SCPIS-R demonstrated good reliability (Cronbach’s α = .90; McDonald’s ω = .90) in our sample.

Data Analysis
Missing Data Analysis and Assumptions Test
     We received a total of 1,128 responses. Of all these responses, 336 respondents missed a significant portion (over 70%) of one or more of the main scales (i.e., WEIP-S, SCTLI, and SCPIS-R). We assessed this portion of values as not missing completely at random (NMCAR), and we proceeded with employing listwise deletion to 336 cases. The data NMCAR may be because of the survey length and time commitment, which is discussed more in the Limitations section. With the remaining 792 cases, the missing values counted for 0.1%–0.7% of missing values across respective scales. We performed a Little’s Missing Completely at Random test using SPSS Statistics Version 26.0 with a nonsignificant chi-square value (p > .05), which suggested that the missing values (across cases) were missed completely at random. Therefore, we retained all 792 cases and followed multiple imputation (Scheffer, 2002) to replace the missing values, using SPSS. Our data met assumptions for mediation analysis, normality based on histograms, and linearity and homoscedasticity as demonstrated through the scatterplots generated from univariate analysis. 

Mediation Analysis
     In our mediation model (see Figure 1), given its combined trait-ability nature and stability over time, school counselors’ emotional intelligence was hypothesized as the causal antecedent to program implementation; we then hypothesized transformational leadership practice to be a mediator for the effect of school counselors’ emotional intelligence on program implementation. We tested our mediation model based on Baron and Kenny’s (1986) approach. Specifically, our mediation analysis entailed four steps involving (a) the role of school counselors’ emotional intelligence (X) in predicting CSCP implementation (Y), with the coefficient denoted as c to reflect the total effect that X has on Y; (b) the predictive role of school counselors’ emotional intelligence (X) on transformational leadership practice (M), with the coefficient denoted as a; (c) the effect of transformational leadership practice (M) on CSCP implementation (Y), controlling for the effect of emotional intelligence (X), with the coefficient denoted as b; and (d) the association between school counselors’ emotional intelligence (X) and CSCP implementation (Y), using transformational leadership practice (M) as a mediator with coefficient denoted as c’ (MacKinnon et al., 2012). The difference between the coefficients c and c’,
(cc’), is the mediation effect of transformational leadership practice.

Figure 1
The Hypothesized Mediation Model

Note. SC = school counselors; CSCP = Comprehensive School Counseling Program.

 

Hayes’s PROCESS v3.5 (with 5,000 regenerated bootstrap samples) was used to perform the mediation analysis. Hayes’s PROCESS is an analytical function in SPSS used to specify and estimate coefficients of specified paths using ordinary least squares (OLS) regression (Hayes, 2012). We consulted Fritz and MacKinnon (2007) regarding sample adequacy for detecting a mediation effect. Specifically, in order to allow .80 power and a medium mediation effect size, a sample of 397 is recommended for Baron and Kenny’s test, and a sample of 558 is considered adequate to detect small effects via percentile bootstrap (Fritz & MacKinnon, 2007). As such, our sample size of 792 met both criteria. According to MacKinnon et al. (2012), the mediation effect is significant, if zero (0) is excluded from the designated confidence interval (95% in our study).

Results

Correlations
     We performed a bivariate analysis on the main study variables of school counselors’ emotional intelligence (measured using the WEIP-S), transformational leadership practice (measured using the SCTLI), and school counselors’ CSCP implementation (measured using the SCPIS-R). School counselors’ emotional intelligence scores were positively correlated with their transformational leadership practice (r = .42, p < .001) and were positively correlated with their CSCP implementation (r = .34, p < .001). Similarly, school counselors’ transformational leadership practice was found to be positively correlated with CSCP implementation (r = .56, p < .001). Table 1 denotes the correlations among variables.

Table 1
Correlation Matrix of Study Variables

Variable EI TL CSCP
EI   – .42** .34**
TL .42**   – .56**
CSCP .34** .56**   –

Note. EI = school counselors’ emotional intelligence scores; TL = school counselors’ transformational
leadership; CSCP = school counselors’ comprehensive school counseling program implementation.
**p < .001

Mediation Analysis Results
     With the total effect model (Step 1), we found a positive relation between school counselors’ emotional intelligence (X) and their CSCP implementation (Y; coefficient c = 0.24; p < .001; CI [0.20, 0.29]). Namely, school counselors’ emotional intelligence scores significantly predicted their CSCP implementation. In Step 2, we found a positive association between school counselors’ emotional intelligence scores (X) and their transformational leadership practice (M; coefficient a = 0.38; p < .001; CI [0.32, 0.43]). In Step 3, school counseling transformational leadership practice (M) was found to significantly predict their CSCP implementation (Y; coefficient b = 0.40; p < .001, CI [0.35, 0.45]) while controlling for the effect of emotional intelligence (X). Lastly, after adding transformational leadership practice as a mediator, we noted a significant direct effect of emotional intelligence on school counselors’ CSCP implementation (coefficient c’ = 0.09; p = .0001; CI [0.05, 0.14]). We also detected a mediation effect (coefficient ab = 0.15 which equaled cc’; p < .001; CI [0.12, 0.18]) of emotional intelligence on CSCP implementation through transformational leadership practice. The 95% confidence intervals did not include zero (0), so the path coefficients were significant.

We performed a Sobel test to further evaluate the significance of the mediation effect by school counseling transformational leadership practice, which yielded a Sobel test statistic of 9.97 with a p value of < .001. The Sobel outcome corroborated the significance of our mediated effect. To calculate the effect size of our mediation analysis, we generated kappa-squared value (k2; Preacher & Kelley, 2011). Our kappa-squared (k2) value of .17 suggested a medium effect size (Cohen, 1988). Table 2 demonstrates regression results for the effect of school counselors’ emotional intelligence on their CSCP implementation outcomes mediated by transformational leadership practice.

Table 2
Regression Results for Mediated Effect by Leadership Practice

Note. N = 792. EI = emotional intelligence; TL = transformational leadership; CSCP = comprehensive school counseling program; CI = 95% Confidence Interval. The 95% CI for ab is obtained by the bias-corrected bootstrap with 5,000 resamples.
aR2 (Y,X) is the proportion of variance in CSCP implementation explained by EI.
bR2 (M,X) is the proportion of variance in TL explained by EI.
cR2 (Y,MX) is the proportion of variance in CSCP implementation explained by EI and TL.
**p < .001.

 

Discussion

In this national sample of 792 practicing school counselors, we examined whether school counselors’ emotional intelligence predicts their CSCP implementation. We also investigated whether engagement in transformational leadership practice mediated the relationship between school counselors’ emotional intelligence and CSCP implementation. First, we found that school counselors who reported higher scores of emotional intelligence were also more likely to score higher in CSCP implementation. Given that designing and implementing a CSCP requires school counselors to engage in a culturally responsive and collaborative effort (ASCA, 2017), our result that suggested emotional intelligence is positively correlated with CSCP implementation is not entirely unpredicted. This result was consistent with previous evidence supporting the positive correlation between emotional intelligence and work performance (Miao et al., 2017a, 2017b; Van Rooy & Viswesvaran, 2004). The result also illustrated the predictive role of school counselors’ emotional intelligence on their CSCP implementation, beyond its significant association with counseling competencies (Constantine & Gainor, 2001; Easton et al., 2008).

Secondly, school counselors’ emotional intelligence was found to be positively associated with their engagement in transformational leadership. This result aligned with previous evidence that school counselors’ emotional intelligence is linked to leadership outcomes demonstrated through the workforce literature (Barbuto et al., 2014; Harms & Credé, 2010; Kim & Kim, 2017). Similarly, the result echoed Mullen et al.’s (2018) finding on the positive relationship between school counselors’ emotional intelligence and leadership scores measured by the Leadership Self-Efficacy Scale (LSES; Bobbio & Manganelli, 2009). Noteworthily, the LSES was normed and validated with college students. Our results advanced the school counseling literature and corroborated the relationship between emotional intelligence and school counseling transformational leadership measured by the SCTLI, a scale developed specifically for school counselors. Our results suggest that school counselors may actively attend to emotional processes in order to effectively enact transformational leadership practice.

Thirdly, we found that school counselors’ engagement in transformational leadership significantly mediated the relationship between their emotional intelligence and CSCP implementation. Because leadership is woven into the ASCA National Model and is considered an integral component of a CSCP (ASCA, 2019b), and school counselors are required to develop collaborative partnerships with a range of educational partners (ASCA, 2019a; Bryan et al., 2017), we were not surprised to find these two concepts were related to CSCP implementation. This result also aligns with empirical evidence in the broader leadership literature that transformational leadership mediated the relationship between emotional intelligence and work performance (Hur et al., 2011; Hussein & Yesiltas, 2020). This result is particularly meaningful in that it demonstrates school counseling leadership as either a significant predictor (Mason, 2010; Mullen et al., 2019) or an outcome variable itself (Hilts, Liu, et al., 2022; Mullen et al., 2018). It enables a more nuanced understanding of mechanisms involved in emotional intelligence, leadership, and program implementation in a school counseling context. To our best knowledge, the current study was the first study that found that through leadership practice, school counselors’ emotional intelligence may offer an indirect effect on their CSCP implementation.

Implications
     Results of this study have implications for school counselor practice and school counselor training and supervision. Given the significant relationships between emotional intelligence, transformational leadership, and CSCP implementation, we suggest that practicing school counselors begin by assessing their emotional intelligence, transformational leadership, and CSCP implementation and then set goals to enhance their performance. This may be especially important considering that other research has suggested that school counselors’ engagement in leadership, as well as their other roles and responsibilities (e.g., multicultural competence; challenging co-workers about discriminatory practices) have changed since the onset of the COVID-19 pandemic (Hilts & Liu, 2022). For instance, Hilts and Liu’s (2022) results indicated that school counselors’ leadership practice scores were higher during the pandemic compared to prior to the COVID-19 outbreak.

Next, school counselors can seek resources and professional development opportunities to support their goals. For example, school counselors may benefit from professional development focused on social-emotional learning (SEL), given SEL’s competency approach to building collaborative relationships (Collaborative for Academic, Social, and Emotional Learning, n.d.). That said, school counselors should also seek supports to experientially integrate their intrapersonal, interpersonal, and systemic skills associated with emotional intelligence, transformational leadership, and CSCP implementation. Intentional application of the Model for Supervision of School Counseling Leadership (Hilts, Peters, et al., 2022) may provide one such example for both school counseling practitioners and those in training.

School counselor training programs can also identify meaningful opportunities to infuse emotional intelligence and transformational leadership into school counselor coursework and supervision. Scarborough and Luke (2008) identified the important role of exposure in training to models of successful CSCP implementation and related resources on subsequent self-efficacy. As such, not only can school counseling coursework infuse the ASCA National Model Implementation Guide: Manage & Assess (ASCA, 2019b) and the Making DATA Work: An ASCA National Model publication (ASCA, 2018) along with additional emotional intelligence and transformational leadership resources, school counseling faculty and supervisors should intentionally incorporate school counseling students’ ongoing exposure to practicing school counselors and supervisors with high scores of emotional intelligence and transformational leadership.

Limitations
     As with all research, the results of this study need to be understood in consideration of the methodological strengths and limitations. Despite obtaining a large national sample, the data collection procedures used in this study prevented our ability to determine the survey response rate. As such, we are unable to make any claim about non-response bias and it is possible that school counselors who declined to participate significantly differed from those who completed the study. Relatedly, the sample included a proportionately large number of participants who started the survey but did not finish. It is possible that the attrition of these school counselors reflected an as of yet unidentified confounding construct that is also related to the variables under study (Balkin & Kleist, 2016). Our sample is nonetheless generally representative of the national school counselor demographic data reported in the recent state of the profession survey of approximately 7,000 school counselors (ASCA, 2021), strengthening the validity and subsequent generalizability of our results.

Another limitation of our study is that all data were cross-sectional and non-experimental. The correlation and mediation analyses used in the study demonstrate the strength of associations between the examined constructs, and do not reflect temporal or causal relationships. The cross-sectional design does not allow statistical control for the predictor and outcome variables; thus, it may not accurately specify the effect of the predictor on the mediator (Maxwell & Cole, 2007). Therefore, any inclination to impose intuitive logic or imbue directionality that emotional intelligence is an antecedent to either transformational leadership or CSCP implementation should be interpreted with caution. Further, all data from this study were collected at the same time and relied upon self-report. As such, common-method variance could have inflated the identified relationships between the constructs.

An important consideration is that this study was delineated to focus on illustrating individual path coefficients between emotional intelligence, leadership, and CSCP implementation and provides limited insight into understanding of complex relationships among latent variables. Likewise, we used Hayes’s PROCESS to examine our mediation model which features procedure rather than overall model fit created through more sophisticated statistical analyses such as structural equation modeling (SEM). Given that PROCESS is a modeling tool that relies on OLS regression, it may be biased in estimating effects without taking into consideration measurement error (Darlington & Hayes, 2017).

Suggestions for Future Research
     The results of this study have numerous implications for future research. Future studies may explore the relationship between emotional intelligence and other forms of leadership prevalent in the counseling literature, such as charismatic democratic or servant leadership (Hilts, Peters, et al., 2022). In addition, because self-report emotional intelligence measures have been described as better to assess intrapersonal processes and ability emotional intelligence measures have been shown to be related to emotion-focused coping and work performance (Miao et al., 2017a, 2017b), future research may consider incorporating ability and mixed emotional intelligence measurements to examine a causal model of emotional intelligence and transformational leadership (or other forms of leadership).

Future research could extend the unit of analysis in this study (e.g., individual school counselor) and adopt a similar perspective to Lee and Wong (2019) to examine emotional intelligence in teams. Studies could similarly expand the use of self-report emotional intelligence measures and include ability or mixed emotional intelligence measurement. Relatedly, as Miao et al. (2017b) described significant moderator effects of emotional labor demands of jobs on the relationship between self-report emotional intelligence and job satisfaction, future research could assess this in the school counseling context, wherein the emotional labor demands of the work may vary. Given the robust workforce literature grounding associations between emotional intelligence and job performance, job satisfaction, organizational commitment, and resilience in the face of counterproductive behavior in the workplace (Hussein & Yesiltas, 2020), future school counseling research can examine emotional intelligence and other constructs, including ethical decision-making, belonging, attachment, burnout, and systemic factors.

Lastly, as most constructs involved in school counseling practice are latent variables in nature, we recommend future scholars consider SEM when it comes to investigating overall model fit between the variables of interest. SEM offers more specification to the model including goodness of fit of the model to the data (Hayes et al., 2018). It minimizes bias involved in mediation effect estimation with consideration of individual indicators for each latent variable (Kline, 2016).

Conclusion

As an initial examination of the relationship between emotional intelligence and CSCP implementation, as well as the role of school counselors’ transformational leadership in mediating the relationship between emotional intelligence and CSCP implementation, this study was grounded in the empirical scholarship on leadership in both school counseling and allied fields. We found support for our hypothesized model of school counselors’ emotional intelligence and their CSCP implementation, mediated by their engagement in transformational leadership. Our examination yielded evidence in support of the significant mediating role of school counselors’ transformational leadership engagement on the relationship between emotional intelligence and CSCP implementation. In the meantime, our results supported the robust reliability of three instruments in our sample: the WEIP-S (Jordan & Lawrence, 2009), the SCTLI (Gibson et al., 2018), and the SCPIS-R (Clemens et al., 2010; Fye et al., 2020), which can be useful for future school counseling researchers and practitioners. This study serves as an important necessary step in establishing these relationships, and we anticipate that our results will ground further investigation related to school counselors’ emotional intelligence, leadership practices, and CSCP implementation, including the development of additional measurements.

Conflict of Interest and Funding Disclosure
This study was partially funded by Chi Sigma
Iota International’s Excellence in Counseling
Research Grants Program.


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Derron Hilts, PhD, NCC, is an assistant professor at Niagara University. Yanhong Liu, PhD, NCC, is an associate professor at Syracuse University. Melissa Luke, PhD, NCC, is a dean’s professor at Syracuse University. Correspondence may be addressed to Derron Hilts, 5795 Lewiston Rd, Niagara University, NY 14109, dhilts@niagara.edu.

Barriers to Seeking Counseling Among STEM Students: The Revised Fit, Stigma, and Value Scale

Michael T. Kalkbrenner, Gabriella Miceli

Meeting the mental health needs of students enrolled in science, technology, engineering, and mathematics (STEM) majors is particularly challenging for professional counselors who work in college settings, as STEM students are a subgroup of college students that face unique risks for developing mental health issues. The scarcity of literature on STEM student mental health coupled with their reticence to seek counseling is concerning. An important next step in this line of research is understanding why STEM students are reticent to seek counseling. Accordingly, the present investigators validated STEM students’ scores on the Revised Fit, Stigma, and Value (RFSV) Scale, a screening tool for measuring barriers to seeking counseling. Results also established the capacity of STEM students’ RFSV scores to predict peer-to-peer referrals to the counseling center and revealed demographic differences in barriers to counseling. Findings have implications for enhancing professional counselors’ efforts to support STEM students’ mental health. 

Keywords: Revised Fit, Stigma, and Value Scale; STEM; student mental health; barriers to counseling; peer-to-peer referrals

The frequency and complexity of college students presenting with mental health issues is a notable concern for professional counselors who work in university settings (Al-Maraira & Shennaq, 2021; Hong et al., 2022). Students enrolled in science, technology, engineering, and mathematics (STEM) majors are a distinctive group of college students who face unique risks for developing mental health issues (Daker et al., 2021; Kalkbrenner, James, & Pérez-Rojas, 2022; Lipson et al., 2016; Shapiro & Sax, 2011). When compared to their non-STEM counterparts, STEM students are less likely to recognize warning signs of mental distress, and they access mental health support services at lower rates than their peers. In addition, the harsh and competitive academic environment in STEM majors can exacerbate students’ risk for mental health distress (Lipson et al., 2016; Shapiro & Sax, 2011). Moreover, Rice et al. (2015) demonstrated that STEM students exhibit higher levels of maladaptive perfectionism, which is associated with higher levels of mental distress.

Whereas substantial academic and financial resources exist to support STEM students (U.S. Department of Education, 2020), there is a dearth of literature on supporting STEM students’ mental health, which is essential for retaining students and ensuring their success both in and out of the classroom (Kivlighan et al., 2021; Schwitzer et al., 2018). This gap in the literature is concerning, as STEM students are at risk for mental health issues, which can lead to attrition, isolation, and suicide (Daker et al., 2021; Kalkbrenner, James, & Pérez-Rojas, 2022; Lipson et al., 2016). As just one example, academic mental health distress is a significant predictor of lower enrollment and completion rates in STEM fields (Daker et al., 2021). Moreover, Muenks et al. (2020) found that higher levels of psychological vulnerability among STEM students was a significant predictor of lower class attendance, higher dropout intentions, and less class engagement.

The literature is lacking research on why STEM students tend to seek counseling at lower rates than non-STEM students. One of the first steps in supporting STEM students’ mental health is validating scores on a screening tool for identifying barriers to accessing mental health support services among STEM students. Although screening tools that appraise barriers to counseling exist, none of them have been validated with STEM students. The Revised Fit, Stigma, and Value (RFSV) Scale is a screening tool for appraising barriers to counseling that has been normed with non–college-based populations (e.g., adults in the United States; Kalkbrenner & Neukrug, 2018) and college students with mental health backgrounds (e.g., graduate counseling students; Kalkbrenner & Neukrug, 2019), as just a few examples. When compared to the existing normative RFSV Scale samples, STEM students are a distinct college student population who utilize counseling services at lower rates than students in mental health majors (e.g., psychology; Kalkbrenner, James, & Pérez-Rojas, 2022). The psychometric properties of instrumentation can fluctuate significantly between different populations, and researchers and practitioners have an ethical obligation to validate scores on instruments before interpreting the results with untested populations (Mvududu & Sink, 2013). Accordingly, the primary aims of the present study were to validate STEM students’ scores on the RFSV Scale (Kalkbrenner & Neukrug, 2019), test the capacity of RFSV scores for predicting referrals to the counseling center, and investigate demographic differences in STEM students’ RFSV scores.

The Revised Fit, Stigma, and Value (RFSV) Scale
     Neukrug et al. (2017) developed and validated scores on the original version of the Fit, Stigma, and Value (FSV) Scale for appraising barriers to counseling among a large sample of human services professionals. The FSV Scale contains the three following subscales or latent traits behind why one would be reluctant to seek personal counseling: Fit, Stigma, and Value. Kalkbrenner et al. (2019) validated scores on a more concise version of the FSV Scale, which became known as the RFSV Scale, which includes the same three subscales as the original version. Building on this line of research, Kalkbrenner and Neukrug (2019) found a higher-order factor, the Global Barriers to Counseling scale. The Global Barriers to Counseling scale is composed of a total composite score across the three single-order subscales (Fit, Stigma, and Value). Accordingly, the Fit, Stigma, and Value subscales can be scored separately and/or users can compute a total score for the higher-order Global Barriers to Counseling scale.

Scores on the RFSV Scale have been validated with a number of non-college populations, including adults in the United States (Kalkbrenner & Neukrug, 2018), professional counselors (Kalkbrenner et al., 2019), counselors-in-training (Kalkbrenner & Neukrug, 2019), and high school students (Kalkbrenner, Goodman-Scott, & Neukrug, 2020). If scores are validated with STEM students, the RFSV Scale could be used to enhance professional counselors’ mental health screening efforts to understand and promote STEM student mental health. Specifically, campus-wide mental health screening has implications for promoting peer-to-peer mental health support. For example, college counselors are implementing peer-to-peer mental health support initiatives by training students to recognize warning signs of mental distress in their peers and, in some instances, refer them to college counseling services (Kalkbrenner, Sink, & Smith, 2020).

Peer-to-Peer Mental Health Support
     College students tend to discuss mental health concerns with their peers more often than with a faculty member or student affairs professional (Wawrzynski et al., 2011; Woodhead et al., 2021). To this end, the popularity and utility of peer-to-peer mental health support initiatives has grown in recent years (Kalkbrenner, Lopez, & Gibbs, 2020; Olson et al., 2016). The effectiveness of these peer-to-peer support initiatives can be evaluated by test scores (e.g., scores on mental distress and well-being inventories) as well as non-test criteria (e.g., increases in the frequency of peer-to-peer mental health referrals). For example, Olson et al. (2016) found that college students who attended a Recognize & Refer workshop were significantly more likely to refer a peer to counseling when compared to students who did not attend the workshop. Similarly, Kalkbrenner, Lopez, and Gibbs (2020) found that increases in college students’ awareness of warning signs for mental distress were predictive of substantial increases in the odds of making peer-to-peer referrals to the counseling center.

Peer-to-peer mental health support also has implications for improving college student mental health (Bryan & Arkowitz, 2015; Byrom, 2018; Caporale-Berkowitz, 2022). For example, Bryan and Arkowitz (2015) found that peer-run support programs for depression were associated with significant reductions in depressive symptoms. In addition, Byrom (2018) demonstrated that peer support interventions were associated with increases in college students’ well-being. The synthesized results of the studies cited in this section suggest that peer-to-peer mental health support has utility for promoting mental health among general samples of undergraduate college students. However, to the best of our knowledge, the literature is lacking research on peer-to-peer mental health support with STEM majors, a subgroup of college students with unique mental health needs (Daker et al., 2021; Lipson et al., 2016; Shapiro & Sax, 2011).

The Present Study
     College counseling services are a valuable resource for students, as attendance in counseling is associated with increases in GPA and retention rates (Kivlighan et al., 2021; Lockard et al., 2019; Schwitzer et al., 2018). Considering STEM students’ unique vulnerability to mental health distress (Daker et al., 2021; Lipson et al., 2016; Shapiro & Sax, 2011) and their reticence to seek counseling (Kalkbrenner, James, & Pérez-Rojas, 2022), professional counselors who work in university settings need screening tools with validated scores for identifying why STEM students might avoid accessing counseling services. The RFSV Scale has potential to fill this gap in the measurement literature, as a number of recent psychometric studies (e.g., Kalkbrenner, Goodman-Scott, & Neukrug, 2020; Kalkbrenner & Neukrug, 2018) demonstrated support for the psychometric properties of scores on the RFSV Scale with non-college populations. However, the literature is lacking a screening tool for appraising barriers to counseling with validated scores among STEM students. Accordingly, a score validation study with STEM students is an important next step in this line of research, as the internal structure of instrumentation can vary notably between different samples (Mvududu & Sink, 2013). The literature is also lacking research on the potential of peer-to-peer mental support (e.g., students recognizing and referring a peer to counseling) among STEM students. This is another notable gap in the literature, as college students are more likely to discuss mental health concerns with a peer than with faculty or other university personnel (Wawrzynski et al., 2011; Woodhead et al., 2021). If STEM students’ scores on the RFSV Scale are validated, we will proceed to test the capacity of scores for predicting peer-to-peer referrals to the counseling center as well as examine demographic differences in STEM students’ RFSV scores.

The findings of the present investigation have implications for campus-wide mental health screening, increasing peer-to-peer mental health support, and identifying subgroups of STEM students that might be particularly reticent to seek counseling. To this end, the following research questions (RQs) and hypotheses (Ha) guided the present investigation: RQ1: Is the internal structure of scores on the RFSV Scale confirmed with STEM students? Ha1: The dimensionality of the RFSV Scale will be confirmed with STEM students. RQ2: Are STEM students’ RFSV scores significant predictors of making at least one referral to the counseling center? Ha2: Higher RFSV scores will emerge as a statistically significant positive predictor of STEM students making one or more peer referrals to the counseling center. RQ3: Are there significant demographic differences in FSV barriers to counseling among STEM students? Ha3: Statistically significant demographic differences in STEM students’ RFSV scores will emerge.

Methods

Participants and Procedures
     Following IRB approval, first author Michael T. Kalkbrenner obtained an email list from the Office of University Student Records of all students who were enrolled in a STEM major at a research-intensive university with four campus locations in three cities located in the Southwestern United States. A recruitment message was sent out to the email list via Qualtrics Secure Online Survey Platform. A total of 407 prospective participants clicked on the survey link. A response rate could not be calculated, as Qualtrics does not track inaccurate or inactive email addresses. A review of the raw data revealed 41 cases with 100% missing data. Likely, these 41 prospective participants clicked on the link to the survey and decided not to participate. Following the removal of those 41 cases, less than 20% of data were missing for the remaining 366 cases. Little’s Missing Completely at Random test indicated that the data could be treated as missing completely at random (p = .118) and expectation maximization was used to impute missing values. An investigation of standardized z-scores revealed six univariate outliers (z > ± 3.29) and Mahalanobis distances displayed eight multivariate outliers, which were removed from the data set, yielding a robust sample of N = 352.

Participants ranged in age from 18 to 63 (M = 24.29; SD = 8.59). The demographic profile for gender identity consisted of 65.1% (n = 229) female, 30.4% (n = 107) male, 2.0% (n = 7) non-binary, 1.1% (n = 4) transgender, 0.6% (n = 2) an identity not listed (“please specify”), and 0.9% (n = 3) prefer not to answer. The ethnoracial demographic profile consisted of 2.6% (n = 9) Native Indian or Alaska Native; 3.1% (n = 11) Asian or Asian American; 2.0% (n = 7) Black or African American; 48.3% (n = 170) Hispanic, Latinx, or Spanish origin; 2.0% (n = 7) Middle Eastern or North African; 3.4% (n = 12) Multiethnic; 36.6% (n = 129) White or European American; 1.1% (n = 4) Another race, ethnicity, or origin (“please specify”); and 0.9% (n = 3) preferred not to answer. The present sample was composed of notably more diverse groups of STEM students when compared to national estimates of STEM students (National Center for Educational Statistics [NCES], 2020). The NCES’s estimates revealed fewer women (33.0%, n = 263,034) and Latinx (12.3%, n = 94,927) STEM students as well as fewer White students (49.8%, n = 385,132). But the NCES’s national estimates included larger proportions of Black (7.2%, n = 55,642) and Asian (11.0%, n = 85,135) STEM students when compared to the present sample.

Instrumentation
     Participants completed a demographic questionnaire by indicating their informed consent, then confirming they met the following inclusion criteria for participation: (a) 18 years or older, (b) enrolled in at least one undergraduate STEM course, and (c) currently a STEM major. The demographic questionnaire concluded with questions about respondents’ age, gender identity, ethnoracial identity, help-seeking history, and if they had referred one or more peers to the counseling center.

The Revised FSV Scale
     The RFSV Scale is a screening tool that was designed to measure barriers to seeking counseling (Kalkbrenner, Neukrug, & Griffith, 2019). Participants respond to a prompt (“I am less likely to attend counseling because . . . ”) for 14 declarative statements on the following Likert scale: 1 = Strongly Disagree, 2 = Disagree, 3 = Neither Agree nor Disagree, 4 = Agree, or 5 = Strongly Agree. The RFSV Scale is composed of three subscales or latent traits behind one’s reticence to seek counseling, including Fit, Stigma, and Value. Scores on the Fit subscale can range from 5 to 25, with higher scores indicating more restraint from seeking counseling because one believes the process of counseling is not suitable with their personal worldview (e.g., “I couldn’t find a counselor who would understand me”). Scores on the Stigma subscale also range from 5 to 25, and higher scores denote a greater hesitation to seek counseling due to feelings of embarrassment or shame (e.g., “It would damage my reputation”). Scores on the Value subscale range from 4 to 20, with higher scores indicating a greater disinclination to seek counseling because they believe the effort required would not be worth the potential benefits (e.g., “Counseling is unnecessary because my problems will resolve naturally”).

The Global Barriers to Counseling scale is composed of test takers’ average composite score across the three Fit, Stigma, and Value subscales and produces an overall estimation of a test taker’s sensitivity to barriers toward seeking counseling. Scores on the Global Barriers to Counseling scale range from 13 to 65, with higher scores indicating a greater reticence to seek counseling. The collective findings of past investigators demonstrated evidence for the internal structure validity (confirmatory factor analysis) and internal consistency reliability (α = .70 to α = .91) of scores on the RFSV Scale with a number of non-college populations (Kalkbrenner, Goodman-Scott, & Neukrug, 2020; Kalkbrenner & Neukrug, 2018, 2019; Kalkbrenner et al., 2019).

Data Analysis
     A confirmatory factor analysis (CFA) based on structural equation modeling was computed in IBM SPSS AMOS version 26 to answer the first RQ about the dimensionality of STEM students’ RFSV scores. We used the joint suggestions from Dimitrov (2012) and Schreiber et al. (2006) for acceptable model fit in CFA: chi-square absolute fit index (CMIN; non-significant p-value or χ2 to df < 3), comparative fit index (CFI; .90 to .95 = acceptable fit and > .95 = close fit), root mean square error of approximation (RMSEA; ≤ .08), and the standardized root mean square residual (SRMR; ≤ .08). Internal consistency reliability evidence of test scores is another important step in testing a scale’s psychometric properties. Cronbach’s coefficient alpha (α) is the most popular internal consistency reliability estimate; however, its proper use is dependent on the data meeting several statistical assumptions (McNeish, 2018). Composite internal consistency reliability estimates, such as McDonald’s coefficient omega (ω), tend to produce more stable reliability estimates of scores. Accordingly, the present investigators computed both α and ω.

College students are more likely to discuss mental health concerns with their peers than with faculty, staff, or other university personnel (Wawrzynski et al., 2011; Woodhead et al., 2021). Accordingly, college counseling researchers and practitioners are devoting more time to peer-to-peer mental health support initiatives with the goal of increasing peer-to-peer referrals to the counseling center (Kalkbrenner, Sink, & Smith, 2020; Olson et al., 2016). Past investigators (e.g., Kalkbrenner, Neukrug, & Esquivel, 2022) found that the RFSV barriers were significant predictors of peer-to-peer referrals to the counseling center with non-STEM students. To test the generalizability of this finding with STEM students, we conducted a logistic regression analysis to answer the second RQ regarding the capacity of STEM students’ RFSV scores to predict at least one peer referral to the counseling center. STEM students’ interval-level composite scores on the Fit, Stigma, and Value subscales were entered into the model as predictor variables. The criterion variable was quantified on a categorical scale. On the demographic questionnaire, students responded to the following question: “Have you ever referred (recommended) another student to counseling services?” and selected either “0 = never referred a peer to the counseling center” or “1 = referred one or more peers to the counseling center.”

A 2(gender) X 3(race/ethnicity) X 2(help-seeking history) multivariate analysis of variance (MANOVA) was computed to investigate the third RQ regarding demographic differences in RFSV barriers among STEM students. The three categorical-level independent variables included gender (male or female), race/ethnicity (Latinx, White, or other ethnicity), and help-seeking history (never attended counseling or attended at least one counseling session). The three interval-level dependent variables included STEM students’ composite scores on the Fit, Stigma, and Value subscales. Discriminant analysis was employed as a post hoc test for MANOVA (Warne, 2014).

Results

The RFSV Scale items were entered into a CFA to test the dimensionality of scores with STEM students (RQ1). Excluding the CMIN (χ2 [74] = 257.55, p < .001, χ2 to df = 3.48), results revealed a satisfactory model fit: CFI = .92; RMSEA = .08, 90% CI [.07, .10]; and SRMR = .08. The CMIN tends to underestimate model fit with samples that are large enough for CFA (Dimitrov, 2012). Thus, adequate internal structure validity evidence of scores was achieved based on the collective CFI, RMSEA, and SRMR results. The standardized factor loadings were all acceptable-to-strong and ranged from .48 to .90 (see Figure 1, Model 1).

Figure 1
Revised FSV Scale Path Models With Standardized Coefficients

Based on the findings of Kalkbrenner and Neukrug (2019), we computed a higher-order confirmatory factor analysis (HCFA) to test for a Global Barriers to Counseling scale. As expected, the single-factor RFSV model (see Figure 1, Model 2) revealed poor model fit: CMIN (χ2 [77] = 1,013.71, p < .001, χ2 to df = 13.17); CFI = .61; RMSEA = .19, 90% CI [.18, .20]; and SRMR = .13. Accordingly, the theoretical support for a higher-order model (Kalkbrenner & Neukrug, 2019) coupled with the poor fitting single-factor model (see Figure 1, Model 2) indicated that computing an HCFA was appropriate. Except for the CMIN (χ2 [74] = 257.55, p < .001, χ2 to df = 3.48), the higher-order model (see Figure 1, Model 3) displayed a satisfactory model fit: CFI = .92; RMSEA = .08, 90% CI [.07, .10]; and SRMR = .08. Tests of internal consistency reliability revealed satisfactory reliability evidence of scores on the Fit (α = .84, ω = .83), Stigma (α = .86, ω = .87), and Value (α = .79, ω = .79) subscales and the Global Barriers to Counseling scale (α = .88, ω = .88).

STEM students’ RFSV scores were entered into a logistic regression analysis to answer RQ2 regarding the capacity of STEM students’ RFSV scores to predict at least one referral to the counseling center. The logistic regression model was statistically significant, X2(1) = 80.97, p < .001, Nagelkerke R2 = .064. The odds ratios, Exp(B), revealed that a decrease of one unit in STEM students’ scores on the Value subscale (higher scores = less value toward counseling) was associated with a decrease in the odds of having made at least one peer-to-peer referral to the counseling center by a factor of .559.

A factorial MANOVA was computed to answer RQ3 regarding demographic differences in RFSV barriers among STEM students. A significant main effect emerged for gender on the combined dependent variables, F(3, 316) = 5.23, p = .002, Pillai’s Trace = 0.05, η2p = 0.047. The post hoc discriminant analysis (DA) revealed a significant discriminant function, Wilks λ = 0.93, χ2 = 23.60, df = 3, canonical correlation = 0.26, p < .001. The standardized canonical discriminant function coefficients between the latent factors and discriminant functions showed that the Value factor loaded more strongly on the discriminant function (1.10) than the Stigma (0.17) or Fit (−0.62) factors. The mean discriminant score on the function for male participants was 0.40. The mean discriminant score on the function for female participants was −0.19. In other words, the MANOVA and post hoc DA revealed that male STEM students scored significantly higher (higher scores reflect greater reluctance to seek counseling) on the Value barrier when compared to female STEM students.

A significant main effect also emerged for help-seeking history on the combined dependent variables, F(3, 467) = 4.65, p = .003, Pillai’s Trace = 0.04, η2p = 0.042. The post hoc DA displayed a significant discriminant function, Wilks λ = 0.93, χ2 = 24.10, df = 3, canonical correlation = 0.26, p < .001. The standardized canonical discriminant function coefficients between the latent factors and discriminant functions showed that the Value factor loaded more strongly on the discriminant function (1.10) than the Stigma (0.01) or Fit (−0.71) factors. The mean discriminant score on the function for participants without a help-seeking history was 0.25. The mean discriminant score on the function for participants with a help-seeking history was −0.29. In other words, the MANOVA and post hoc DA showed that STEM students without a help-seeking history scored significantly higher on the Value barrier than STEM students with a help-seeking history.

Discussion

The purpose of the present study was to validate STEM students’ scores on the RFSV Scale and investigate demographic correlates with the Fit, Stigma, and Value barriers. The CFA results demonstrated that the RFSV Scale and its dimensions were estimated adequately with a sample of STEM students. This finding is consistent with the existing body of literature on the generalizability of scores on the RFSV Scale with a number of non-college populations (e.g., Kalkbrenner, Goodman-Scott, & Neukrug, 2020; Kalkbrenner & Neukrug, 2018). In addition to a stringent test of internal structure validity, CFA is also a theory-testing procedure (Mvududu & Sink, 2013). Thus, our CFA results indicated that Fit, Stigma, and Value comprise a tri-dimensional theoretical model of barriers to counseling among STEM students. Consistent with the results of Kalkbrenner and Neukrug (2019), we found support for a higher-order Global Barriers to Counseling scale. The presence of a higher-order factor (see Figure 1, Model 3) indicates that the covariation between the first-order Fit, Stigma, and Value subscales comprises a meta-level latent trait. Collectively, the single-order and higher-order CFA results indicate that Fit, Stigma, and Value are discrete dimensions of an interconnected latent trait. Accordingly, CFA results provided support for the dimensionality of both the single-order RFSV model (see Figure 1, Model 1) and the higher-order model (see Figure 1, Model 3) with STEM students.

STEM students face unique risks for mental health issues, including maladaptive perfectionism as well as intense pressure to perform in harsh and competitive academic environments (Rice et al. 2015; Shapiro & Sax, 2011). These unique risk factors coupled with STEM students’ reticence to seek counseling (Kalkbrenner, James, & Pérez-Rojas, 2022) created a need for a screening tool for appraising why STEM students might avoid accessing counseling services. The results of the CFA and HCFA in the present study begin to address the gap in the literature regarding the lack of a screening tool with validated scores for appraising barriers to counseling among STEM students. Our CFA and HCFA results suggest that college counselors can use the RFSV Scale as one way to understand why STEM students on their campus are reluctant to access counseling services.

Consistent with the findings of Kalkbrenner and Neukrug (2019), we found statistically significant differences in peer-to-peer referrals and demographic differences in STEM students’ scores on the Value barrier. Specifically, increases in STEM students’ belief in the value of attending counseling were associated with significant increases in the odds of making one or more peer referrals to the counseling center, as indicated by the moderate effect size of the finding. It appears that STEM students’ attendance in personal counseling increases their propensity for recommending counseling to their peers. Similar to Kalkbrenner and Neukrug (2018), tests of group demographic differences revealed that STEM students in the present study with a help-seeking history were less sensitive to the Value barrier than STEM students without a help-seeking history. These findings indicate that attendance in counseling might enhance STEM students’ belief that the effort required to attend counseling is worth the benefits. Perhaps experiencing counseling firsthand increases STEM students’ belief in the value of counseling as well as their disposition to refer a peer to counseling. This finding has particularly important implications, as STEM students are a distinct college-based population with unique mental health needs who tend to utilize mental health support services at lower rates than non-STEM students (Kalkbrenner, James, & Pérez-Rojas, 2022; Rice et al., 2015; Shapiro & Sax, 2011). In particular, our results suggest that STEM students who access counseling services usually see value in the process. STEM students’ general attitudes about counseling might become more positive if more and more STEM students participate in counseling.

Also, consistent with the findings of Kalkbrenner and Neukrug (2018), we found demographic differences in STEM students’ scores on the Value barrier by gender identity, with males attributing less value to attending counseling than females. Macro- and micro-systemic gender role forces tend to contribute to men’s reticence to seek counseling (Neukrug et al., 2013). These forces might be intensified among male STEM students considering the intersectionality between gender roles and the high-pressure environment in STEM majors to not show vulnerability (Lipson et al., 2016; Neukrug et al., 2013). Specifically, gender-role pressures to avoid showing vulnerability coupled with a high-pressure academic environment might make male STEM students especially reluctant to seek counseling. Men are also less likely than women to recognize and seek treatment for mental health issues (Kalkbrenner & Neukrug 2018; Neukrug et al., 2013). Thus, it is also possible that male STEM students are less likely to recognize mental distress as a potentially serious health issue, which contributes to them placing less value on the benefits of counseling when compared to their female counterparts. Future research is needed to test these possible explanations for this finding.

Implications
     The findings of this study have a number of implications for professional counselors who work in college settings. The CFA and HCFA results extend the psychometric properties of the RFSV Scale to STEM students (RQ1), which is an important contribution to the measurement literature, as the scale offers professional counselors a brief screening tool that usually takes 10 minutes or less to complete. The RFSV Scale can be administered at the systemic level (e.g., all STEM students at a university). Tests of internal structure reveal support for a three-dimensional RFSV model (see Figure 1, Model 1) as well as a higher-order model (see Figure 1, Model 3) with STEM students. Accordingly, professional counselors can administer and score one or both RFSV models depending on their mental health screening goals. The Global Barriers to Counseling scale might have utility for college counselors who are aiming to gather baseline information about STEM students’ general reticence to seek counseling. The three-dimensional model can provide more specific information (Fit, Stigma, and/or Value) about the reasons why STEM students on a particular campus are reluctant to seek counseling.

Our results reveal that increases in STEM students’ scores on the Value subscale were associated with a noteworthy increase in the odds of making a peer-to-peer referral to the counseling center. This finding coupled with STEM students’ vulnerability to mental distress (Daker et al., 2021; Kalkbrenner, James, & Pérez-Rojas, 2022; Lipson et al., 2016; Shapiro & Sax, 2011) suggests that peer-to-peer referrals to mental health support services might be more important than ever before in connecting STEM students in mental distress to support services. Professional counselors who work in college settings can administer the RFSV Scale to STEM students and use the results as one method of informing the content of peer-to-peer mental health support initiatives. If, for example, STEM students on a particular campus score higher on the Value subscale (higher scores denote less value toward counseling), there might be utility in including information about the many benefits of counseling in peer-to-peer outreach initiatives for STEM students. Specifically, it might be beneficial to discuss both the academic and personal benefits associated with attending counseling. For groups of STEM students who score higher on the Stigma scale, college counselors might take a strengths-based perspective by discussing how attending counseling takes courage and strength.

College counselors and student affairs officials can reach STEM students by partnering with STEM faculty and administrators to attend STEM orientations and classes that are held in large lecture halls. College counselors may build relationships with department heads and program directors of STEM programs through sharing empirical evidence on STEM students’ unique mental health needs and their reticence to access mental health support services (Kalkbrenner, James, & Pérez-Rojas, 2022; Lipson et al., 2016; Shapiro & Sax, 2011). College counselors might also discuss how increases in STEM students’ mental health is associated with greater retention and academic success, which are key values in STEM programs (Daker et al., 2021; Lockard et al., 2019; Meaders et al., 2020; Muenks et al., 2020). As buy-in from STEM department heads and program directors increases, there might be utility in professional counselors regularly making presentations and facilitating discussions about mental health and the benefits of attending counseling during new STEM student orientations. The content of these presentations can be based on the extant literature regarding the socio-personal factors that can place STEM students at risk for mental distress—for example, maladaptive perfectionism (Rice et al., 2015), high-pressure academic environments (Shapiro & Sax, 2011), and difficulty recognizing warning signs for mental distress (Kalkbrenner, James, & Pérez-Rojas, 2022). Once STEM students learn about these socio-personal factors, the presentation content can shift to psychoeducation about the utility of counseling for improving both personal and academic outcomes (Lockard et al., 2019).

The RFSV Scale can also be administered on more targeted levels, for example, to specific groups of STEM students who might be particularly vulnerable to mental health distress. There might be utility in administering the RFSV Scale to male STEM students considering that we found male STEM students were more sensitive to the Value barrier than female STEM students. College counselors can use the RFSV results to identify specific barriers (e.g., Value) that might be making STEM students on their campus unlikely to access counseling services. Such results can be used to inform thes curriculum of mental health programming (e.g., peer-to-peer support initiatives). When working with male STEM students, college counselors might consider the intersectionality of academic pressure (Lipson et al., 2016) and gender-role–based mental health stressors (Neukrug et al., 2013) they might be facing. In all likelihood, considering the intersectionality between these socio-personal factors will help college counselors address their clients’ presenting concerns holistically.

Limitations and Future Research
     The methodological limitations of this research should be reviewed when considering the implications of the results. The preset data were collected from STEM students in three different cities located in the Southwestern United States; however, results might not generalize to STEM students in other geographical locations. Future researchers can validate RFSV scores with national and international samples of STEM students. Moreover, the findings of cross-sectional research designs are correlational, which prevents researchers from drawing conclusions regarding cause-and-effect. Now that STEM students’ scores on the RFSV Scale are validated, future investigators can extend this line of inquiry by conducting outcome research on the effectiveness of interventions geared toward promoting the utilization of mental health support services among STEM students.

Although factor analytic results in the present study were promising, STEM students are not a homogenous group. To this end, future investigators can extend this line of research by conducting factorial invariance testing to examine the psychometric equivalence of RFSV scores across subgroups of STEM students. As just one example, past investigators (e.g., Shapiro & Sax, 2011) found differences in STEM students’ mental health by gender identity. Relatedly, our results did not reveal demographic differences by race/ethnicity in STEM students’ vulnerability to barriers to counseling. However, we used a dummy-coding procedure to create racial/ethnic identity comparison groups (Latinx, White, or other ethnicity) that were large enough for statistical analyses. Clustering participants with racial/ethnic identities other than White or Latinx into one group might have masked significant findings within the other race/ethnicity group. It is also possible that some participants identified as White and Latinx, as White is a racial category and Latinx is an ethnic category. Future researchers should examine potential disparities in barriers to counseling among more racially and ethnically diverse samples of STEM students. In an extension of the extant literature on samples of primarily male STEM students, the present study included notably more (> 50%) female STEM students when compared to a national demographic profile of STEM students (NCES, 2020). However, the findings of the present study might not generalize to STEM students with gender identities that extend beyond only male or female. Accordingly, future researchers can test the invariance of RFSV scores with more gender-diverse samples.

The findings of the CFA and HCFA in the present study supported Fit, Stigma, and Value as barriers to counseling among STEM students. However, the deductive nature of quantitative research does not capture the nuances of participants’ lived experiences. One way that future investigators can extend this line of research is through qualitative investigations of STEM students’ attitudes and values about seeking counseling services. Qualitative results might reveal important nuances and insights into STEM students’ propensity to access mental health support services.

Conclusion

To the best of our knowledge, the present investigation is the first to establish the psychometric properties of a barriers to counseling tool with STEM students. The results represent an important contribution to the measurement literature, as confirming the internal structure of test scores on an existing measure with a previously untested population is a vital step in demonstrating construct validity. We also found that decreases in STEM students’ reticence to seek counseling was predictive of statistically significant increases in the odds of making a peer referral to the counseling center. In addition, results revealed demographic differences in barriers to counseling among STEM students by gender and help-seeking history. Collectively, our findings suggest that professional counselors who work in college settings can use the RFSV Scale as one way to support STEM college student mental health by identifying why STEM students might be reticent to access counseling services. Supporting STEM students’ mental health has implications for increasing their retention rates, completion rates, and overall psychological well-being.

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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Michael T. Kalkbrenner, PhD, NCC, is an associate professor at New Mexico State University. Gabriella Miceli, MS, LPC-A, is a doctoral student at New Mexico State University. Correspondence may be addressed to Michael T. Kalkbrenner, 1780 E. University Ave., Las Cruces, NM 88003, mkalk001@nmsu.edu.

Book Review—Surviving and Thriving in Your Counseling Program

by Julius A. Austin and Jude T. Austin II

This is the book I wish I had when I started graduate school.

I thoroughly enjoyed this book. The authors of this book present the material in an authentic voice that makes the reader feel accepted and understood at whatever stage of the process they are at in the counseling program. The authors readily present their own fears and expectations when they began graduate school. They are humble and honest about things they wish they had done differently, and they embody a calm and considerate approach with a welcome addition of humor.

The authors begin with an informative section that touches on all the normal concerns and fears you may have as a student just starting a counseling program, and the book progresses through every stage of a counseling program from your first year all the way through graduation and your first job. The authors touch on core concepts in each section, common fears, and resources for success. They even provide perspective on pursuing a doctoral degree and skills for choosing where you would like to start your first job after graduation.

The book’s structure makes it flow easily from chapter to chapter, giving light to the gradual progression of course work and your own personal development and self-care. In each chapter, the authors blend in voices and stories from people currently in the profession. Sharing examples, struggles, development, and successes helps to give credibility to the process and normalize expectations and concerns.

The authors also provide a section on emotional maturity in the book. I found this section to be a welcome addition in that it defines several examples of emotional immaturity and characteristics of emotionally mature students. This section provided insight into emotional stability, emotional intelligence, and the self-awareness that is beneficial to success in a counseling program.

In addition to this, the authors also provide a section on dealing with setbacks and managing conflicts. Both sections contain valuable information to consider, and I don’t believe these topics are discussed frequently enough without judgement in other texts. Setbacks and conflicts are bound to happen in any setting. Normalizing this and looking at skills and reflections to approach these conflicts are a welcome addition to strengthening the effectiveness of this text.

Overall, I think this book is valuable, and students should consider reading this book in full when considering entering into a counseling program. This book would have also been beneficial as an assigned text during my first semester of graduate school. It is an easy and informative read that does an excellent job of reflecting on all those questions that either I was too scared to ask, only asked in my small group of equally confused classmates after class, or quite honestly, didn’t even have enough information to know I needed to ask.

This book gives amazing insight into not just the information about a counseling program, but also manages to grasp how it changes you as a person and how it changes your perspectives, your family dynamics, and your own value system. It normalizes the stress of a graduate program but also highlights the journey and the beauty of those outcomes.

 

Austin, J. A., & Austin, J. T., II (2020). Surviving and thriving in your counseling program. American Counseling Association.

Reviewed by: Megan Ries, NCC

Book Review—Becoming a Counselor: The Light, the Bright, and the Serious (3rd ed.)

by Samuel T. Gladding

Dr. Samuel T. Gladding’s third edition of Becoming a Counselor: The Light, the Bright, and the Serious offers a genuine and insightful reflection of his experiences both as an individual and as a counselor.

In Becoming a Counselor, Dr. Gladding (PhD, NCC, CCMHC, LPC) describes his experiences in counseling through a series of vignettes. These brief but comprehensive stories are cohesively told through his personal lens as a counseling professional. These vignettes range from Dr. Gladding’s impressions from his experiences growing up in Decatur, Georgia, to teaching within a counseling program, to the COVID-19 pandemic in 2020.

The book is divided into 17 sections, which contain a series of vignettes and stories pertaining to the section’s specific theme of counseling and Dr. Gladding’s experiences. Each section begins with a poem, composed by Dr. Gladding, which gives a brief glimpse into what the following section will entail. The third edition expands on previous editions to include an additional 35 vignettes, as well as an introduction that explains Dr. Gladding’s personal worldview. In this introduction, Dr. Gladding specifically acknowledges his own biases and experiences that shaped him as a counselor, providing crucial self-disclosure prior to delving into his personal experiences.

Limitations for Becoming a Counselor include the highly personal nature of the majority of these vignettes. Although the themes established within this volume assist with generalizing this knowledge outside of Dr. Gladding’s experiences, this book tends to take an autobiographical tone, rather than an educational one.

Nonetheless, fellow mental health professionals can use this book as a useful tool to guide their own journey through professional development and leadership. Dr. Gladding’s conversational tone guides the reader toward a deeper understanding of seemingly superficial events.

The primary strength of this book is within the universality of its themes. Through interweaving brief stories about his experiences, Dr. Gladding shares both ordeals and successes in vignettes that can easily be incorporated into a class lecture. Practicum or internship courses would doubtlessly find short stories detailing Dr. Gladding’s experiences as useful material to discuss within the classroom. Another strength of this book includes its organization of seemingly enormous and intimidating topics, such as finding success in academia, and then taking the teeth from these topics by including fun, good-humored titles for the individual vignettes. Although many books are professional in nature, it is rare to find one that also carries a sense of humor. However, Dr. Gladding does not shy away from the more serious topics of counseling.

If you read this book, you will undoubtedly find it difficult to put it down. This book reads more as a story than a text at times, which will more than likely lead to you finishing it by the end of the day.

Although not entirely educational in nature, Becoming a Counselor carries lessons from an autobiographical standpoint that many counselors can value. This edition was one of Dr. Gladding’s final works prior to his passing in December 2021. Within the latest edition of his book, Dr. Gladding encourages the reader to carry a level of levity, insight, and seriousness as both a counselor and an individual through their own experiences.

 

Gladding, S. T. (2021). Becoming a counselor: The light, the bright, and the serious (3rd ed.). American Counseling Association Foundation.

Reviewed by: Katie Michaels, MA, NCC, ALC

Herbal Remedies, Over-the-Counter Drugs, and Dietary Supplements: A Primer for Counselors

Sabina Remmers de Vries, Christine D. Gonzales-Wong

U.S. consumers are spending billions on complementary and alternative medicines, and nearly half of those consumers on psychiatric prescription drugs also use herbal remedies. Clients may take herbaceuticals, over-the-counter drugs, and dietary supplements instead of, or in combination with, prescription drugs. This frequently occurs without the input or knowledge of prescribers, which can create significant problems for clients. There is a growing need for counselors to be familiar with herbal remedies, over-the-counter drugs, and dietary supplements. It is vital that counselors understand the potential interaction of these substances with prescribed medications, as well as their impact on clients’ emotions, thoughts, and behaviors. This article reviews relevant research and professional publications in order to provide an overview of the most commonly used psychoactive non-prescription products, counselor roles, client concerns, associated counseling ethics, diversity and cultural considerations, and counselor supervision concerns.

Keywords: counseling ethics, herbaceuticals, over-the-counter drugs, dietary supplements, diversity

A recent survey by the World Health Organization (WHO) World Mental Health Survey Consortium reported inadequate treatment of mental health conditions, especially in disadvantaged populations (Borges et al., 2020). In 2019, an estimated 20.6% of adults in the United States (51.5 million adults) experienced some type of mental health problem (National Institute of Mental Health, 2019). In an attempt to address mental health concerns, clients may take a variety of drugs, which can range from prescribed psychotropic medications to self-administered herbal remedies, over-the-counter drugs (OTCs), and dietary supplements (Ravven et al., 2011). Researchers have found that older adults, particularly, use prescription drugs, herbal remedies, and dietary supplements concurrently (Agbabiaka et al., 2017; Kaufman et al., 2002). Herbal remedies and dietary supplements are part of complementary and alternative medicines (CAMs), which consist of various products and practices (Nahin et al., 2009).

In terms of mental health diagnoses (e.g., major depressive disorder, bipolar disorder, schizophrenia, anxiety disorder), prescription medication noncompliance can range between 28%–72% (Julius et al., 2009). There are many reasons clients do not adhere to their psychotropic medication regimens, including client-specific factors (psychological factors, habits, and beliefs), drug-specific factors (side effects), social/environmental factors (support system issues), and financial considerations (cost of medications, copays, and deductibles; Freudenberg-Hua et al., 2019; Julius et al., 2009; Phillips et al., 2016). There are clients who want to take their medication as prescribed but may not be able to afford it (Wang et al. 2015). Researchers found that clients might be prone to reduce use of prescription medication or substitute with OTCs and CAMs when experiencing financial pressures (Agbabiaka et al., 2017; Gibson, 2005; Wang et al., 2015). Another concern is lack of client knowledge pertaining to medications and diagnoses. Makaryus and Friedman (2005) found that only 27.9% of surveyed patients knew the names of all of the medications they had been prescribed, only 37.2% knew the purpose of all of their prescribed drugs, and only 14% knew the most frequent side effects.

For a variety of reasons, a substantial number of clients do not readily disclose the use of CAMs and OTCs to physicians or therapists (Agbabiaka et al., 2017; Ravven et al., 2011). This is concerning, as clients may be unaware of the pharmacological properties and side effects of these products. Considering these factors, counselors have a professional and ethical obligation to possess a working knowledge of psychopharmacology (American Counseling Association [ACA], 2014; Council for Accreditation of Counseling and Related Educational Programs [CACREP], 2016; Murray & Murray, 2007). We assert that this knowledge should include herbal remedies, OTCs, and dietary supplements.

Despite the potential impact of psychoactive drugs on mental health, there is a paucity of research in the counseling literature that addresses psychopharmacology (Ingersoll, 2005; Sepulveda et al., 2016). There is even less counseling literature available that references herbal remedies, dietary supplements, and OTCs (Ingersoll, 2005; Kaut & Dickerson, 2007). A recent search of the ACA and ACA division journals returned very limited results on psychopharmacology, herbal remedies, OTCs, and dietary supplements. For example, the greatest number of articles pertaining to psychopharmacology was found in the Journal of Mental Health Counseling. The journal published five articles that ranged in year of publication from 2002 to 2011. The Journal of Counseling & Development published three articles that ranged in year of publication from 1985 to 2004. The only article related to herbaceuticals was published in the Journal of Counseling & Development in 2005. This article by Ingersoll (2005) discussed herbaceuticals in reference to the counseling profession. Although this review provided an overview of herbal remedies, it did not explore OTCs or dietary supplements. The counseling literature is in urgent need of expansion in this area because the scope of the counseling profession and mental health care are steadily evolving (Kaut, 2011; Sepulveda et al., 2016).

Given the lack of literature, counseling professionals providing services to clients may lack practical information pertaining to herbal remedies, OTCs, and dietary supplements. The goal of this primer is to provide counselors with an introduction to CAMs and OTCs that clients may be taking. It provides an overview of the most frequently used non-prescription psychoactive products, and addresses the actions of these products (pharmacodynamics) and how the body responds (pharmacokinetics) to these substances. The most significant effects as well as side effects are also discussed. In addition, effective communication with clients about prescription and non-prescription drugs is examined. It reviews ethical and cultural considerations pertaining to counseling clients who use psychoactive herbal remedies, OTCs, and dietary supplements. The herbal remedies, OTCs, and dietary supplements selected for this article were those that, based on the literature, appeared to be most commonly used.

Definition of Terms

For the purpose of this article, several terms are defined. For example, pharmacodynamics is the study of how the body responds to a drug. As such, it addresses therapeutic effects as well as side effects (Stahl, 2021). Pharmacokinetics describes how the body absorbs, distributes, metabolizes, and excretes drugs and herbal remedies (He et al., 2011). Drugs and herbal remedies may affect organs, enzymes, and receptor sites. There are receptors located on neurons, which offer binding sites for neurotransmitters. These receptors are designed to respond to specific neurotransmitters. For example, dopamine will only bind to dopamine receptors and will not impact receptors designed for other neurotransmitters (Preston et al., 2021).

There are several neurotransmitters that are considered important in terms of mental health. Neurotransmitters can be agonistic, which means they can activate specific receptors. Neurotransmitters can also exert antagonistic effects by blocking receptor sites and preventing the activation of receptors (Preston et al., 2021). The most important neurotransmitters in terms of mental health are serotonin, dopamine, GABA, norepinephrine, glutamate, and acetylcholine (Stahl, 2021). It is important to note that these neurotransmitters are involved in complex brain functions and often act in combination with other substances and neurotransmitters. Serotonin plays a role in anxiety disorders and depression. Dopamine has been implicated in psychotic disorders as well as bipolar disorder. GABA is considered to be inhibitory to the firing of neurons. Norepinephrine is involved in many functions including memory and mood. Glutamate is an excitatory neurotransmitter. Too much glutamate can lead to cell death. It has been implicated in bipolar disorder and Alzheimer’s disease. Acetylcholine is involved in memory and it has also been implicated in Alzheimer’s disease (Ingersoll & Rak, 2016).

The therapeutic index or window describes the parameter between an effective dose and a toxic dose of a drug. Some drugs such as lithium (used for the treatment of bipolar disorder) have a narrow therapeutic window, meaning that the effective dose and the toxic dose are in close proximity to each other and care must be taken when prescribing these drugs (Preston et al., 2021).

Drugs and herbal remedies may be additive (or synergistic). Additive effects are those in which a drug or herbal remedy may increase or improve the action of another drug or herbal remedy. Drugs or herbal remedies may also act antagonistically, which means the drug/herbal remedy renders another drug/herbal remedy less effective (Sharma et al., 2021). Drug interaction refers to how two or more drugs impact each other in terms of changes in absorption, distribution, metabolism, and excretion (Preston et al., 2021). Half-life refers to the time it takes the body to decrease the blood level of a drug by 50%. The half-life of drugs and herbal remedies can vary greatly, ranging from hours to days (Ingersoll & Rak, 2016). Many herbal remedies and drugs are metabolized through the cytochrome P450 enzymatic system located primarily in the liver and the gastrointestinal system (Stahl, 2021).

Finally, serotonin syndrome can be a life-threatening, adverse reaction to the often unintentional overuse of drugs containing serotonin, or drugs that inhibit serotonin reuptake. Scotton et al. (2019) provided an overview of serotonin syndrome, noting that serotonin serves many functions in the brain and body, including regulating cognitive, emotional, and behavioral functions as well as regulating body temperature and digestion. Serotonin syndrome symptoms can range from mild to severe and can even lead to death. There are a host of symptoms caused by serotonin toxicity (too much serotonin) ranging from diarrhea, tachycardia, agitation, and experiencing tremors to life-threatening symptoms such as delirium, neuromuscular rigidity, hyperthermia, seizures, and coma. The main group of drugs implicated in serotonin syndrome are SSRIs in combination with other serotonergic substances, which also include herbal remedies and OTCs (Scotton et al., 2019). The following sections provide counselors with a detailed overview of herbal remedies and OTCs.

Herbal Remedies

It has been estimated that about 25%–35% of Americans use or have used herbal medicines (Rashrash et al., 2017; Wu et al., 2011). A National Institute of Health survey (Nahin et al., 2009) revealed that in the United States, consumers spent $33.9 billion on CAMs, with $14.8 billion going toward non-vitamin, non-mineral, and natural products (e.g., herbal remedies, melatonin, fish oil, glucosamine). This is roughly equivalent to one-third of the out-of-pocket expenditure for prescription drugs (Nahin et al., 2009). Ravven et al. (2011) found that 44.7% of those using psychiatric prescription drugs also used herbal remedies at the same time.

The WHO defines herbal medicines as consisting of “herbs, herbal materials, herbal preparations, and finished herbal products” (Disch et al. 2017, p. 7). The U.S. Food and Drug Administration (FDA) considers herbal products to be botanicals, which include plant parts, fungi, and algae (FDA, 2015). Many herbal remedies contain compounds that are pharmaceutically active. These compounds can exert an effect on the body or the central nervous system (Sarris, 2018). It has been estimated that about 40% of modern pharmaceuticals originated from naturally occurring treatments (Balick & Cox, 2021). However, in accordance with U.S. laws, herbal remedies or herbaceuticals cannot be marketed as drugs. The FDA is only able to regulate herbaceuticals as dietary supplements. In general, oversight seems marginal in comparison to prescription drugs. For example, manufacturers do not have to seek FDA approval before selling herbal remedies as is required for prescription drugs, and claims made by manufacturers pertaining to dietary supplements are not evaluated by the FDA (A. C. Brown, 2017). Herbal remedies and dietary supplements do not undergo rigorous research and development in the same manner as pharmaceuticals. The FDA is currently only able to monitor those herbal remedies and dietary supplements (and their corresponding ingredients) after they are sold and adverse reactions have been reported, making possible adulteration one of the most worrisome safety concerns pertaining to herbal remedies and dietary supplements (A. C. Brown, 2017). Research has shown that many herbaceuticals are contaminated and are augmented with unlabeled fillers (Crighton et al., 2019; Newmaster et al., 2013). Herbaceuticals can be contaminated by dust and pollen; microbes; parasites; fungi; pesticides; and heavy metals such as lead, arsenic, mercury, and cadmium (de Sousa Lima et al., 2020; Posadzki et al., 2013,: P. Singh et al., 2008). Also, product substitution is a common problem; however, the lack of more effective FDA oversight does not limit herbaceutical popularity or use (Newmaster et al., 2013).

Ravven et al. (2011) estimated that one-quarter to one-third of all herbal remedies in the United States are purchased with the intent to treat mental health conditions, especially anxiety and depression. CAMs such as herbal remedies and dietary supplements can create problems when they interact with medication prescribed by a physician. It is also important to note that many herbal remedies are not harmless; some can cause significant toxic side effects. Counselors must be familiar with the benefits and risks of the more widely used remedies, including St. John’s wort, valerian, kava, ginkgo, and cannabidiol.

St. John’s Wort
     St. John’s wort has been found to be effective in the treatment of mild to moderate depression (Apaydin et al., 2016). There are some indications that it is comparable in effectiveness to tricyclic antidepressants and selective serotonin reuptake inhibitors (SSRIs) while also offering greater tolerability (Zirak et al., 2019). A meta-analysis including 27 studies and 3,808 participants confirmed that St. John’s wort seems to be as effective as SSRIs and tricyclic antidepressants when used in the treatment of depression (Q. X. Ng et al., 2017). St. John’s wort was found to be associated with significantly lower discontinuation rates when compared to prescribed antidepressants, may cause fewer side effects than prescription antidepressants, and might be beneficial for clients who struggle with tolerating the side effects of commonly prescribed antidepressants (Q. X. Ng et al., 2017; Zirak et al., 2019). St. John’s wort is also considered a low-cost alternative to prescription antidepressants (Zirak et al., 2019). It is most frequently taken orally as either a whole herb formulation or as an extract, and can also be prepared as an herbal tea (Kladar et al., 2020).

Despite all the benefits it offers, taking St. John’s wort is not without risks. It acts as an SSRI and can lead to serotonin syndrome if combined with other SSRIs (Apaydin et al., 2016). In addition to affecting serotonin levels, St. John’s wort also impacts the neurotransmitters dopamine, norepinephrine, GABA, and glutamate (Brahmachari, 2018). A main side effect is photosensitivity. It is also possible for St. John’s wort to negatively interact with MAOIs (LaFrance et al., 2000; Sidhu & Marwaha, 2021). In addition, due to cytochrome P450 induction, it also impacts the effectiveness of commonly used medications such as warfarin (used to treat blood clots), ciclosporin (an immunosuppressant), digoxin (for arrythmias and heart failure), some anticonvulsants, oral contraceptives, and other drugs (Barnes et al., 2001; Chrubasik-Hausmann et al., 2019; Sharma et al., 2021). It has been noted that consumers continue to take St. John’s wort in combination with other drugs despite warnings, and it is important that clients receive further education on this topic (Chrubasik-Hausmann et al., 2019).

Valerian
     Valerian root has been used as a sedative and hypnotic since antiquity (Perry et al., 2006). In Europe, valerian is widely used for the treatment of anxiety and sleep disorders (Shinjyo et al., 2020). It is considered to be effective in the treatment of anxiety, certain sleep disorders, some seizure disorders, possibly OCD, cognitive problems, and menstrual and menopausal symptoms (LaFrance et al., 2000; Shinjyo et al., 2020). The medicinal parts of the plant consist of the underground segments and roots and can be ingested as a juice, tea, dried herb, extract, or tincture (Gruenwald et al. 2007). Valerian is thought to enhance GABA transmission and prevent enzymatic breakdown of GABA in the brain (Mulyawan et al., 2020; K. Savage et al., 2018).

No noteworthy adverse side effects seem to occur when it is taken at an appropriate dose (LaFrance et al., 2000; Shinjyo et al., 2020). Effective doses can range from 450mg–1410mg per day for whole herb preparations, and 300mg–600mg per day for valerian extract (Shinjyo et al., 2020). The non–habit-forming properties and limited potential for side effects may be beneficial for some clients (Al-Attraqchi et al., 2020). However, if valerian is combined with hepatoxic drugs, it may increase the risk of hepatoxicity and could lead to liver damage. Also, taking valerian in combination with other sedating drugs or alcohol may result in additive or synergistic effects, resulting in amplification of sedation or intoxication greater than their combined effect; when taken with loperamide (anti-diarrhea drug), it may also cause delirium (Gruenwald et al., 2007).

Kava
     Kava is a medicinal plant belonging to the pepper family with origins in the South Pacific. Traditionally, it has been used as a relaxant. Kava ingested in larger quantities can cause intoxication (Sarris, 2018). Kava is considered to be a hypnotic and a sedative, and it also has analgesic properties (Gruenwald et al., 2007). Hypnotics are drugs that tend to be sleep inducing, whereas sedatives tend to have calming, anxiety-reducing effects (Perry et al., 2006). The medicinally active part of the plant are the rhizomes or creeping rootstalks (Gruenwald et al., 2007). Traditionally, kava beverages were made from the rhizomes; however, in the United States it is mainly available as dry-filled capsule preparations and less commonly as a tincture (Liu et al., 2018). It acts on GABA and has been found to be effective in the treatment of anxiety and insomnia (Gruenwald et al., 2007; LaFrance et al., 2000; Perry et al., 2006; Sarris, 2018). It also has muscle-relaxing, anticonvulsive, and antispasmodic effects (Gruenwald et al., 2007). It is comparable to diazepam in its effectiveness when used to treat anxiety, but it can cause elevation of liver enzymes, which may be an indication of inflammation or even damage to liver cells (Gruenwald et al., 2007; Pantano et al., 2016). When combined with benzodiazepines, kava can cause disorientation and lethargy due to an additive effect in which both substances bind to similar neuron receptors (Surana et al., 2021; Tallarida, 2007).

It is important to note that in the 1990s, Germany approved the use of kava to treat anxiety-related disorders. In 2001, it was banned in Germany and across the European Union because of concerns over liver toxicity. The FDA issued a consumer advisory warning pertaining to the use of kava (Liu et al., 2018). Additional findings indicated only limited risk of liver toxicity when kava was used appropriately, and in 2015 the kava ban in Germany was lifted; however, kava products remain strictly regulated and monitored. In the United States, kava remains available over the counter (Liu et al., 2018).

Ginkgo
     Ginkgo has been used in Chinese medicine for a millennium. The herbal remedy is derived from an ancient tree native to China, Japan, and Korea (Gruenwald et al., 2007; Ingersoll, 2005). Ginkgo biloba extract is made from the ginkgo tree leaves (S. K. Singh et al., 2019). It can be difficult to obtain a high-quality product because of poor oversight and regulation of herbal remedies (Booker et al., 2016); however, a standardized ginkgo biloba extract (EGb761) is available (Hashiguchi et al., 2015). Ginkgo shows some effectiveness in the treatment of dementia, Alzheimer’s disease, and other neurodegenerative disorders (S. K. Singh et al., 2019). Several meta-analyses have confirmed the effectiveness of ginkgo biloba. For example, a meta-analysis conducted by Liao et al. (2020) that included seven studies and 939 participants found that standardized gingko extract was effective in improving cognitive function in Alzheimer’s patients. It has been shown that ginkgo has anti-inflammatory, vascular, and cognition enhancing effects. Ginkgo is considered a GABA agonist as well as an antioxidant (S. K. Singh et al., 2019). In addition to improving cognitive function, it may also lessen oxidative damage, which has been implicated in the development of Alzheimer’s disease (S. K. Singh et al., 2019; Solas et al., 2015). Ginkgo appears to be effective in the treatment of mild to moderate memory loss in the elderly and it may slow the deterioration rate in severe dementia. In addition to neuroprotective properties, ginkgo also appears to be effective in the treatment of asthma, depression, and vascular deficiencies (S. K. Singh et al., 2019.) In terms of adverse effects, it may cause mild gastrointestinal upset, and it may also lower the seizure threshold in vulnerable individuals (Gruenwald et al., 2007).

Cannabidiol
     Cannabidiol (CBD) is an active compound found in the cannabis plant (FDA, 2020a) and is most commonly promoted online as a remedy for anxiety and physical pain (Tran & Kavuluru, 2020). It also has promising potential for anti-inflammatory effects and has shown positive results in treating schizophrenia and social anxiety disorder (Burstein, 2015; Millar et al., 2019). CBD is a cannabinoid system modulator (Darkovska-Serafimovska et al., 2018) and differs from delta-9-tetrahydrocannabinol (THC) in that it does not produce intoxication (Burstein, 2015). The FDA has approved EpidiolexTM, a prescribed CBD-derived oral solution, for use with treating rare forms of epilepsy (FDA, 2020a).

Although under federal law it is currently illegal to add CBD to food or beverages, individual states have differing laws regarding the distribution of CBD, so the dosage of CBD products remains mostly unregulated (FDA, 2020b). Researchers examined 84 CBD products including vaporization liquids, oils, and tinctures and found that 69% of dosage labels were inaccurate (Bonn-Miller et al., 2017). Although unlikely, it is possible for consumers to test positive for THC in some drug screening tests because up to 0.3% THC may be allowed in CBD products in the United States (Gerace et al., 2021; Spindle et al., 2020). CBD taken in combination with other drugs can cause adverse drug reactions and drug–drug interactions (J. D. Brown & Winterstein, 2019). For example, when CBD is taken with a benzodiazepine (e.g., alprazolam for anxiety), it can increase the risk of side effects of alprazolam. It should be noted that researchers mainly examined EpidiolexTM in studies exploring drug–drug interactions and adverse side effects, as the CBD dosage is controlled in this formulation (J. D. Brown & Winterstein, 2019). Because of the wide dosage variance in unregulated CBD products, it is difficult to research and predict the effects. In a review of clinical studies, the therapeutic window appears to be wide, but phase III trials have not been conducted to provide conclusive evidence (Millar et al., 2019).

Over-the-Counter Medications

Globally, in 2017 the OTC market reached $80.2 billion in consumer spending (PR Newswire, n.d.) and research indicates that 81% of American adults reach for OTCs, or medicine that can be purchased without a prescription, as an initial treatment for minor medical conditions. The average American makes 26 trips to OTC outlets compared to three doctor’s visits annually, and there are around 54,000 pharmacies in the United States compared to over 750,000 retailers that sell OTCs (Consumer Healthcare Products Association, n.d.). Despite the popularity of OTCs, many clients lack the required health knowledge to safely self-medicate.

Acetaminophen
     Many consumers do not know that an overly high dose of acetaminophen could be lethal, or that varying OTCs contain acetaminophen and taking more than one of these products simultaneously might lead to an unintentional overdose (Boudjemai et al., 2013; Wolf et al., 2012). There are a number of OTCs that have psychotropic properties. For example, Durso et al. (2015) found that acetaminophen blunts more than just pain—it seems that the OTC pain medication also diminishes emotional responses to both negative and positive events. Researchers went so far as to label acetaminophen as an “all-purpose emotional reliever” (Durso et al., 2015, p. 756). In addition, it is of interest to note that acetaminophen decreases a person’s ability to empathize with pain experienced by others (Durso et al., 2015). Roughly one-quarter of American adults are taking this drug on a weekly basis. It begs the question as to the societal implications or social cost of its frequent use (Mischkowski et al., 2016, 2019).

Sleep Aids
     It is common for people to experience trouble with falling asleep or staying asleep. The Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013) indicates that one-third of adults in the United States experience insomnia symptoms. This issue is evident in consumer spending: In 2018 Americans spent $410 million on OTC sleep aids (Consumer Healthcare Products Association, n.d.).

Diphenhydramine and doxylamine are OTC antihistamines with considerable sedative properties and are marketed as treatment options for sleep disturbances (Perry et al., 2006). It was found that doxylamine seems to be as effective as the barbiturate secobarbital; also, doxylamine is comparable to zolpidem, a frequently prescribed sleep aid. Diphenhydramine and doxylamine are considered to be non-selective histamine H1 receptor antagonists (antihistamines for the prevention of allergies) and they are also anticholinergic (causing dry mouth, constipation, urinary retention, blurred vision, and sedation; Perry et al., 2006). Abraham et al. (2017) found that 58.6% of the elderly sample surveyed used at least one sleep aid containing diphenhydramine or doxylamine.

Chlorpheniramine is also an OTC antihistamine, and it can be found as the sole active compound in remedies such as Chlor-TrimetonTM and similar generic formulations (Hellbom, 2006), or in combination with other substances to treat cold and allergy symptoms. Popular cold remedy combinations of chlorpheniramine and dextromethorphan (a cough suppressant also available over the counter) can be problematic. Dextromethorphan is a moderate SSRI (Boyer & Shannon, 2005; Foong et al., 2018), which means it acts like an SSRI antidepressant. Furthermore, diphenhydramine and chlorpheniramine have also been found to block serotonin reuptake, making them some of the oldest SSRIs (Foong et al., 2018; Hellbom, 2006; Ravina, 2011). It is not commonly known that fluoxetine (Prozac®) was derived from diphenhydramine as a result of attempts to make this drug less sedating (Ravina, 2011).

Despite the fact that these products are readily available over the counter, drugs like diphenhydramine as well as doxylamine are not designed for the long-term treatment of sleep disorders (Abraham et al., 2017). There is a lack of supporting literature in terms of using these drugs for treatment of mental health concerns (Culpepper & Wingertzahn, 2015). It is important to note that if clients are prescribed an antidepressant, chlorpheniramine as well as diphenhydramine can increase the risk of serotonin syndrome (Abraham et al., 2017). It is also important to keep in mind that diphenhydramine can be found in combination with pain relievers/fever reducers such as acetaminophen. This may add to the risk of developing serotonin syndrome because clients may not be aware of the exact content of these formulations (Abraham et al., 2017). Diphenhydramine may also be a drug of abuse. When taken in high doses, it may create a buzz or high because of possible activation of the dopamine-related reward pathways of the brain, which may lead to drug-seeking behaviors (Saran et al., 2017). Finally, a lethal dose of doxylamine can range from 25mg–250mg per kg in body weight (Müller, 1992, as cited in Bockholdt et al., 2001). Doxylamine overdose symptoms include respiratory depression, sedation, and coma (Bockholdt et al., 2001).

Dietary Supplements

Dietary supplements are defined as dietary ingredients that include vitamins, minerals, amino acids, and herbs or botanicals, as well as other substances that can be used to supplement the diet (FDA, 2015). Much like herbal remedies, the FDA does not sufficiently regulate dietary supplements.

Melatonin
     Melatonin is a naturally occurring substance that is synthesized from tryptophan. It is secreted by the pineal gland in order to regulate the circadian rhythm. Melatonin is effective in inducing sleep when taken orally as well. In the United States, synthesized melatonin is marketed as a dietary supplement and can be purchased over the counter in doses ranging from 0.3mg–10mg (Perry et al., 2006).

Because the FDA does not sufficiently regulate melatonin, it is important to note that specific dosing guidelines do not exist (R. A. Savage et al., 2020). However, studies have found that doses over 5mg are no more effective than lower doses. Side effects may include headache, fatigue, dizziness, irritability, abdominal cramps, itchiness, and elevated alkaline phosphatase in long-term use (Perry et al., 2006). Furthermore, it was found that the labeled concentration of melatonin content frequently does not match actual content. Erland and Saxena (2017) found variability of melatonin in various samples ranging between ˗83% (lesser dose) to +478% (higher dose). Erland and Saxena also found that eight of their 30 samples contained undisclosed/unlabeled serotonin in addition to melatonin, which may add to health concerns. The majority of supplements that were found to include serotonin also contained other additives such as passionflower, hops, and valerian root. Interestingly, serotonin is a precursor to melatonin (Erland & Saxena, 2017). Unlabeled serotonin content poses a significant problem because many clients self-prescribe melatonin supplements and, under the right circumstances, a relatively small dose can lead to serotonin syndrome (Erland & Saxena, 2017).

SAMe
     SAMe (S-Adenosyl-L-methionine) is required for the brain to synthesize the neurotransmitters norepinephrine, dopamine, and serotonin. In the United States, SAMe has been widely available over the counter since the late 1990s (Mischoulon & Fava, 2002). The general consensus is that it is effective in treating depression (Sakurai et al., 2020). Also, SAMe can be utilized as an adjunct to antidepressant medications (Papakostas, 2009; Sakurai et al., 2020). It can be taken orally or be administered by intravenous infusion (Sakurai et al. 2020). A recommended dose of SAMe can range from 400mg–1600mg per day; however, some individuals may have to take a higher dose to achieve improvement of depressive symptoms (Mischoulon & Fava, 2002; Olsufka & Abraham, 2017; Sakurai et al., 2020). Overall, use of SAMe results in little to no side effects, although at higher doses SAMe may cause gastrointestinal discomfort (Sakurai et al., 2020). In clients diagnosed with bipolar disorder it may cause anxiety and mania (Mischoulon & Fava, 2002; Olsufka & Abraham, 2017).

Tryptophan
     Tryptophan is an amino acid that the body requires to synthesize proteins (Modoux et al., 2020). Tryptophan is also needed to synthesize serotonin and melatonin (Modoux et al., 2020). Tryptophan was available in the United States in the 1990s. At that time, there was some evidence that it might be effective in treating depression (Perry et al., 2006). Tryptophan was taken off the market after there were concerns that it caused several deaths because of eosinophilia-myalgia syndrome (EMS), an inflammatory disorder that affects multiple body parts and causes high white blood cell counts. There was some speculation that in these cases the ingested tryptophan may have been contaminated (Perry et al., 2006). Tryptophan can now be purchased over the counter again; however, Perry et al. (2006) suggested that because of EMS risks, clients should be encouraged to consult with their physician before taking this product.

The Role of the Counselor

Concerns regarding psychotropic medication can find their way into counseling settings. Clients may take any number of drugs, ranging from prescribed psychotropic medications to herbal remedies, OTCs, and dietary supplements. In order to be able to provide effective counseling services, counselors must attempt to understand the role these drugs play in clients’ lives. Areas to consider include education, assessment, diagnosis, case conceptualization, treatment planning, and client advocacy, such as referral and consultation with medical and psychiatric treatment providers.

Education
     Clinicians should be knowledgeable about the intended use of prescribed psychoactive medications as well as herbal remedies, OTCs, and dietary supplements. It is also important to be familiar with route of administration, pharmacokinetics/pharmacodynamics, therapeutic effects, side effects, and contraindications. CAMs frequently fall in and out of favor because of marketing efforts and fads (Crawford & Leventis, 2005; Smith et al., 2017). Consequently, in order to stay abreast of current trends, it is prudent to pursue continuing education in this area. Counselors should be skilled in nonjudgmentally addressing CAMs and OTCs in a variety of areas, including assessment, education, and referrals.

CACREP’s 2016 standards require that counseling students receive education in the “classifications, indications, and contraindications of commonly prescribed psychopharmacological medications for appropriate medical referrals and consultation” (CACREP, 2015, Section 5.2.h., p. 18). Many states, including Texas (Professional Counselors, 2021), require psychopharmacology training for counselor licensure. It could be argued that this education should also extend to herbal remedies, OTCs, and dietary supplements.

Assessment
     Counselors also have the option to be proactive and include questions inquiring about CAMs, OTCs, and prescription medication during the intake process, as well as intermittently throughout the counseling relationship with clients. Assessment may include questions about dosage, frequency, and reason for use. Because clients may not think to share CAM and OTC use with counselors, direct questions during the intake process can initiate conversations about psychoactive drugs. Counselors also have the opportunity to educate clients on the biopsychosocial impact of psychoactive drugs that may play a role in their presenting concerns (Kaut & Dickinson, 2007). Assessment also allows counselors to educate clients on the risks and benefits of CAM and OTC use.

Diagnosis
     Knowledge about clients’ use of herbal supplements, OTCs, and dietary supplements is important, as clients may unknowingly experience substance-induced problems. For example, garcinia cambogia, a popular weight-loss herbal supplement, can induce mania (Hendrickson et al., 2016). Clients who have taken garcinia cambogia may present with manic symptoms such as grandiosity, decreased need for sleep, irritability, and hallucinations (Hendrickson et al., 2016). Psychosis has also been induced by L-dopa and dendrobium extract, found in OTC performance-enhancing supplements (Flynn et al., 2016), and by herb–herb interactions when taking multiple supplements simultaneously (Wong et al., 2016). Because of the potential for substance-induced problems, counselors should make differential diagnoses by discussing all potential conditions that may be causing the client’s symptoms, which includes ruling out substance etiology (First, 2013).

Case Conceptualization
     To understand the nature, history, and context of clients’ presenting concerns, counselors should engage in a case conceptualization process. Macneil et al. (2012) recommended considering predisposing, precipitating, perpetuating, and protective/positive factors that may contribute to or alleviate the client’s presenting concerns. Counselors should consider how herbal supplements, OTCs, and dietary supplements may be a precipitating, perpetuating, and/or positive factor, as these substances may contribute to or alleviate clients’ symptoms.

Treatment Planning
     Counselors consider a client’s diagnosis, presenting concerns, and case conceptualization information to make a personalized treatment plan (Macneil et al., 2012). If CAMs and OTCs are relevant to the client’s treatment, counselors may include the monitoring of such substances as an intervention. This would include assessing the client’s use and compliance with their medication regimen, inquiring about side effects, and evaluating how these factors relate to the client’s mental health. Counselors should only practice within the scope of their license, and clients must be referred to qualified medical providers for any medical or medicinal concerns. Counselor roles may include the referral of a client to a specialist such as a psychiatrist for medication evaluation as a component of the client’s treatment plan. Counselors should ensure that physicians they refer to provide quality care.

Client Advocacy
     Counselors may advocate for their clients and consult with prescribers on clients’ behalf (Bentley & Walsh, 2013). Again, a significant concern is that clients frequently do not discuss the use of alternative treatments with their physician (Abraham et al., 2017; Agbabiaka et al., 2017). Direct inquiry into the use of CAMs and OTCs and client education can bring about greater clarity and the opportunity to ask clients to discuss these with their medical providers (Agbabiaka et al., 2017). Counselors can encourage and educate clients on how to discuss CAMs and OTCs with their physician or psychiatrist. When assessing, educating, referring, and advocating, counselors must abide by ethical and legal standards.

Ethical Considerations

It is important to note that counselors should under no circumstances recommend herbal remedies, OTCs, or dietary supplements to clients because doing so would be outside of the scope of their practice (ACA, 2014; Ingersoll & Rak, 2016). The ACA (2014) Code of Ethics specifies that “counselors practice only within the boundaries of their competence, based on their education, training, supervised experience, state and national professional credentials, and appropriate professional experience” (Section C.2.a, p. 8). Despite this, professional role boundaries related to psychopharmacology between prescribing physicians and counselors can be unclear at times (Ingersoll & Rak, 2016). For example, clients may ask counselors for advice on medication. So, in addition to keeping abreast of trends in the use of CAMs and OTCs and attending to this during intake and work with clients, developing consultation and referral resources in this area is an important consideration for counselors (Preston et al., 2021). Resources may vary from state to state given differences in licensing and certification of health professionals and general prescribing privileges for psychotropic medications.

There are wide-ranging opinions among counselors pertaining to prescribing psychotropic medications to clients (Ingersoll & Rak, 2016). These opinions cannot dictate whether a client is referred to the medical community for medication evaluation. Counselors are ethically obligated to refer clients to a medical professional when necessary, including referrals related to pharmacotherapy as well as non-prescription drugs, herbal remedies, or dietary supplements. Withholding such a referral may constitute malpractice. The ACA (2014) Code of Ethics states that “counselors act to avoid harming their clients, trainees, and research participants and to minimize or to remedy unavoidable or unanticipated harm” (ACA, 2014, Section A.4.a., p. 4) and also specifies that “counselors are aware of—and avoid imposing—their own values, attitudes, beliefs, and behaviors” (ACA, 2014, Section A.4.b., p. 8).

Diversity and Cultural Considerations
     It is important that counselors are able to discuss racial and cultural considerations with clients to ensure competence and to promote the welfare of clients (ACA, 2014). Our commitment to diversity and inclusion must also be extended to clients who are taking psychoactive substances and herbal remedies. It should be noted that genetic research has found that there are a number of significant differences in terms of drug metabolism, effectiveness, and side effects among ethnic groups (Burroughs et al., 2002). At the same time, race, age, and gender can be crude or flawed yardsticks for predicting responsiveness to drugs; however, counselors do need to be aware that there are significant variations in response to drugs based on multiple factors, and that these variations are more the norm than the exception (Bhugra & Bhui, 2018; Burroughs et al., 2002).

Further, racial and ethnic disparities persist in health care, and this may contribute to clients’ decisions to take CAMs and OTCs (Gureje et al., 2015). Less than 6% of active physicians are Hispanic and less than 5% are Black (American Association of Medical Colleges, 2019), even though 40% of Americans are non-White or Hispanic (U.S. Census Bureau, 2020). This can create barriers to obtaining and providing appropriate care, as it has been found that racial or ethnic minority clients are less likely than their White counterparts to receive prescriptions to treat their mental health conditions (Coleman et al., 2016). This inequality may lead clients to seek CAMs or OTCs to treat mental health issues (Coleman et al., 2016; Gureje et al., 2015).

Counselors should consider cultural factors such as a preference for herbal remedies, immigration status and language use, socioeconomic status, and availability of insurance coverage. Traditional medicine often involves the use of herbal remedies and is closely connected to one’s culture, so counselors should be mindful to discuss CAMs with clients in a nonjudgmental and empathetic manner. Traditional forms of medicine have a long history, having evolved over thousands of years (Gureje et al., 2015). Depending on historical or cultural background, there are numerous ways in which these healing methods are being implemented (Gureje et al., 2015).

It is also important for counselors to recognize that traditional medicine is commonly used in middle- and low-income countries and that transplants from these cultural groups in the United States may use or even prefer these types of healing approaches (Gureje et al., 2015). Poverty also plays a role in the use of traditional medicine versus conventional medicine. For many, traditional medicine may be the only affordable or accessible health care option (Gureje et al., 2015). In Mexican culture, individuals may seek assistance from curandera/os for physical or psychological issues (Hoskins & Padrón, 2018). Traditional medicine may be used to treat nervios, depression, and anxiety (Guzmán Gutierrez et al., 2014). For example, an infusion of the yoloxchitl (magnolia) plant may be used to treat nervios, a culture-specific syndrome that can share symptoms of depression and anxiety (Guzmán Gutierrez et al., 2014). Because curanderismo is also a spiritual practice, counselors should be sensitive to the values that may be tied to the use of herbs for mental health concerns.

In addition, some clients prefer to use traditional medicine as well as conventional medicine (Gureje et al., 2015). Although countries such as China and India are formally supporting the integration of traditional and conventional medicine (Gureje et al., 2015), Western medicine and traditional herbal medicine use are not always compatible (C. H. Ng & Bousman, 2018). Because traditional medicine practices are culture-specific, asking clients if they utilize traditional medicine can be an invitation to share about their practices and allow counselors to approach their clients holistically.

Conclusion

There is a growing need for counselors to possess a working knowledge not only of prescribed psychotropic medications, but also of herbal remedies, OTCs, and dietary supplements. As more training programs and licensure boards require psychopharmacology education, counselors should be invested in learning about other psychoactive products clients may be taking. Counselors have the opportunity to assess clients’ use of CAMs and OTCs and consider how they may be relevant to diagnosis, case conceptualization, and treatment planning. In addition, counselors can educate clients about psychoactive products and their impact on mental health. Counselors can also provide referrals and serve as advocates for their clients when working with prescribing providers. From an ethical perspective, psychopharmacology knowledge is increasingly required in order to provide adequate client care. Although this may appear to move counseling practices more toward the medical model, in reality it means the profession is responding to current trends in counseling and client needs. Understanding the potential impact of herbal remedies, OTCs, and dietary supplements on clients’ mood, thinking, and behavior is imperative to understand the whole person and to maintain a holistic counseling approach.

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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Sabina Remmers de Vries, PhD, NCC, LPC-S, is an associate professor at Texas A&M University–San Antonio. Christine D. Gonzales-Wong, PhD, NCC, LPC, is an assistant professor at Texas A&M University–San Antonio. Correspondence may be addressed to Sabina de Vries, One University Way, San Antonio, TX 78224, sabina.devries@tamusa.edu.