School Counseling in the Aftermath of COVID-19: Perspectives of School Counselors in Tennessee

Chloe Lancaster, Michelle W. Brasfield

The COVID-19 pandemic led to an unparalleled disruption of student learning, disengaged students from school and peers, increased exposure to trauma, and had a negative impact on students’ mental health and well-being. School counselors are the most accessible mental health care professionals in a school, providing support for all students’ social and emotional needs and academic success. This study used an exploratory survey design to investigate the perspectives of 207 school counselors in Tennessee regarding students’ COVID-19–related mental health, academic functioning, and interpersonal skills; interventions school counselors have deployed to support students; and barriers they have encountered. Results indicate that students’ mental health has significantly declined across all grade levels and is interconnected with academic, social, and behavioral problems; school counselors have provided support consistent with crisis counseling; and caseload and non-counseling duties have created significant barriers in the provision of care.

Keywords: COVID-19, school counselors, student mental health, interventions, barriers

The psychological cost of the COVID-19 pandemic has been profound and wide-reaching. Although the K–12 population has been less susceptible to the adverse physical effects of COVID-19, for many, the pandemic has left an indelible mark on their mental health (Karaman et al., 2021). Before the outbreak of COVID-19 in 2020, youth mental health had become an issue of national concern, with one in six minors struggling with mental illness (Whitney & Peterson, 2019). Research has emerged to indicate that COVID-19 has further elevated the mental health problems of K–12 students across the nation (Ellis et al., 2020; Karaman et al., 2021; Magson et al., 2021). The end of COVID-19 lockdown restrictions may have alleviated immediate issues associated with social isolation and online learning; however, for those students experiencing COVID-19–related trauma and crisis, symptomatology has persisted beyond school reentry (Centers for Disease Control and Prevention [CDC], 2022; Patterson, 2022). As frontline helping professionals with training in mental health and school systems, school counselors are often the first responders to students in crisis (Karaman et al., 2021; Lambie et al., 2019), yet researchers have not explored reentry problems from the school counselor’s perspective. We conducted this study to understand school counselors’ experience of COVID-19–related student issues, their strategies to assist students, and their encountered barriers. We theorized that persistent problems related to the organizational structures within which counselors work, such as large caseloads, assignment of non-counseling duties, and under-resourced schools and communities (Lambie et al., 2019), may have greatly impacted their ability to meaningfully help students in high need of mental health support.

Literature Review

Students and COVID-19–Related Distress
     From the outset of the COVID-19 pandemic, scholars predicted that disruptions to schooling, COVID-19–related stress, family conflict, and frequent media exposure to the pandemic would amplify mental health problems in children and youth (Imran et al., 2020). Empirical studies published in 2020 and 2021 have substantiated this concern, with findings indicating that COVID-19 restrictions adversely affected youth in multiple ways, including the development of unhealthy eating habits, increased screen time, reduced physical activity, sleep disturbances, academic delays, social problems, and an overall escalation in mental health concerns (Ellis et al., 2020; Karaman et al., 2021; Magson et al., 2021). The preponderance of research focused on adolescents, particularly as extended time in social isolation disrupted their developmental reliance on peer interactions for social and emotional support (Imran et al., 2020). Multiple studies found that not feeling connected to friends, high social media usage, and general COVID-19–related fears were associated with higher levels of depression and anxiety (Ellis et al., 2020; Karaman et al., 2021; Magson et al., 2021).

Although less is known about the impact of COVID-19 on younger children, evidence is emerging to indicate that the COVID-19 pandemic has elevated adverse childhood experiences (ACEs; Bryant et al., 2020). From a developmental perspective, children are less able to communicate and process their thoughts and feelings and are greatly affected by the emotional state of their caregivers (Zimmer-Gembeck & Skinner, 2011). Thus, exposure to parental anxieties related to housing, food, and economic insecurity likely exerted a destabilizing effect on children during the stay-at-home mandate and beyond (Imran et al., 2020). Further, children in poverty may be particularly vulnerable to an amplification of ACEs due to their families being disproportionately impacted by economic hardships and family mortality during the pandemic (Bryant et al., 2020).

Students’ Mental Health Pre-Pandemic
     The COVID-19 pandemic increased intra-family adversity, which has long-term implications for the well-being of children and adolescents (CDC, 2022). However, in pre–COVID-19 times, with the rise in school shootings and teen suicide, the mental health of K–12 populations had already become a public health concern. According to the National Alliance on Mental Illness, one in six children aged 6–17 experienced a mental health disorder (Whitney & Peterson, 2019). Since reentry following COVID-19 shutdowns, indicators suggest the COVID-19 pandemic has worsened children’s mental health (CDC, 2022; Karaman et al., 2021), with widespread reports of student learning gaps, chronic absenteeism, declines in social skills, and increased behavior problems (CDC, 2022; Patterson, 2022). Further, previous research on children’s responses to a variety of traumatic events has found that children and adolescents can develop long-term mental illness following a traumatic experience, which is unlikely to abate without intervention (Udwin et al., 2000). For youth, the experience of mental health problems increases their risk factors in other areas, such as a decline in academic performance, poor decision-making, drug use, and high-risk sexual behaviors (CDC, 2022). In this regard, the responsiveness of schools to flex their organizational resources to address the psychological changes in their student body seems instrumental in assuaging the long-term effects of COVID-related trauma and the mitigation of adverse educational outcomes (Savitz-Romer et al., 2021).

School Counselors’ Role in Provision of Mental Health Services
     Schools have long been discussed as a primary access point for mental health services, given that children spend much of their day in school, and children and adolescents in need of mental health care are more likely to receive assistance in a school as opposed to a clinical setting (Lambie et al., 2019). Conversations about students’ access to mental health care in school settings segue to the role of school counselors and students’ access to school counseling services. School counselors are the most accessible mental health care professionals in schools, with 80.7% of schools employing full-time or part-time school counselors (Lambie et al., 2019). By contrast, only 66.5% employ a school psychologist, and 41.5% employ a school social worker (National Center for Educational Statistics, 2016). Further, school counselors are trained in crisis prevention and responsive services, including individual and group counseling; consultation with administrators, teachers, parents, and professionals; and coordination of services within a multi-tiered system of supports (MTSS; Pincus et al., 2020).

Evidence to support school counselors’ work in times of crisis comes from multiple sources. Salloum and Overstreet (2008) found that a school counselor–led small group implemented after Hurricane Katrina improved PTSD symptoms among elementary school students. Similarly, Udwin and colleagues (2000) found that students who received psychological support at school following a national crisis experienced a reduction in PTSD symptomology. Additionally, scholars have proposed that school counselors utilize their skill set in assessment to administer universal mental health screenings to identify students at greater risk of having or developing mental health concerns (Lambie et al., 2019; Pincus et al., 2020).

Barriers School Counselors Face in the Provision of Services
     Although school counselors have the training and skills necessary to assist students transitioning back to school from a disruption like COVID-19, they face multiple barriers to their work. Most notably, they struggle with unmanageable caseloads. The American School Counselor Association (ASCA) recommends that counselor-to-student ratios not exceed 1:250 (ASCA, 2019). Yet, the average ratio in the United States is 1:455, with Tennessee experiencing an average ratio of 1:450 (Patel & Clinedinst, 2021). Research indicates that large school counselor caseloads adversely affect student outcomes, insofar as attendance, graduation, and disciplinary problems are more prevalent in schools with high school counselor caseloads (Parzych et al., 2019). Unfortunately, minority students in under-resourced schools are disproportionately impacted by high counselor ratios (Whitney & Peterson, 2019) and are more likely to experience adverse educational outcomes, as well as unmet mental health needs (Kaffenberger & O’Rorke-Trigiani, 2013). These findings raise concern for students whose mental health and academics have declined since the emergence of COVID-19 who attend schools with overstretched counselors struggling to meet the needs of their student body. This study was conducted in part to explore if caseload correlates to school counselors’ perceived ability to attend to students’ COVID-related problems and if differences were more pronounced in schools with lower socioeconomic status (SES).

In addition to ratios, ASCA recommends that school counselors spend 80% of their time providing direct and indirect services to students. Program elements within direct service include curriculum delivery, individual student planning, and responsive services. Indirect services include referrals to other agencies and programs within and outside the school system and consultation and collaboration with stakeholders, particularly for crisis response (ASCA, 2019). Researchers have documented the favorable effects on student academics and behaviors when school counselors follow these national guidelines for time and role allocations (Cholewa et al., 2015). Nonetheless, school counselors are often assigned non-counseling duties by their campus and district administrators (Gysbers & Henderson, 2012), preventing them from fulfilling their appropriate roles. These duties include test coordination, record keeping, attendance monitoring, substitute teaching, and student discipline (ASCA, 2019). Data indicate that non-counseling duties may be more problematic at the secondary level, with high school counselors over-reporting non-counseling duties, when compared to elementary school counselors (Chandler et al., 2018). Geographic differences have also been documented, with rural school counselors reporting higher levels of non-counseling duties in comparison to urban school counselors (Chandler et al., 2018). In the current study, we were curious to understand the impact of non-counseling duties on school counselors’ response to students’ COVID-19 concerns and to explore the intersection of counselor responsiveness to COVID-19 by non-counseling duties, grade level, and geographic region (e.g., urban, suburban, rural), respectively.

School Responses to COVID-19 in Tennessee
     In response to the COVID-19 pandemic, Tennessee’s governor ordered all Tennessee public schools closed from March 20 until March 31, 2020, and extended this closure through the end of the 2019–2020 school year. To complete the school year outside of the physical educational space, districts created their own plans to address student learning, often dependent on available technology and resources (Tennessee Office of the Governor, 2020). Districts made decisions for returning in the fall 2020 semester based on guidelines from the Tennessee Department of Education (DOE), which included social distancing, smaller class size, assigned seats, and alternating in-person days with distance learning (Tennessee DOE, 2020). To provide further context to our survey responses, in 2019, the state DOE (Tennessee State Board of Education, 2017) updated its school counseling policy and standards to require school counselors to spend 80% of their time in direct service to students, a specification consistent with the ASCA National Model for allocation of school counselor time. Although the policy stated counselor ratios should not exceed 1:500 in elementary and 1:350 in secondary schools, this specification falls short of the ASCA 1:250 recommendation. Further, because of the state funding formula that permits school districts to hire administrators in lieu of school counselors, depending on school needs, we expected many of the school counselors would have caseloads that exceeded DOE policy.

Purpose of Study
     School counselors are uniquely positioned to assist students with their mental health, including COVID-19–related concerns, in a school context (Pincus et al., 2020). Yet, even before the COVID-19 pandemic, school counseling programs were frequently under-equipped to meet the magnitude of students’ mental health needs (DeKruyf et al., 2013). This study was conducted to understand, from the perspective of school counselors in Tennessee, the ongoing impact of COVID-19 upon students’ mental health, examine strategies they have deployed to assist students, and discover barriers encountered in providing care to meet their students’ needs. Because poor mental health manifests in a plethora of academic, behavior, and social skill adjustment issues for children and adolescents (CDC, 2022), we also examined school counselors’ perceptions of changes in those domains from pre-pandemic to current times. Given documented patterns of variability in school counselor programs, we also investigated school counselors’ perceived barriers to assisting students by location, SES, and assigned non-counseling duties. To address the aim of the study, we posited three related research questions (RQs):

RQ1: How has COVID-19 affected students’ mental health, academics, and social skills in Tennessee? What issues presented the greatest concern, and how did interventions differ by grade level (elementary, middle, or high school)?
RQ2: What interventions do school counselors in Tennessee use to assist students with their COVID-19–related concerns, and how do interventions differ by grade level (elementary, middle, or high school)?
RQ3: What barriers do school counselors in Tennessee report as interfering with their ability to address students’ COVID-19 concerns? Do reported barriers differ by grade level (elementary, middle, or high), location (urban, suburban, or rural), socioeconomic status, non-counseling duties, size of caseload (small, medium, or large), or following the state guideline for spending 80% of the time in student services?

 

Method

Study Design and Instrumentation
     Given the absence of research examining school counselors’ perspectives of how the pandemic has affected student mental health, their response to students’ COVID-19 issues, and barriers encountered in their efforts, we employed an exploratory research design. Exploratory designs are used when there is limited prior research to warrant the examination of a directional hypothesis (Swedberg, 2020). Within the framework of an exploratory design, we developed a non-standardized instrument to answer the three research questions. Although this constitutes a limitation of the study, we endeavored to address validity concerns by following the principles of the tailored design method of survey research (Dillman, 2007). Prior to constructing the survey, we reviewed the extant literature on students’ COVID-19–related issues, school counselors’ roles, and professional issues, in addition to conducting a focus group (N = 7) with school counselors and school counseling supervisors from across the state in which the study was conducted to explore their perceptions in changes to student functioning, strategies they have deployed to assist students, and obstacles they have encountered. Focus group data were used to inform the development of survey items and ensure the instrument covered relevant content. For example, the focus group provided expert insight into the non-counseling duties that are frequently assigned to counselors in the state, as well as the nature of students’ psychological, academic, and behavioral problems witnessed since the onset of COVID-19. Before launching the survey, we piloted the survey with 19 school counselors in Tennessee to elicit feedback about the flow and coverage of the survey. Based on their responses, we added an item addressing universal intervention and edited language on multiple items to align with state-specific terminology (e.g., “MTSS coordination” was expanded to “RTI2B/MTSS/PBIS coordinator” to reflect more state-recognized school counselor titles when operating in these capacities).

The final survey consisted of 64 items in predominantly binary, checkbox, and Likert scale formats. Demographic items were informed by categories outlined by the U.S. Census, the Tennessee DOE, and inclusive practices for data collection (Fernandez et al., 2016). Twenty-one items gathered demographic data related to school counselor characteristics (e.g., age, race, gender), counseling program variables (e.g., caseload, division of time, non-counseling duties, fair-share responsibilities), and school variables (e.g., school level, Title I status, location, staffing patterns). SES was measured using a school’s designated Title I status, with response categories of “yes,” “no,” and “unsure.” Likewise, to determine if school counselors dedicated 80% of their time to direct service, we created a multiple-choice item with the options of “yes,” “no,” and “unsure.” A concise description of the state guidelines was embedded into the survey to promote accurate responses to this item. We gathered data on counselors’ perspectives of their students’ current functioning in areas of mental health, academics, social skills, and behaviors through multiple-choice items with a 5-point range of “much better” to “much worse.” For each area of functioning, school counselors were required to indicate the areas of concern via a checkbox item. Additionally, checkbox items were used to identify school counselors’ strategies to assist students, barriers encountered, and needed resources. As noted, these response categories were based on extant literature and expert input.

Cronbach’s alphas were computed to determine the reliability of the survey items in indicating overall post–COVID-19 functioning of students according to school counselors. These values indicate that these four areas were moderately related with acceptable consistency (α = .653). When making additional comparisons among the four constructs, two areas—behavior and social skills—were found to be more consistent (α = .705; Sheperis et al., 2020). Further, reliability scores likely reflect the exploratory design, which requested participants respond to conceptually related but not converging constructs (e.g., academics, mental health, social skills, and behavior). For example, a change in student academics would not necessarily signify a change in student mental health and vice versa. Thus, participant responses would not necessarily be uniform across items measuring students’ mental health, academics, and social skills, and overall instrument consistency would not be affected in turn.

Participants
     We recruited a state-level sample of professional school counselors employed in K–12 public schools in Tennessee. Following the pilot study, in December 2021, we recruited participants through an anonymous Qualtrics link utilizing multiple platforms: the state school counselor association’s listserv, social media, respondent referrals, and dissemination via school counseling supervisors. Participants were eligible to complete the survey if they were currently employed in a K–12 public school in Tennessee. Upon examination of our survey data, we found 276 total responses with 220 complete for a completion rate of 79.7%. Because the survey was distributed through the above-mentioned methods, we were unable to calculate the response rate without knowing how many of the approximately 2,000 public school counselors in Tennessee received the survey. Upon further examination of the survey respondents, we removed one school counseling supervisor; four school counselors whose students were remote/hybrid; and eight school counselors in private, charter, or alternative schools to maintain focus on the experiences of traditional public school counselors working with students in person during the ongoing COVID-19 pandemic for a final sample of 207 participants. An examination of the respondents’ demographics revealed a sample that was predominantly female and White/Caucasian and worked in Title I, suburban, or rural elementary schools. The sample’s mean years serving as a school counselor was 11.7 (SD = 7.5), with mean years at current school of 6.8 (SD = 6.4). See Table 1 for more demographic information. For analysis purposes, we divided the school counselors into three groups by the size of their reported caseload. These categories were informed by a national study of school counselor ratios (National Association of College Admission Counselors, 2019) and consisted of ratios in the range of small (1:100–1:300; 14.0%, n = 29), medium (1:301–1:550; 69.6%, n = 144), and large (1:551 and higher; 15.0%, n = 31).

Table 1
Demographic Characteristics of the Sample

Characteristic n %
Age
     18–24 years   3  1.4
     25–44 years 99 47.8
     45–64 years          102 49.3
     65 years plus   3   1.4
Race/Ethnicity
     Black/African American 17  8.2
     Latinx/Hispanic   1  0.5
     White/Caucasian          183 88.4
     American Indian/Alaskan Native   1   0.5
     Other   5   2.4
Gender
     Female 192 92.8
     Male   15   7.2

Note. N = 207.

Data Analysis
     We ran a post hoc power analysis using the G*Power 3.1.9.7 statistical software to determine if our sample size was sufficient at the .80 power level with α = .05 and found that a minimum sample size of 100 was required for our analyses. Given our sample size of 207 participants, the power analysis indicated that our sample size was sufficient (Faul et al., 2007). We utilized SPSS version 26 to calculate the following analyses for this study: (a) descriptive statistics; (b) Fisher’s exact test for two dichotomous nominal variables; (c) an extension of Fisher’s exact test, the Freeman-Halton exact test, for one dichotomous nominal variable and one nominal variable with three levels; and (d) point-biserial correlation analysis for one nominal variable and one interval variable (Frey, 2018). We also examined effect size to determine practical importance using the following levels for examining nominal data (Rea & Parker, 1992), precedence for which has been established by complementary studies in educational research (K. Erickson & Quick, 2017; Kotrlik et al., 2011): negligible [0, .1), weak [.1, .2), moderate [.2, .4), relatively strong [.4, .6), strong [.6, .8), and very strong [.8, 1.0). Phi (ϕ) indicates the effect size for the exact tests, and the correlation is the effect size for the point-biserial correlation. We only included statistical analyses that resulted in moderate associations or higher. Three school counselors (1.4%) who reported caseloads that were unusually small (< 100) and outside our specified caseload parameters were removed from the analysis. Additionally, we excluded school counselors who indicated “unsure” in the categories of location (rural, suburban, urban), Title I status, and adherence to state policy for direct service to students. See Table 2 for school characteristics.

Results

Research Question 1
     RQ1 examined school counselors’ perspectives of the impact of COVID-19 on students’ mental health, academics, and social skills as well as variation by grade level (elementary, middle, or high school). When asked about the mental health changes they have witnessed in their students post–COVID-19 pandemic, 93.7% (n = 194) of school counselors reported negative changes with 42.5% (n = 88) reporting “much worse” and 51.2% (n = 106) reporting “somewhat worse” changes. Specifically, school counselors reported issues regarding anxiety (92.8%, n = 192), depression (77.3%, n = 160), family dysfunction (71.0%, n = 147), COVID-19–related grief and loss (63.8%, n = 132), technology addiction (52.7%, n = 109), suicidality (50.7%, n = 105), fear of COVID-19 (49.8%, n = 103), substance use issues (21.7%, n = 45), and other issues (12.6%, n = 26) such as separation anxiety, self-harm, and anger. The Freeman-Halton exact test revealed a significant relationship between grade level (n = 183) and depression (p < .001, ϕ = .301) with a moderate positive association, suicidality (p < .001, ϕ = .499) with a relatively strong positive association, and substance use (p < .001, ϕ = .583) with a relatively strong positive association. For depression, 90.0% (n = 54) of high school counselors and 85.7% (n = 36) of middle school counselors reported this issue as compared to 63.0% (n = 51) of elementary school counselors. For suicidality, 76.2% (n = 32) of middle school counselors and 71.7% (n = 43) of high school counselors reported this concern as compared to 23.5% (n = 19) of elementary school counselors. For substance use, 58.3% (n = 35) of high school counselors and 20.0% (n = 8) of middle school counselors reported this concern as compared to 1.2% (n = 1) of elementary school counselors. All other mental health concerns were not significant with grade level.

When queried regarding academic changes post–COVID-19, 90.3% (n = 187) of school counselors reported negative changes to students’ academics with 35.3% (n = 73) reporting “much worse” and 55.1% (n = 114) reporting “somewhat worse” changes. School counselors reported an overall decline across all subjects (80.7%, n = 167). Additionally, school counselors reported non-cognitive factors regarding lack of motivation (84.1%, n = 174), lack of parental support during the school day (75.4%, n = 156), attention issues (71.0%, n = 147), poor mental health (64.7%, n = 134), sleep deprivation (41.1%, n = 85), limited technology during virtual learning (33.3%, n = 69), lack of space to work at home during virtual learning (30.4%, n = 63), poor physical health (17.9%, n = 37), and other (3.9%, n = 8). The Freeman-Halton exact test revealed a significant relationship between grade level (n = 183) and lack of motivation (p = .001, ϕ = .265), poor mental health (p = .001, ϕ = .269), and attention issues (p = .009, ϕ = .232), all with positive moderate associations. For lack of motivation, 96.7% (n = 58) of high school counselors and 88.1% (n = 37) of middle school counselors reported this issue as compared to 75.3% (n = 61) of elementary school counselors. For poor mental health, 78.3% (n = 47) of high school counselors and 69.0% (n = 29) of middle school counselors reported this outcome as compared with 49.4% (n = 40) of elementary school counselors. For attention issues, 79.0% (n = 64) of elementary school counselors and 73.8% (n = 31) of middle school counselors reported concerns as compared to 55.0% (n =33) of high school counselors.

Table 2
School/Program Characteristics

Characteristic n %
Location
     Urban 31 15.0
     Suburban 95 45.9
     Rural 72 34.8
     Unsure  9   4.3
Title I Status
     Yes        121 58.5
     No          57 27.5
     Unsure          29 14.0
Grade Level
     Elementary 81 39.1
     Middle 42 20.3
     High 60 29.0
     Other 24 11.6
Follows 80% Direct Service Guideline
     Yes         112 54.1
     No 65 31.4
     Unsure           30 14.5
School Counselor-to-Student Ratio (caseload)
     1:1–1:300 29 14.0
     1:301–1:550          144 69.6
     1:551 and higher 31 15.0
     Other   3   1.4

Note. N = 207

When asked about behavioral changes, 87.4% (n = 181) of school counselors reported negative changes to behaviors with 30.4% (n = 63) reporting “much worse” and 57.0% (n = 118) reporting “moderately worse” changes. Comparably, when asked about social skills changes, 87.0% (n = 180) of school counselors reported negative changes to students’ social skills with 36.2% (n = 75) reporting “much worse” and 50.7% (n = 105) reporting “moderately worse” changes. Specifically, school counselors reported trouble socializing with peers (84.1%, n = 174), absence of social flexibility (58.0 %, n = 120), increase of physical aggression (55.1%, n = 114), increase in relational aggression (50.7%, n = 105), increase in cyberbullying (23.7%, n = 49), increase in bullying (19.3%, n = 40), and other (8.2%, n = 17) such as issues with conflict resolution and preference for technology. The Freeman-Halton exact test revealed a significant relationship between grade level (n = 183) and cyberbullying (p = .003, ϕ = .255), with a moderate positive association with 42.9% (n = 18) of middle school counselors, 23.3% (n = 14) of high school counselors, and 14.8% (n = 12) of elementary school counselors reporting an increase in this area. All other social skills changes were not significant with grade level.

Research Question 2
     RQ2 examined the interventions that school counselors used in assisting students with their COVID-19–related concerns and if this differed by grade level. School counselors reported the various supports that they provided to their students who struggled with COVID-19–related issues, including individual counseling (95.7%, n = 198), consultation with parents/teachers (85.5%, n = 177), referrals (80.7%, n = 167), collaboration with other school-based helpers (77.3%, n = 160), coping skills instruction (71.5%, n = 148), group counseling (44.0%, n = 91), universal health screenings (17.9%, n = 37), and other interventions (4.3%, n = 9) such as food programs, holiday donation programs, peer support, and academic support meetings. We used the Freeman-Halton exact test to examine the relationship between grade level (n = 183) and these supports and found that small group counseling (p < .001, ϕ = .405) and coping skills instruction (p = .028, ϕ = .200) were significant, both with moderate positive association. For small group counseling, 63.0% (n = 51) of elementary school counselors and 45.2% (n = 19) of middle school counselors provided this support as compared to 16.7% (n = 10) of high school counselors. For coping skills instruction, 77.8% (n = 63) of elementary school counselors and 71.4% (n = 30) of middle school counselors reported this intervention as compared to 56.7% (n = 34) of high school counselors.

Research Question 3
     RQ3 examined the barriers school counselors encountered in their ability to provide services and if this differed by grade level, SES, location, number of non-counseling duties, caseload size, and following the state guideline to spend 80% of time providing student services. When asked if they had encountered barriers to assisting their students with their COVID-19–related needs, 54.6% (n = 113) of school counselors reported that they had experienced barriers, and 45.4% (n = 94) reported that they had not. For those counselors who answered “yes,” barriers included: high caseload (44.4%, n = 92), number of non-counseling duties (20.3%, n = 42), lack of administrator support (12.1%, n = 25),  being included on master schedule for guidance classes (10.1%, n = 21), lack of training to address COVID-19 needs (8.2%, n = 17), too much time coordinating the MTSS program (7.7%, n = 16), and other reasons (9.7%, n = 20). Examples of other reasons include students’ attendance, lack of resources (both space and personnel), and focus on academics over mental health. Of note, 47.3% (n = 98) of school counselors reported an increase in non-counseling duties since COVID-19, ranging from a substantial to a slight increase.

We used the Freeman-Halton exact test to examine the aforementioned barriers by grade level (n = 183) and found that being on the master schedule (p < .001, ϕ = .297) was significant with moderate positive association with 19.8% (n = 16) of elementary school counselors reporting this task as compared to 2.4 % (n = 1) of middle school counselors and 1.7% (n = 1) of high school counselors. We used point-biserial correlation analysis to examine how the number of new post–COVID-19 non-counseling duties related to the perceived barriers to providing services to students and found this to be significant (rpb = .211, p = .002) with a positive moderate association. School counselors who reported barriers to providing services had been allocated more non-counseling duties since the pandemic (n = 113, M = 1.22, SD = 1.49) than those who did not report barriers (n = 94, M = .66, SD = 1.04). We used a Freeman-Halton exact test to examine the specific barriers by caseload (n = 204) and found school counselors with a high caseload reported significantly more difficulty in addressing students’ COVID-19–related needs (p < .001, ϕ = .284), with a moderate positive association for large (58.1%, n =18) and medium (47.2%, n = 68) caseloads, as compared to those with a small (10.4%, n = 3) caseload. Investigating the state DOE guideline for 80% of time in service to students (n = 177), excluding those who were unsure, revealed that 63.3% (n = 112) followed the guideline and 36.7% did not (n = 65). We used a Fisher’s exact test to examine the relationship between following the 80% guideline and specific barriers and found that reporting too many non-counseling duties (p < .001, ϕ = -.358) was significant, with a moderate negative association for those who did not follow the guideline (41.5%, n = 27) in comparison to those who did follow the 80% guideline (10.7%, n = 12). All other barriers were not significant with grade level, SES, location, number of non-counseling duties, caseload size, and following the 80% state guideline. We used a Fisher’s exact test to examine SES by Title I (n = 178) classification and found that it was not significant with any of the barriers.

Discussion

Our results render a disturbing picture of students’ post–COVID-19 mental health functioning and school counselors’ perceived ability to effectively meet their students’ needs since a return to in-person learning, as reported by this sample of 207 school counselors in Tennessee. For RQ1, over 93% of our respondents indicated that their students’ mental health had worsened, with anxiety and depression identified as the most pronounced psychological concern, followed by family dysfunction, grief, technology addiction, and suicidality. These results confirm our predictions that the COVID-19 pandemic would exert a harmful impact on the mental health of children and adolescents (Bryant et al., 2020; Cénat & Dalexis, 2020). Depression and suicidality were significant concerns for middle and high school counselors, and substance abuse was significant at the high school level. The reported spike in diagnosable mental health problems by secondary school counselors aligns with research indicating that half of all mental health and substance use disorders begin at 14 (Quinn et al., 2016). The CDC recently reported that depression, substance abuse, and suicide have increased among adult populations since COVID-19, with young adults presenting the most significant risk (Czeisler et al., 2020). Our results provide preliminary evidence indicating that COVID-19–related trends have similarly impacted adolescents. Further, given the relationship between ACEs and substance misuse (CDC, 2022; Quinn et al., 2016), it may be reasonable to conjecture that an increase in family dysfunction, grief, fear of COVID-19, and severance of social relationships underscored a rise in substance use problems, particularly among high school students.

In addition to mental health, student academics notably declined according to school counselors in Tennessee, with 90.3% of participants reporting negative changes to students’ academics. Previous research attributed students’ COVID-19 pandemic–related academic issues to the vagaries of online instruction, a lack of parental supervision, inadequate technology, and limited workspace, among other factors (Ellis et al., 2020; Karaman et al., 2021; Magson et al., 2021). Our results aligned with these findings by explicitly connecting delays in students’ academic progress to psychological factors. Of note, we found a significant relationship between grade level, lack of motivation, poor mental health, and attention issues, with middle and high school counselors reporting greater concerns in the areas of motivation and mental health, and elementary and middle school counselors identifying attention problems as the greatest concern. The developmental onset of mental health disorders (Lambie et al., 2019) likely accounts for increased student mental health problems reported by middle and high school counselors. However, motivation and attentional issues across the grades were problematic, and because both are symptomatic of depression and anxiety, they raise a red flag for the mental health of all K–12 students in Tennessee.

Alongside academics, 87.0% of school counselors reported negative changes in students’ social skills and 87.4% reported worsened behaviors among students, with trouble socializing with peers, absence of social flexibility, and an increase in physical and relational aggression being the most pronounced problems. Declines in students’ ability to get along with peers may be uniquely linked to social isolation during lockdown (Ellis et al., 2020; Karaman et al., 2021); however, of great concern is the increase in all forms of bullying, with cyberbullying being particularly problematic in middle school. Youth aggression is a long-term consequence of ACEs and has implications for overall school safety, with victimization and perpetration both positively associated with school violence (Forster et al., 2020).

RQ2 investigated what interventions school counselors used to assist students with their COVID-19–related concerns and examined interventions by grade level. The preponderance of school counselors relied on individual counseling (95.7%), consultation (85.5%), referrals (80.7%), collaboration with other school-based helpers (77.3%), and coping skills instruction (71.5%), all of which are consistent with crisis-level supports. Nonetheless, only 44% of the sample, primarily elementary school counselors, had used small group counseling, despite its proven efficacy with children exposed to trauma (Salloum & Overstreet, 2008). The underutilization of group work at the high school level presents a concern, given that group work provides context for peer support and social learning, both considered critical therapeutic factors for adolescents (Gysbers & Henderson, 2012). Nonetheless, this finding resonates with previous results that high school counselors are more apt to assume administrative roles in place of the provision of direct student services (Chandler et al., 2018). Universal assessment has been proffered as an efficient and empirically grounded method for the early identification of at-risk students in need of COVID-19–related interventions (A. Erickson & Abel, 2013; Karaman et al., 2021; Pincus et al., 2020). Unfortunately, only 17.9% of the sample reported administering universal mental health screeners, a finding aligned with other studies that indicate schools have resisted adopting mental health screeners because of inadequate resources and related concerns about following up with students identified as being at risk (Burns & Rapee, 2022).

For RQ3, we explored the school counselors’ perspectives of the barriers they have encountered in assisting their students with their COVID-19 concerns. The proliferation of barriers reported by school counselors (high caseload, non-counseling duties, lack of administrator support, being on the master schedule for guidance classes, and a lack of training) verifies our concern that school counselors in Tennessee did not receive the support instrumental to their ability to provide effective student services at this critical time. Our state-level findings resonate with studies conducted in other states that indicate school counselors’ non-counseling duties increased during the pandemic while administrator support declined (Savitz-Romer et al., 2021). Other studies have also drawn attention to widespread staffing shortages associated with COVID-related absences and a reduced pool of substitute teachers (Patterson, 2022). Although we did not examine staff resources explicitly, with almost 50% of our Tennessee sample witnessing an increase in their non-counseling duties, it would be reasonable to infer that campus administrators are deploying school counselors to triage critical gaps in staffing patterns. Interestingly, despite a widespread increase in non-counseling duties post–COVID-19, only 20.3% of counselors reported non-counseling duties as a barrier to providing care. The discrepancy between these two results may be indicative of the phenomenon of role diffusion in school counseling, a problem that emerges when school counselors begin to integrate non-counseling duties as part of their accepted role and thus do not perceive them as antithetical to their professional identity (Astramovich et al., 2013). Furthermore, neither SES (Title I) nor location (rural, suburban, urban) were significant with barriers, and although this could reflect our relatively small sample, it could also be indicative of staff shortages adversely affecting the role of school counselors across all settings, regardless of the school’s demographic status.

The most notable barrier reported by respondents was a large caseload. School counselors with large and medium-sized caseloads reported more barriers and were less likely to follow the 80% guideline. Thus, those students who were negatively impacted by large counselor caseloads before COVID-19 faced further obstacles in accessing their school counseling services despite an overall increase in their mental health and academic needs. Further, elementary school counselors listed on the master schedule for guidance classes faced additional barriers to addressing their students’ needs outside of their prevention-focused (Tier 1) activities. Classroom guidance is considered helpful in elementary school for building social skills and study habits; however, when counselors are placed on the master schedule, it can impact their ability to provide responsive student services (Gysbers & Henderson, 2012) which seemed to be the case with our respondents.

Implications for Professional Advocacy
     The results of this study illustrate a decline in student functioning, pronounced in the area of mental health, and have implications for school counselor advocacy in the areas of policy and practice. Advocating for policy change takes time and is beyond the individual efforts of school counselors, who are often beholden to their principal’s limited understanding of school counselors’ appropriate role and function (Lancaster & Reiner, 2022) and subsumed by untenable caseloads in under-resourced schools (Lambie et al., 2019). We, therefore, assert that advocacy is the professional imperative for all vested school counseling professionals (state counseling associations, school counselor educators, school counseling supervisors, and school counselors), all of whom could be working in tandem to advance the profession.

At the policy level, state and national counseling associations should reconsider the important role school counselors play in supporting students’ mental well-being and re-examine policies that delineate the appropriate use of school counselors’ time. Currently, the state school counseling model (Tennessee Policy 5.103) mirrors the national model (ASCA, 2019), perennially focusing on school counselors’ role in supporting student academics and delimiting their counseling role to prevention services, crisis counseling, and referrals to other mental health professionals. For state and national counseling associations, positioning school counselors as primarily focused on student academics demonstrated their value during the No Child Left Behind Act (NCLB; 2001) era, which prioritized unidimensional outcome measures of student success, particularly in math and reading (Savitz-Romer, 2019). However, the Every Student Succeeds Act (ESSA) replaced NCLB in 2015 and emphasizes more holistic aspects of student development and school climate. Many scholars argue that the ESSA (2015) combined with the rise in mental health issues has created a policy window for school counselors, led by their state and national professional associations (Savitz-Romer, 2019), to focus on the non-cognitive aspects that undergird healthy student development and to reclaim mental health as a domain central to school counselor practice (Lambie et al., 2019).

Redefining school counselors’ role in terms of mental health would require them to receive more clinical supervision (Lambie et al., 2019). In comparison to counselors in clinical settings, school counselors receive little to no supervision for their clinical efforts, which affects their clinical identity and weakens their counseling skills over time (Lancaster & Reiner, 2022). To address this gap, symbiotic partnerships could be formed with counselor education programs, particularly those that offer doctoral degrees in counselor education and supervision, to provide clinical supervision to local school counselors. Progress in this area may be forthcoming in the state, as institutions of higher education that operate school counseling, school psychology, and school social work programs have been invited to apply for grants funded through COVID-19 relief funding to support student internships in high-need schools. In addition, funds are available to support clinical supervision experiences that extend beyond students’ graduate training programs (Tennessee DOE, 2023).

MTSS programs also offer a promising prevention and intervention framework for meeting students’ comprehensive needs, including mental health, and align to both state and national school counseling models (Goodman-Scott et al., 2019). Further, the Tennessee DOE (2018) has developed a resource guide based on a tiered model for supporting students’ differential mental health needs, which school counselors could efficiently implement within their existing MTSS programs. Of note, within the Tennessee model, Tier 1 mental health practices build a foundation for mental wellness for all students. Advanced supports at Tiers 2 and 3 provide students who are at risk because of behavioral and/or mental health concerns with access to small groups and mental health interventions. One dimension of the state’s tiered mental health model is universal screening to identify students with internalizing behavioral disorders. Although few counselors in this study utilized universal screening, we recommend school counselors and their supervisors leverage the preexisting Tennessee DOE guidelines to petition their districts to adopt universal mental health screening.

Although the state mandated reduced counselor ratios in 2017 (Policy 5.103.), the funding formula allowed for uneven adoption of this policy (Tennessee Comptroller of the Treasury, n.d.), and target ratios fell short of national recommendations (ASCA, 2019). Thus, a function of this research was to utilize results in policy contexts to advocate for ratio realignments. In partnership with the state school counselor association, we produced a one-page results summary, written in simple language, to disseminate to state politicians to illuminate the acuity of mental health issues faced by K–12 students and proposed a solution through increased school counselor access. An advocacy effort led by the state association resulted in proposed legislation TN HB0364/SB0348, which would require one licensed full-time professional school counselor position for every 250 students and is currently advancing through the state Senate and House committees. A significant takeaway from this study is the importance and potency of coordinated partnerships between researchers, state counseling associations, and school counselors—an alliance that could be replicated in other states by school counselor stakeholders to advocate for the profession.

Limitations
     The generalizability of these findings is limited because of the use of a state-level sample and a non-standardized, self-report survey. First, self-report surveys are sensitive to respondents’ tendency to rate themselves more favorably. Thus, it would be reasonable to conjecture that school counselors overestimated their adherence to the state guideline to spend 80% of their time in service to students and underreported their non-counseling duties. Second, although the items were informed by previous research on the psychological issues faced by children and adolescents during COVID-19 (Ellis et al., 2020; Karaman et al., 2021; Magson et al., 2021) and those factors that affect school counselors’ ability to provide direct services (Kaffenberger & O’Rorke-Trigiani, 2013; Parzych et al., 2019; Whitney & Peterson, 2019), the use of an ad hoc survey precluded us from performing more robust analyses (e.g., regression analysis). Third, because we only gathered data on students’ mental health issues and academic functioning post–COVID-19 pandemic, we have no benchmark data of students’ pre–COVID-19 functioning with which to make objective comparisons.

Fourth, although the sample was large enough to find some significant results, it was a small percentage of the state’s total population of public school counselors, which is estimated to be over 2,000. A larger sample would have increased the generalizability of findings and impacted the significance levels and practical importance of the results. Fifth, our sample lacks racial and gender diversity; however, it does align with the state’s overall population of educators (Tennessee DOE, 2021). Finally, regarding data analysis, interpreting correlations on a small population sample needs to be performed cautiously because of the possibility of sampling error. Additionally, point-biserial correlation can be impacted by the dichotomous nature of one of the variables, which constrains the variability of the results (Hinkle et al., 2002). Nonetheless, correlational analyses of ordinal and nominal variables in small-scale research are consistent with our exploratory design, and the results provide evidence that the variables examined share some type of relationship and provide direction for future research.

Future Research
     Given that we conducted this study in the aftermath of the COVID-19 pandemic and have utilized data and policy to advocate for expanded student access to school counseling services in Tennessee, this study design could be replicated by future researchers in the event that another pandemic or crisis of similar scale affects K–12 populations. Nonetheless, our exploratory design is an inherent limitation with the preponderance of our findings based on correlational analysis of largely non-parametric data. Future studies could explore dimensions of students’ mental health utilizing student data from empirical inventories. Rather than relying on school counselor perception data, researchers could use results from universal screenings, such as the Behavior Assessment System for Children-3rd edition (BASC-3), to better understand the nature of student issues and examine differential risk by demographic factors (e.g., age, gender, ethnicity), which could be used to inform evidence-based interventions with at-risk and high-risk populations. Further, researchers could employ quasi-experimental designs to assess outcomes of school counselor-led interventions, such as small groups, with students who have scored as being at risk based on universal screening. Studies of this nature can help build a case for the efficacy of school counselors and, in turn, protect them from role misallocation. Qualitative research could also be conducted in those schools in which school counselors implement a universal screening, intervention, and referral system to glean an implementation blueprint practical to other school counselors within and outside the state.

Conclusion

With elevated rates of depression, anxiety, substance use, and bullying, it is reasonable to conjecture that students in Tennessee have experienced COVID-19–related trauma, which according to research is unlikely to abate without intervention (CDC, 2022; Savitz-Romer et al., 2021). Although our state-level respondents indicated that they provided services consistent with crisis counseling (e.g., individual counseling, group counseling, consultation, and referrals), almost 50% of the counselors had been burdened with additional non-counseling duties, which could reduce their capacity to work with students at different levels of risk. Large caseload was a significant barrier, leaving counselors struggling to provide an appropriate level of care. This finding raises considerable concern about the risk faced by students who have experienced deterioration in their mental health and academics since the onset of COVID-19, yet attend schools in Tennessee with elevated school counselor-to-student caseloads. Nationally and at the state level, school counselors are the most prevalent mental health professionals in schools and are trained in crisis response (National Center for Education Statistics, 2016). Unfortunately, Tennessee school counselors appear to be facing barriers in the provision of student services related to high caseload and non-counseling duties, which presents cause for professional advocacy within the state and beyond.

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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Chloe Lancaster, PhD, is an associate professor at the University of South Florida. Michelle W. Brasfield, EdD, LPSC, is an assistant professor at the University of Memphis. Correspondence may be addressed to Chloe Lancaster, 422 E. Fowler Ave, EDU 105, Tampa, FL 33620, clancaster2@usf.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.