school counselors - The Professional Counselor

The Technology Acceptance Model (TAM): Exploring School Counselors’ Acceptance and Use of Naviance

Oct 21, 2018 | Volume 8 - Issue 4

Vernell Deslonde, Michael Becerra

This study utilized a qualitative dominant crossover mixed analysis that examined why school counselors (N = 38) choose or do not choose to use Naviance—an online college, career, and financial planning tool. The study further explored whether school counselors’ acceptance and use of Naviance enhances counseling practices, job productivity, and efficiency. The Technology Acceptance Model (TAM) was used for the theoretical framework. TAM is comprised of four constructs: perceived ease of use, perceived usefulness, attitudes, and actual behaviors. Bandwidth, training, and connectivity influenced some counselors’ attitudes toward usage and productivity; however, overall attitudes toward Naviance were positive. Future research should explore the connection between counselor usage and the number of hours trained on Naviance.

Keywords: school counselors, Technology Acceptance Model, TAM, Naviance, qualitative dominant crossover mixed analysis

New technologies are pervasive in the counseling profession. School counselors are experiencing a growing field of technologies that include virtual counseling platforms, smartphone applications, and learning management systems that provide the ability to see students face-to-face, quickly access information through an application, and offer high school students resources and information, ultimately assisting in the school-to-work transition. Additionally, the value of integrating new technologies into practice to support counselor growth as well as student outcomes is recognized in the education field. Many researchers believe that online technologies are effective educational tools (Serdyukov, 2017; Sung, Chang, & Liu, 2016; Tarhini, Hone, & Liu, 2015; Teo, 2011).

According to the Condition of Education 2017 report, in 2013–2014, K–12 schools spent $634 billion integrating technology to support academic achievement (National Center for Education Statistics, 2017). The bulk of the cost has been on purchasing equipment, integrating hardware and software, and training staff personnel. Despite the promise and financials spent, the lack of user acceptance is a barrier to the success of integrating new technologies (Blanchard, Prior, Barton, & Dawson, 2016; Davis, 1993; Tarhini et al., 2015; Teo, 2011). Without user acceptance, the value of the technology diminishes. Alternatively, increased technology acceptance can enable educators, including school counselors, to become effective with transferring knowledge, preparing and advancing student outcomes (Hu, Clark, & Ma, 2003), and enhancing counseling practices (Hayden, Poynton, & Sabella, 2008; Steele, Jacokes, & Stone, 2014).

Numerous theoretical models have been developed to investigate users’ acceptance of new technologies. The most widely researched model on user acceptance that investigates why a user chooses to use or not to use technology is the Technology Acceptance Model (TAM; Davis, 1993; Nair & Das, 2011; Tarhini et al., 2015; Teo, 2011). TAM predicts the level of technology acceptance and usage. Few studies exist on TAM within the context of K–12 schools and even fewer on the school counseling profession (Tri Anni, Sunawan, & Haryono, 2018). Utilizing TAM as a guiding framework, this research extends and advances knowledge on factors that influence school counselors’ acceptance and use of technologies, specifically Naviance, an online college, career, and financial planning counseling platform.

School Counselors’ Technology Acceptance

Perceived Ease of Use
Research has indicated that individuals are more likely to accept and use new technology if they perceive the technology as easy to use (Davis, 1993; Nair & Das, 2011; Saade & Bahli, 2005). Perceived ease of use is determined when a user believes that using a system is free of effort (Nair & Das, 2011;
Tarhini et al., 2015). Previous studies reveal common themes of perceived ease of use of certain technologies in the school counseling profession. For example, many school counselors perceive that sending email communication, creating multimedia presentations and webpages, developing newsletters, and retrieving information from schools’ student information systems are relatively easy functions (Carey & Dimmitt, 2004; Carlson, Portman, & Bartlett, 2006; Kozlowski, Mikesina, & Genova, 2015; Loague, Alexander, & Reynolds, 2010; Steele et al., 2014; Van Horn & Myrick, 2001). Today, many school counselors find it easy to retrieve counseling-related information from the internet and create targeted presentations for students. Further, school counselors perceive that delivering counseling curriculum, disseminating information, and administering needs and career assessments require minimal effort (Hayden et al., 2008; Holcomb-McCoy, Gonzalez, & Johnston, 2009; Loague et al., 2010; Millsom & Bryant, 2006; Steele et al., 2014).

School counselors have found certain types of technology easier to use. For example, in a quantitative study, Carlson et al. (2006) investigated how school counselors use technology and their comfort level. The results indicated that counselors felt comfortable or somewhat comfortable (92.7%) utilizing certain types of technology and software, such as desktop computers, VCRs and monitors, overhead projectors to create visual presentations, and Microsoft Word and Microsoft PowerPoint, as additional resources. However, most school counselors (76.9%) experienced low comfort levels and felt anxious or somewhat anxious using new software.

Perceived Technology Usefulness

Technology acceptance also is influenced by perceived usefulness. Perceived usefulness is determined by a user’s belief that a type of technology enhances job performance (Tarhini et al., 2015). Although a
reasonable amount of literature exists on how school counselors use technology in the counseling profession (Carlson et al., 2006; Hayden et al., 2008; Steele et al., 2014), little exists on perceived usefulness (Tri Anni et al., 2018). Tri Anni et al. (2018) surveyed school counselors in Indonesia and found that counselors who perceived that technology was easy to use were more likely to determine that the technology was useful. However, Tri Anni et al.’s study did not focus on a specific type of technology such as Naviance to determine whether such a tool enhances job effectiveness.

In another study, Steele et al. (2014) surveyed school counselors and found that many (45%) remained neutral when asked whether the advantages of online communication in their counseling practice outweighed the disadvantages. Furthermore, 61% felt slightly or not comfortable at all using online technology to perform counseling duties. When asked specifically about using Skype and other synchronous online communication technologies, researchers found a positive correlation among counselors’ level of training and comfort.

Attitudes Toward Technology Use

Guzman and Nussbaum (2009) argued that merely acquiring the hardware or software is insufficient to integrate technologies and therefore stressed the importance of the user’s attitude. The more positive the attitude about technology, the higher the actual usage (Teo, 2011). Several researchers have found school counselors’ attitudes toward the use of technology to be mostly positive, but lower when new technologies are introduced (Carlson et al., 2006; Rainey, Mcglothlin, & Miller, 2008; Steele et al., 2014).

It is important to note that there are external forces that shape a person’s perceived ease of use and usefulness of technology, and these forces may negatively affect attitudes. Such barriers include limited training on new software, age of the user, bandwidth challenges, slow data access, time delays in downloading content, and limited equipment (Carlson et al., 2006; Guzman & Nussbaum, 2009; Hu et al., 2003; Lederer, Maupin, Sena, & Zhuang, 2000; Steele et al., 2014). Moreover, large counselor caseloads might be a barrier to perceived ease of use and usefulness. For example, counselors working in states with higher caseloads may perceive that learning new technological software while managing higher caseloads and trying to capture large amounts of student information can be difficult.

Naviance: An Online College Career and Financial Planning Tool
Although many school counselors and students have used Naviance for more than a decade, a Google Scholar search revealed only one study in which the authors explored the relationship between the number of times that students visit Naviance and increased college application rates (Christian, Lawrence, & Dampman, 2017). Naviance is an online college and career readiness tool developed by Hobsons (Hobsons, 2017). According to Hobsons’ website, “more than 10 million students rely on Naviance to achieve key readiness milestones and answer critical questions such as: Who am I? What do I want to be? How will I get there? and Will I be successful?” (Hobsons, 2017). From a college and career counseling perspective, Naviance is used by middle and high school counselors and personnel to support and track student progress. Some of the features in Naviance include course planning; postsecondary planning; career inventories; career and college searches; college majors; college applications; test preparation (SAT, ACT, and Advanced Placement); college enrollment; and 28 curriculum lessons in college, career, and financial planning.

TAM

Technology acceptance and adoption is well documented in the literature. Although several factors influence the acceptance and use of technologies, TAM, grounded in Fishbein and Ajzen’s (1975) research on beliefs, attitudes, and behaviors, indicates that perceived usefulness and perceived ease of use predict attitudes and actual behaviors (Davis, 1993; Nair & Das, 2011). Essentially, TAM captures the user’s overall attitude toward online technologies.

Davis (1993) hypothesized that one’s attitude toward using technology is a function of two beliefs: perceived ease of use and perceived usefulness. Perceived ease of use is the degree to which a person believes that using the system would require minimal effort, whereas perceived usefulness is the extent to which the information system enhances job performance (Lederer et al., 2000). Two other constructs of TAM are a person’s attitude toward the use of the system (which is the user’s desire to employ the system) and behavioral intention (which is the likelihood that a person will use the system; Davis, 1993; Lederer et al., 2000). Scholars have argued that perceived ease of use of the technology and perceived usefulness determines one’s attitude toward a new technology (Davis, 1993; Padmavathi, 2016; Teo, 2011), such as Naviance.

Purpose of the Study

The purpose of this study was two-fold. First we sought to investigate if school counselors utilized Naviance. Second, we examined how Naviance usage enhances middle and high school counselors’ practices, productivity, and efficiency. Although many school counselors integrate technology into their practice (Kozlowski et al., 2015; Reljic, Harper, & Crethar, 2013; Steele et al., 2014), few studies address whether school counselors accept new technologies, as well as examine attitudes and actual usage. TAM provides the theoretical framework to understand school counselors’ acceptance and use of Naviance. To shed light onto the phenomenon, the following research questions guided this study: (a) Do school counselors choose to use or not choose to use Naviance; and (b) how does Naviance acceptance and usage enhance school counseling practices in terms of productivity and efficiency?

Methods

Data sources collected for this qualitative dominant crossover mixed analysis study included a survey questionnaire, face-to-face semi-structured interviews, and Naviance staff usage and engagement reports. According to Onwuegbuzie and Teddlie (2003), the benefits of a crossover mixed analysis include the ability to compare, correlate, and integrate quantitative and qualitative findings to describe the phenomenon. This type of qualitative dominant crossover mixed analysis takes into consideration a qualitative stance with quantitative data that provides additional detail to the study (Frels & Onwuegbuzie, 2013; Onwuegbuzie, Leech, & Collins, 2011). Ross and Onwuegbuzie (2010) grouped quantitative analyses according to difficulty, starting at the basic, descriptive level 1, and reaching as high as level 8, which includes multidirectional and multilevel analyses like multilevel structural equation modeling. In this study, the researchers used a level 1 quantitative analysis, which includes descriptive data taken from usage and engagement reports, and percentages from the questionnaire to determine productivity and efficiency.

Participants

A purposeful and convenience sample was utilized for this study. Purposeful sampling is used to identify and select individuals who are knowledgeable about a phenomenon (Palinkas et al., 2015), whereas convenience sampling is beneficial when participants are easily accessible and in close geographic proximity (Etikan, Musa, & Alkassim, 2016). The first researcher purposefully sought out middle and high school counselors who worked in close proximity and use Naviance in their role, from 14 public schools within the southwestern part of the United States. The first researcher sent an email to 48 potential participants. Of the 48 participants contacted, 38 school counselors agreed to participate, of which 10 were male and 28 were female. Twelve counselors worked at the middle school level and 26 at the high school level. All participants held a master’s degree and Pupil Personnel Service credential. Counselors ranged in age from 25 to 51. The age range for 55% of the school counselors was 25–44 years, whereas the remaining 45% age range was 45–51 years.

School District and Research Team

The school district implemented Naviance in 2014. The Naviance technology was given a low to medium priority, with the expectation that school counselors would at least minimally use the technology. The Naviance implementation occurred over a 3-year period. In the first year, two middle and two high schools implemented Naviance. In the second year, three additional high schools, two alternative high schools, and two additional middle schools launched Naviance, and during the final year, the remaining three middle schools rolled out the technology tool. Also in the third year, all Advanced Placement (AP) teachers were trained on Naviance AP test prep at each high school. Counselors and select school personnel received two full-day trainings on Naviance during each implementation year and Webex trainings were offered quarterly to those who needed a refresher on Naviance features and functionalities. In addition, professional development was offered to counselor groups upon request.

The first researcher works at the district office and provides monthly professional development to school counselors; however, the first researcher does not supervise the school counselors. Further, there are multiple layers of supervision that remove the first researcher from the day-to-day interactions of school counselors; the first researcher does not sign the performance evaluations of counselors, thereby preventing the first researcher from being able to use knowledge obtained from this study to negatively affect the participants. The second researcher works at a university in Texas as an adjunct faculty member. The first researcher identifies as African American and the second as Afro-Latino, with a mean age of 45. The first researcher is female and the second is male. Neither researcher has received financial assistance to conduct this study from Hobsons or its affiliates.

Instruments

Survey questionnaire. The TAM electronic questionnaire, first developed by Davis (1993) and validated in different contexts by several researchers (Nair & Das, 2011), consisted of 17 questions, of which 13 were on a 5-point Likert-type scale questionnaire, with the scale ranging from 1 (strongly agree) to 5 (strongly disagree). Also included in the survey questionnaire was demographic information (questions 1–3). To explore the research question, survey questions 4–15 asked about the extent to which Naviance was easy to use (4 questions); whether Naviance enhanced middle and high school counselors’ counseling practices, job productivity, and efficiency (4 questions); if Naviance was useful (2 questions); and attitudes toward using Naviance (2 questions). Question 16 was open-ended and regarded counselors’ overall attitude toward using Naviance, and the last question asked participants to indicate the frequency that they use Naviance (1 = daily, 2 = weekly, 3 = monthly, 4 = at least every other month, or 5 = not at all). Validation of the survey questions was established through a school counseling professional, who is a researcher, university faculty, and a retired school counselor of 30 years. Both researchers had combined experience of more than 30 years in counseling.

Interviews. Face-to-face, semi-structured interviews were another source of data for this study to help answer both research questions. The researchers used TAM and the survey questionnaire to construct 10 interview questions. The 10 interview questions centered on usefulness, ease of use, attitudes, and whether Naviance helped to enhance school counseling practices, job productivity, and efficiency. To ascertain ease of use, the first two interview questions focused on which of the functionalities in Naviance were the easiest to navigate and which data visualization features were easy to decipher. Questions 3 and 4 investigated how Naviance enhanced the role of school counselors and the benefits of using Naviance to engage multiple stakeholders. Interview questions 5–8 examined the ways that Naviance increases job effectiveness, efficiency, and productivity. The remaining questions explored whether Naviance was worthwhile and integration challenges.

Validation of the interview questions were by an expert panel of doctoral-level professionals in the fields of education and school counseling. Two members of the panel have been school principals and district personnel for more than 20 years combined. The third expert panelist is a university faculty member and retired school counselor. The first researcher sent the interview questions to the expert panel via email and requested feedback. One of the experts suggested that the researchers add a definition for perceived ease of use and perceived usefulness for the participants as part of interview questions two and three, which the first researcher subsequently incorporated. The second expert suggested that the researchers incorporate the language middle and high school counselor as part of the purpose of the study in the interview script rather than school counselor, which the first researcher included. The third expert did not offer additional suggestions.

Archival materials. To further help address the second research question, the researchers used the Naviance staff usage and engagement reports as a secondary data source. Specifically, the staff usage report showed the number of times that school counselors had accessed Naviance since implementation. In addition, the engagement reports showed the features in Naviance school counselors use to support the academic, college, and career development of students.

Procedure

The first researcher sent an email invitation along with a Qualtrics link for the TAM questionnaire to 48 middle and high school counselors to participate in this study. The survey remained open for 10 business days. Within that timeframe, 38 middle and high school counselors consented to participate in this study. After the survey closed, the first researcher sent an email to all 48 counselors inviting those who completed the survey to participate in face-to-face interviews. Of the 38 counselors who completed the study, 10 consented (three middle and seven high school counselors) to participate in the face-to-face interviews. The first researcher told participants that the interviews would be digitally recorded, they could withdraw any time, and their demographic information and personal identities would remain confidential. The first researcher conducted 10 separate interviews, which lasted on average 33 minutes.

After transcription of the interviews by rev.com, an online transcription company, each participant received a copy of the transcript to review and offer feedback within five business days. At the close of the five business days and with no changes suggested from participants, the first researcher deleted information that could identify participants and emailed the interview and Naviance staff and engagement data, which was retrieved at the district level, to the second researcher. The use of video conference calls as a virtual workspace was useful in collectively reading over transcripts, developing and comparing coding, and discussing themes.

Trustworthiness Procedures

To ensure trustworthiness and credibility of the study, the researchers used the process of triangulation and member checking to strengthen construct validity during the data collection process. The selection of triangulation allowed the researchers to collect data using a combination of sources to incorporate multiple perspectives on technology use and integration. Although archival materials (e.g., school counselor usage and engagement reports) did not require insight from the participants to increase the researchers’ understanding because of their pre-existing nature (Yin, 2014), the materials were instrumental in authenticating information from the interviews and were determined to be a valued data source. Another method used to strengthen trustworthiness was member checking. The first researcher separately emailed each participant, asking them to review the interview transcriptions to check for accuracy and offer feedback. Each participant replied within the 5-day timeframe indicating no corrections or feedback were necessary.

Data Analysis

The process of thematic analysis guided this study, which involved identifying patterns, insights, or concepts in the data that help to explain why those patterns are there (Bernard & Ryan, 2010). Both researchers used the process of open and axial coding, which involved breaking apart each data source, and deductive coding, which uses a top-down approach making connections and categorizing themes under TAM (i.e., perceived ease of use, perceived usefulness, attitudes, and actual usage). After reviewing themes from both researchers, there was absolute agreement about themes and codes.

The researchers followed the six phases of thematic analysis described by Clarke and Braun (2013), which included (a) familiarization of the data; (b) generation of initial codes; (c) identification of themes; (d) review themes; (e) define and name themes; and (f) produce the report. First, the researchers read through each line of the transcript several times to become familiar with content and understand perceptions regarding the usefulness, ease of use in using Naviance, and attitudes. Second, the researchers generated initial codes. Open coding allowed the researchers to break apart and group the data, and axial coding allowed the researchers to make connections to the data once it was categorized (Bernard & Ryan, 2010).

Next, the researchers categorized themes according to TAM from the transcribed interviews. TAM served as a priori themes, which related to the research questions as well. Themes capture important data about the research questions (Clarke & Braun, 2013) and explore patterns (Alhojailan, 2012). To help sort through the data to identify potential themes and the relationship between the codes, the first researcher established a codebook to assist in analyzing the data. Then, the researchers defined and named the themes based on TAM. Next, the researchers connected the narrative to the themes, named each theme according to the model, and generated themes. The last step of the data analysis process was to produce a concise, non-repetitive account of the story related to the research questions (Clarke & Braun, 2013).

Results

Perceived Ease of Use

Drawing from the survey questionnaire, 79% of the middle and high school counselors (n = 30) strongly or somewhat agreed that Naviance has a friendly interface for students and counselors, requires minimal effort, and was easy to use, while 5% (n = 2) neither agreed nor disagreed and 16% (n = 6) somewhat disagreed. Similarly, when asked whether Naviance was clear and understandable, 79% (n = 30) strongly or somewhat agreed, while 3% (n = 1) neither agreed nor disagreed, and 18% (n = 7) somewhat or strongly disagreed.

During the interviews, the counselors reported that the Naviance data platform layout made it easy to view and use all the pertinent data required for advising students on academic performance, college readiness, and social and emotional development. Specifically, some of the layout features discussed by counselors included Quick Links (i.e., application manager, transcript manager, journal dashboard, curriculum, and test prep) and counseling tabs (i.e., students, planner to help assign tasks and discuss goals, course planner, scholarships, colleges, careers, and a new feature, analytics). Other areas described by counselors that contributed to the ease of use of Naviance was data visualization of college applications submitted by students on the home page, and outcome images (i.e., overall percentage of students that applied and were accepted to at least one college and overall percentage that applied to and were accepted to a 4-year college).

Another feature reported by middle and high school counselors that they believed was easy to use was the reports and analytics functionality. At the middle school level, counselors indicated that they were able to run reports on whether students completed their career inventories or curriculum assignments. If a student failed to complete an assignment, counselors mentioned that sending an electronic reminder to their student via Naviance was seamless. One middle school counselor stated, “I run various queries in Naviance, which are extremely helpful. I like the feature where it allows me to automatically generate a weekly status report on all of my students.”

One high school counselor described Naviance’s academic, college, and career online resources: “Naviance is the best setup I’ve seen in my 20-plus years of being a counselor. It’s a one-stop shop and really simple to use.” Two other high school counselors described the ability to cross-share information with other Naviance counselors nationwide. For instance, a male high school counselor stated, “I no longer need to create student surveys! Other counselors who use Naviance in other states have created a battery of surveys across entire grade levels that I can export and electronically use with my students.”

Overall, most of the middle and high school counselors reported that Naviance was easy to use; however, some school counselors somewhat disagreed. For example, one high school counselor mentioned, “When Naviance is working correctly and the students can complete the activities, Naviance is easy to use. As a counselor, Naviance feels like busy work [record keeping, student follow-up, having groups of students logging in to a system], especially when there are issues with connectivity.” Another counselor reported, “Naviance is not user-friendly at the high school level. It’s too cumbersome and time consuming.”

Perceived Usefulness

On the survey questionnaire, when asked whether Naviance increases job-related effectiveness and productivity, in both instances most school counselors (79%, n = 30) strongly or somewhat agreed, while some were neutral (5%, n = 2) or somewhat disagreed (16%, n = 6). When asked whether Naviance enhances counseling practices, 84% of school counselors (n = 32) strongly or somewhat agreed, while 16% somewhat disagreed (n = 6). When asked whether Naviance was useful 92% of school counselors agreed (n = 35), while 8% (n = 3) somewhat or strongly disagreed.

During the interviews, eight of the 10 middle and high school counselors reported that the Naviance system is a comprehensive counseling solution that allows for the collection and quick retrieval of information that shows measurable results of their work, which increases their job effectiveness and productivity. For instance, school counselors identified the ability to retrieve overall assessment results, graduation status, academic progress, individual and small group tracking, pre- and post-outcomes, analysis on college application and acceptance rates (i.e., 2- and 4-year acceptances), field trip numbers, PSAT/SAT/ACT historical data, and more. The collection, analysis, and reporting of data from Naviance was perceived by school counselors as a useful strategy that supported their effort in becoming more data-driven, with data needed for school counselors to establish credibility in their role, evaluate their impact, and demonstrate program accountability that promotes student outcomes. The perception by many middle and high school counselors was that the Naviance system facilitated evidence-based practices. One high school counselor put it this way, “administrators understand data, and if we want to demonstrate our value to stakeholders, we must show how our work impacts student outcomes.” A middle school counselor stated, “Presenting survey data and responses from students after each presentation or field trip shows teachers, administrators, and parents the effect of our efforts.”

When asked whether Naviance enhances their counseling practice, one middle school counselor stated, “I think that Naviance makes our jobs a lot easier. . . . Naviance has helped to streamline the college, career, and academic process and make it very clear. Everything about our job as counselors is more fluid.” Another middle school counselor stated, “I think Naviance is very beneficial to my role. I can track student progress, communicate to teachers about relevant meetings, quickly deliver services, and actively engage to find digital resources to address needs.” A counselor at the high school level stated, “The more I used Naviance, the more I saw the many benefits, possibilities, and connections to the work that I do every day. Naviance has become a really important tool in my arsenal.” A high school counselor commented that Naviance helps capture whether students are on or off track to graduate and is a source to share electronic resources for students needing Tier 2 supports. Another high school counselor reported that Naviance was helpful in saving time when completing tasks and gathering student information. She stated, “Using Naviance makes me a better counselor; I’m more productive throughout my day, and I can tackle other more pressing issues students might have instead of working late to update my Excel spreadsheet.”

Although there were more counselors who found Naviance useful in their role, one middle school counselor and one high school counselor did not agree that Naviance enhanced their counseling practice. The high school counselor stated, “Naviance is yet again another system to use to support students that might go away when there is no more funding, so why learn it.” The same counselor went on to add that she has students who are “dealing with anger, drug addiction, pregnancy, suicide, and anxiety, and Naviance does not offer curriculum on those topics.” She further stated, “I can upload resources into Naviance, but it’s not useful because my role also includes helping students in the areas of social and emotional development.”

The middle school counselor described her experience using Naviance and added, “Naviance is good for kids, but I honestly do not see how it makes me a better counselor or my job more efficient or productive.” The same counselor added, “My job is about building trust, establishing relationships, advocating, and guiding students through middle school. Naviance is a tool that can help facilitate that process, but it does not enhance my counseling skills.”

Attitudes

When asked whether counselors like using Naviance and whether they have a generally favorable attitude toward it, in both instances the results were mixed. Twenty-eight (72%) of the 38 school counselors strongly or somewhat agreed that they liked using Naviance, four counselors (10%) neither agreed nor disagreed, and seven (18%) somewhat disagreed or strongly disagreed. When asked about having a favorable attitude toward Naviance, 23 (61%) strongly or somewhat agreed, 5 (13%) neither agreed nor disagreed, and 10 (26%) disagreed or strongly disagreed. Twenty-three school counselors (61%) reported on the open-ended survey question that Naviance was desirable to use for academic and related counseling purposes. Several counselors indicated that multiple training opportunities contributed to comfort level and positive attitudes. However, one high school counselor whose attitude was less than positive stated, “I would prefer to use Californiacolleges.edu, which is a free program that essentially offers the same activities for our students instead of Naviance. Plus, the system specifically caters to counselors and students in California, unlike Naviance.”

Two challenges identified by several school counselors that interfered with having a positive attitude about Naviance related to bandwidth issues and access to schools’ computer labs. Counselors expressed frustration by the slow internet connection at their schools, which they reported was due to limited bandwidth capacity. One counselor commented, “due to bandwidth limitations, Naviance does not always work.” Another challenge identified that interfered with overall satisfaction of Naviance was limited access to computer labs. One high school counselor stated, “Computer labs are scarce and accessibility to use Naviance with students is difficult.”

Actual Usage

Drawing from the Naviance usage and engagement reports, actual Naviance usage and engagement among school counselors was high. Since the implementation of Naviance, school counselor usage has increased each year (see Table 1). Counselor-supported engagement within Naviance is highest among high school counselors (see Table 2).

Table 1

Actual Usage of Naviance Since Implementation

Descriptors

Year 1

(2014–2015)

Year 2

(2015–2016)

Year 3

(2016–2017)

Middle and High School

1,295

3,277

5,574

Note.
Number of times school counselors used or accessed Naviance from 2014–2017.

Table 2

Counselor Engagement Support Provided to Students

Descriptors	Naviance Guidance Curriculum	ACT/SAT/ AP Study Plans	College Planning	Career Planning	Academic Planning
Middle School	12,887	0	599	10,735	32
High School	22,366	153,000	11,623	508	497

Note.
Number of times Naviance was used to engage students in 2016–2017.

On the survey, middle and high school counselors were asked the frequency of Naviance usage. Most school counselors used Naviance daily, followed by weekly usage. Sixty-six percent (n = 25) reported using Naviance daily, whereas 24% (n = 9) indicated using Naviance weekly, and 5% (n = 2) reported monthly use. Finally, 5% (n = 2) reported not using Naviance at all. Table 3 shows the frequency of Naviance usage.

Table 3

Naviance Frequency of Use by School Counselors

Descriptors	Daily	Weekly	Monthly	At Least Every Other Month	Not At All
Middle School	10	2	0	0	1
High School	15	7	2	0	1

Note.
Frequency in which school counselors used Naviance during the
2016–2017 academic year.

Discussion

Implementing technology in school counseling is a call to action from past counseling researchers (Casey, Bloom, & Moan, 1994; Creamer, 2000; Dahir, 2009; Granello, 2000) to move the profession into the future (Dahir, 2009). When school counselors adopt and integrate technology into their practices, they can be effective in their role (Hu et al., 2003). The first research question, whether school counselors choose to use or not use Naviance, was answered by most of the counselors, who indicated that the ease of use and the overall usefulness influenced their decision to use the Naviance platform or not. Barriers identified that interfered with ease of use and usefulness were bandwidth issues within schools and school counselors’ ability to connect to the resource tool.

The second research question, how Naviance acceptance and usage enhance school counseling practices, productivity, and efficiency, was answered by most of the school counselors in this study, who stated that the use of Naviance positively enhanced their job productivity, efficiency, and counseling practices. Particularly, the ability to introduce college-related material to help students develop individual education plans, identify courses, provide social and emotional resources, and advise on graduation status and college eligibility, was positive. In addition, more school counselors used Naviance as a vehicle to share information with teachers, administrators, and parents.

Limitations

There were several limitations. The results of this study indicated that school counselors had positive attitudes toward the integration and usage of Naviance; however, the findings were limited to middle and high school counselors who work in a specific public school district located in the southwestern part of the United States, which prevented the inclusion of experiences and expertise of other public and private school counselors throughout the country. The addition of other Naviance users in small public and private schools might have produced other results. Another limitation was that the first researcher has used Naviance for the past 10 years in various roles as a district administrator. To prevent bias, the first researcher did not make assumptions based on what participants chose to share or attempt to present answers. In contrast, the second researcher has never used Naviance, which allowed for an unbiased viewpoint when writing the analysis. Further, a school counselor educator, familiar with Naviance, reviewed and read over this study prior to publication to minimize researcher technology bias.

Finally, Naviance generally provides district offices and schools with reports on engagement activities and staff and student usage. Although researchers used the Naviance engagement reports to speak to overall usage in subcategories such as college planning, career planning, guidance curriculum, and test preparation, multiple school engagement reports were combined to differentiate middle and high school engagement activities. In addition, Naviance provides reports on staff usage; therefore, the first researcher retrieved data at the school site level to determine counselor usage rather than usage by staff, such as teachers and administrators, during data analysis.

Implications for Counselors

One of the benefits of using an online platform such as Naviance is that it can bring value to the practices of school counselors when helping to introduce and prepare students for college. For instance, such a tool can support dissemination of critical student-related information, data collection, tracking and analysis, customization of 4-year graduation plans, and communication between multiple stakeholders, to name a few.

The knowledge generated from this study is useful to school counselors in several ways. First, understanding the intricacies and impact of Naviance could offer school counselors additional ways to support their students’ academic development, college preparedness, and readiness efforts, and to share and provide social and emotional resources to students. Second, knowing which features in Naviance influence career and college-related outcomes at the middle and high school level can improve engagement and communication efforts between school counselors, parents, and teachers. Third, exposing students early to the numerous college readiness features and functionalities in Naviance can increase graduation and college application rates of high school students, which is consistent with literature findings. Fourth, capturing college- and career-related data can help school counselors communicate, gather, analyze, and synthesize information required to meet state accountability standards and evaluate the effectiveness of counseling programs.

Recommendations and Future Research

Given the benefits of integrating Naviance into the daily practice of school counselors, two recommendations for future practice include leveraging the reports and analytic features to emphasize programmatic effectiveness and student outcomes, and infusing the college-related curriculum into subject matter classes. Although the high school counselor is the primary interpreter of the college preparation, application, and enrollment sources, incorporating college-related information into classroom instruction could be used as a springboard to deliver information on college and career readiness and support the understanding of the relationship between academic performance and college eligibility. This practice could free up time for the high school counselor to have more meaningful and deliberate conversations with students to support their understanding of college norms and expectations and effectively facilitate the college enrollment process.

The findings indicate a need to extend TAM by exploring other external factors that influence user acceptance of Naviance. For example, future research could explore the connection between counselor usage and the number of hours trained on Naviance. Low counselor usage could be the result of insufficient training or differences in age. In addition, as many schools, particularly those located in urban settings, focus on increasing college eligibility, future studies should be conducted on Naviance test prep (i.e., ACT, SAT, AP) and student outcomes.

Conclusion

Research into school counselors’ technology integration and usage has been a focus in the counseling profession since the 1980s and continues to be an important area for investigation today. Most school counselors suggested that Naviance was useful in their role as a school counselor in providing academic, career, college, and personal counseling to students and that actual usage enhanced their job performance, productivity, and proficiency. In addition, many expressed that Naviance was a tool that required minimal effort, if usage was ongoing. Lastly, perceived usefulness and perceived ease of use was connected to school counselors’ positive attitude regarding Naviance.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

References

Alhojailan, M. I. (2012). Thematic analysis: A critical review of its process and evaluation. Retrieved from https://fac.ksu.edu.sa/sites/default/files/ta_thematic_analysis_dr_mohammed_alhojailan.pdf

Bernard, H. R., & Ryan, G. W. (2010). Analyzing qualitative data: Systematic approaches (1st ed.). Los Angeles, CA: Sage.

Blanchard, A., Prior, F., Barton, J., & Dawson, T. (2016). Barriers in new health and social care technology implementation in the United Kingdom, a case study. Retrieved from https://www.computer.org/csdl/proceedings/dese/2016/5487/00/07930625.pdf

Carey, J., & Dimmitt, C. (2004). The web and school counseling. Interdisciplinary Journal of Practice, Theory, and Applied Research, 21(3–4), 69–79. doi:10.1300/J025v21n03_08

Carlson, L., Portman, T., & Bartlett, J. (2006). Professional school counselors’ approaches to technology. Professional School Psychology, 9, 252–256. doi:10.5330/prsc.9.3.9162536405845454

Casey, J. A., Bloom, J. W., & Moan, E. R. (1994). Use of technology in counselor supervision. Retrieved from https://www.counseling.org/resources/library/ERIC%20Digests/94-25.pdf

Christian, D., Lawrence, A., & Dampman, N. (2017). Increasing college access through the implementation of Naviance: An exploratory study. Journal of College Access, 3(2), 28–44.

Clarke, V., & Braun, V. (2013). Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), 120–123.

Creamer, M. B. (2000). Technology utilization in the field of school counseling: An action research study. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.502.8338&rep=rep1&type=pdf

Dahir, C. A. (2009). School counseling in the 21st century: Where lies the future? Introduction to the special section. Journal of Counseling & Development, 87, 3–5. doi:10.1002/j.1556-6678.2009.tb00542.x

Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. Retrieved from https://deepblue.lib.umich.edu/bitstream/handle/2027.42/30954/0000626.pdf?sequence=1

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5, 1–4. doi:10.11648/j.ajtas.20160501.11

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

Frels, R. K., & Onwuegbuzie, A. J. (2013). Administering quantitative instruments with qualitative interviews: A mixed research approach. Journal of Counseling & Development, 91, 184–194.
doi:10.1002/j.1556-6676.2013.00085.x

Granello, P. F. (2000). Historical context: The relationship of computer technologies and counseling. Retrieved from https://www.ericdigests.org/2001-3/context.htm

Guzman, A., & Nussbaum, M. (2009). Teaching competencies for technology integration in the classroom. Journal of Computer Assisted Learning, 25, 453–469. doi:10.1111/j.1365-2729.2009.00322.x

Hayden, L., Poynton, T., & Sabella, R. A. (2008). School counselors’ use of technology within the ASCA national model’s delivery system. Journal of Technology in Counseling 5(1). Retrieved from https://www.researchgat
e.net/publication/287417135_School_counselors’_use_of_technology_with_in_the_ASCA_national_mod
el’s_delivery_system

Hobsons. (2017). Connecting learning to life. Retrieved from https://www.naviance.com/solutions

Holcomb-McCoy, C., Gonzalez, I., & Johnston, G. (2009). School counselor dispositions as predictors of data usage. Professional School Counseling, 12(5). doi:10.1177/2156759X0901200504

Hu, P. J.-H., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information & Management, 41, 227–241. doi:10.1016/S0378-7206(03)00050-8

Kozlowski, K. A., Mikesina, L., & Genova, A. (2015). Incorporating technology into a school counseling body image group. Retrieved from https://www.counseling.org/docs/default-source/vistas/incorporating-technology-into-a-school-counseling-body-image-group.pdf?sfvrsn=8

Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support Systems, 29, 269–282. doi:10.1016/S0167-9236(00)00076-2

Loague, A. M., Alexander, J. J., & Reynolds, G. P. (2010). The school counselor web page. Retrieved from https://www.counseling.org/docs/default-source/vistas/vistas_2010_article_62.pdf?sfvrsn=b665e8d_11

Millsom, A., & Bryant, J. (2006). School counseling departmental websites: What message do we send? Professional School Counseling, 10(2).

Nair, I., & Das, V. M. (2011). Analysis of recent studies undertaken for assessing acceptance of technology among teachers using TAM. International Journal of Computer Applications, 32(8), 38–46.

National Center for Education Statistics (2017). The condition of education 2017. Retrieved from https://nces.ed.gov/pubs2017/2017144.pdf

Onwuegbuzie, A. J., Leech, N. L., & Collins, K. M. T. (2011). Toward a new era for conducting mixed analyses: The role of quantitative dominant and qualitative dominant crossover mixed analyses. In M. Williams & W. P. Vogt (Eds.), The Sage handbook of innovation in social research methods (pp. 353–384). Thousand Oaks, CA: Sage.

Onwuegbuzie, A. J., & Teddlie, C. (2003). A framework for analyzing data in mixed methods research. In A.
Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (1st ed., pp.
351–383). Thousand Oaks, CA: Sage.

Padmavathi, M. (2016). A study of student-teachers’ readiness to use computers in teaching: An empirical study. I-manager’s Journal on School Educational Technology, 11(3), 29–39.

Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42, 533–544.
doi:10.1007/s10488-013-0528-y

Rainey, S., Mcglothlin, J., & Miller, L. (2008). Technology: School counselor attitudes, experiences and competency. Journal of Technology in Counseling, 5(1).

Reljic, R., Harper, A., & Crethar, H. (2013). Using technology creatively to empower diverse populations in counseling. Retrieved from https://www.counseling.org/docs/default-source/vistas/using-technology-creatively-to-empower-diverse-populations-in-counseling.pdf?sfvrsn=57116101_12

Ross, A. A., & Onwuegbuzie, A. J. (2010). Mixed methods research design: A comparison of prevalence in JRME and AERJ. International Journal of Multiple Research Approaches, 4, 233–245.
doi:10.5172/mra.2010.4.3.233

Saade, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the Technology Acceptance Model. Information & Management, 42, 317–327. doi:10.1016/j.im.2003.12.013

Serdyukov, P. (2017). Innovation in education: What works, what doesn’t, and what to do about it? Journal of Research in Innovative Teaching and Learning, 10, 4–33. doi:10.1108/JRIT-10-2016-0007

Steele, T. M., Jacokes, D. E., & Stone, C. B. (2014). An examination of the role of online technology in school counseling. Professional School Counseling, 18, 125–135. doi:10.1177/2156759X0001800118

Sung, Y.-T., Chang, K.-E., & Liu, T.-C. (2016). The effects of integrating mobile devices with teaching and learning on students’ learning performance: A meta-analysis and research synthesis. Computers & Education, 94, 252–275. doi:10.1016/j.compedu.2015.11.008

Tarhini, A., Hone, K., & Liu, X. (2015). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 46, 739–755. doi:10.1111/bjet.12169

Teo, T. (Ed.). (2011). Technology acceptance in education: Research and issues. Retrieved from http://users.ugent.be/~wduyck/articles/PynooDevolderTondeurVanBraakDuyckDuyck2011b.pdf

Tri Anni, C., Sunawan, S., & Haryono, H. (2018). School counselors’ intention to use technology: The technology acceptance model. Retrieved from https://www.researchgate.net/publication/324438702_School_Counselors’_Intention_to_Use_Technology_The_Technology_Acceptance_Model

Van Horn, S., & Myrick, R. (2001). Computer technology and the 21st century school counselor. Retrieved from https://www.researchgate.net/publication/234687258_Computer_Technology_and_the_21st_Century_School_Counselor

Yin, R. K. (2014). Case study research: Design and methods (5th ed.). Thousand Oaks, CA: Sage.

Vernell Deslonde is a director at Fontana Unified School District. Michael Becerra is an adjunct instructor at the University of North Texas at Dallas. Correspondence can be addressed to Vernell Deslonde, 9680 Citrus Ave., Fontana, CA 92334, deslonde08@gmail.com.

Burnout and Implications for Professional School Counselors

Sep 1, 2018 | Volume 8 - Issue 3

Nayoung Kim, Glenn W. Lambie

To prevent school counselors from experiencing feelings of burnout, identifying relevant factors is important. The purpose of this article is to review studies investigating the constructs of burnout and occupational stress in school counseling samples. Eighteen published research articles fit the inclusion criteria for this review. The researchers identified external and internal variables relating to school counselor burnout, as well as protective and risk factors. The review identified that school counselors’ higher level of burnout correlated with having non-counseling duties, being assigned large caseloads, working in schools that did not meet adequate yearly progress (AYP) status, experiencing a lack of supervision, possessing greater emotion-oriented stress coping scores, providing fewer direct student services, and having greater perceived stress. In contrast, feelings of burnout among school counselors were mitigated when counselors received supervision, possessed higher task-oriented stress coping strategies, scored at higher levels of ego maturity, reported greater occupational support at their schools, had greater grit scores, and worked in schools that met AYP.

Keywords: burnout, occupational stress, school counselors, non-counseling duties, coping strategies

There are multiple definitions of burnout (e.g., Burke & Richardson, 2000; Stalker & Harvey, 2002); however, the primary consistent aspect of burnout is that it is a psychological phenomenon associated with job-related stress (Maslach, 2017). Burnout occurs when professionals are unable to meet their own needs, as well as their clients’ needs, in a high-pressure environment (Maslach, 2017). Freudenberger (1990) identified common symptoms of burnout, including negative changes in individuals’ (a) attitudes and decision making; (b) physiological states; (c) mental, emotional, and behavioral health; and (d) occupational motivation. Burnout has significant consequences, including compromised physical health, increased risk of mental health disorders (e.g., depression, substance abuse), poor job performance, absenteeism, occupational attrition, and low self-esteem (Maslach & Leiter, 2016). Burnout can also cause symptoms such as fatigue, exhaustion, and insomnia (Armon, Shirom, Shapira, & Melamed, 2008).

Burnout in School Counseling

Morse, Salyers, Rollins, Monroe-DeVita, and Pfahler (2012) identified that 21% to 67% of mental health professionals reported experiencing high levels of burnout, possibly because of dealing with high client caseloads (Ducharme, Knudsen, & Roman, 2007) or overall job effectiveness (Stalker & Harvey, 2002). In addition, Oddie and Ousley (2007) found that 21% to 48% of mental health workers reported experiencing high levels of emotional exhaustion. School counselors specifically are at risk for experiencing feelings of burnout because of their multiple job demands, including paperwork, parent conferences, school-wide testing, large caseloads, and requests from administrators (McCarthy & Lambert, 2008), and other factors such as role ambiguity and limited occupational support (Young & Lambie, 2007). The school counseling job environment, where “the demands of the work are high, but the resources to meet those demands are low” (Maslach & Goldberg, 1998, pp. 63–64), increases susceptibility to experiencing feelings of burnout (e.g., average student-to-counselor ratio being 491-to-1; National Center for Education Statistics, 2016). Stephan (2005) found that within a national sample of school counselors, 66% of middle school counselors scored at moderate to high levels of emotional exhaustion. Further, Wachter (2006) found that 20% of the school counselors in her investigation (N = 132) experienced feelings of burnout; 16% scored at moderate levels of burnout, and 4% scored at severe levels of burnout. Thus, many school counselors experience feelings of burnout that may influence their ability to provide ethical and effective counseling services to the students they serve.

School counselors may experience chronic fatigue, depersonalization, or feelings of hopelessness and leave their jobs because of the rigidity of school systems and limited support (Young & Lambie, 2007). In fact, counselors experiencing significant feelings of burnout provide reduced quality of service to their clientele because burnout relates to lower productivity, turnover intention, and a lowered level of job commitment (Maslach, Schaufeli, & Leiter, 2001). Because of the importance of preventing the burnout phenomenon, the American School Counselor Association’s (ASCA; 2016) ethical standards note that school counselors are responsible for maintaining their health, both physically and emotionally, and caring for their wellness to ensure their effective practice. The American Counseling Association’s (2014) ethical standards also state that school counselors have an ethical responsibility to monitor their feelings of burnout and remediate when their feelings potentially influence their ability to provide quality services to their stakeholders. To monitor burnout, counselors need to understand the symptoms of burnout and prevent it from happening, while maintaining their psychological well-being.

School counselors face challenges with their significant job demands (McCarthy, Van Horn Kerne, Calfa, Lambert, & Guzmán, 2010), such as large caseloads (Lambie, 2007) and extreme amounts of non-counseling duties (Moyer, 2011). In fact, school counselors report job stress and dissatisfaction when they are required to complete non-counseling duties, hindering their ability to work with their students (McCarthy et al., 2010). Examples of non-counseling duties include clerical tasks, such as scheduling students for classes; fair share, such as coordinating the standardized testing program; and administrative duties, such as substitute teaching (Scarborough, 2005). School counselors with large caseloads and high student-to-counselor ratios are more likely to experience increased feelings of burnout (Bardhoshi, Schweinle, & Duncan, 2014). Although ASCA (2015) recommends a student-to-counselor ratio of 250-to-1, the U.S. average student-to-counselor ratio is almost double the recommended proportion (491-to-1; National Center for Education Statistics, 2016).

Insufficient resources for school counselors and negative job perception increase their likelihood of experiencing feelings of burnout. Lower levels of principal support and lack of clinical supervision raise school counselors’ occupational stress (Bardhoshi et al., 2014; Moyer, 2011). For instance, school counselors with higher levels of role ambiguity are likely to experience burnout (Wilkerson & Bellini, 2006). School counselors experience role ambiguity when their responsibilities or the expected level of performance is not clearly identified (Coll & Freeman, 1997). As a result, school counselors report increased levels of stress (Culbreth, Scarborough, Banks-Johnson, & Solomon, 2005), leading to burnout and attrition from the profession (Wilkerson & Bellini, 2006). ASCA (2016) dictated that school counselors’ responsibilities include providing counseling services to students to support their development, which distinguishes them from other school personnel. With the importance of preventing burnout in school counseling, the purpose of this review is twofold: (a) to present identified factors influencing school counselors’ levels of burnout and (b) to offer strategies to assist school counselors in mitigating the feelings of burnout.

Research Examining Burnout in School Counseling

We began by conducting a formal search of electronic databases—PsycINFO, ERIC (EBSCOhost), and Academic Search Premiere—relating to school counselor burnout. The search term burnout was first used to analyze the research trend in the field. Both the search terms burnout and school counselors OR school counseling were used to collect any articles on the topic of school counselor burnout published between 2000 and 2018. An additional search was conducted with the terms occupational stress and school counselors OR school counseling to identify potential studies related to the topic in the same type of literature.

The following inclusion criteria were applied for our review: (a) investigations of school counselor burnout and occupational stress, (b) sample participants were school counselors in the United States, (c) the primary topic of the investigation was burnout and/or occupational stress, (d) articles were written in English, (e) articles were published in refereed journals, and (f) articles were published between 2000 and 2018. In addition, our review excluded literature reviews, editorials, and rejoinders. The abstracts of the articles meeting the criteria were examined and confirmed in order to be included in our review.

Our literature search based on the inclusion criteria produced 51 articles. As not all articles from the search satisfied the criteria, the articles were reviewed manually to evaluate whether they met the criteria, resulting in 35 articles not meeting criteria (e.g., conceptual articles, studies related to teachers) and 16 articles meeting all criteria. An additional literature search yielded two more studies meeting the inclusion criteria, identifying 18 studies in total. None of the identified research articles examined prevention or treatment interventions for burnout in school counselors. The 18 investigations had school counselor burnout or occupational stress as the constructs of interest. The research findings identified the positive relationships between school counselors’ burnout or occupational stress scores and the following factors: (a) non-counseling duties, (b) large caseloads, (c) not meeting adequate yearly progress (AYP) status (i.e., the expected amount of students’ academic growth per year based on the No Child Left Behind mandate [Minnesota House of Representatives, 2003]), (d) lack of supervision, (e) emotion-oriented stress coping scores, (f) grit, and (g) perceived stress.

Fourteen out of 18 articles provided information related to school counselor burnout (see Table 1 for quantitative studies and Table 2 for qualitative studies), and the other four studies investigated school counselors’ occupational stress (see Table 3). Occupational stress refers to the strain a person experiences when the perceived stress in a workplace outweighs their ability to cope (Decker & Borgen, 1993). Quantitative research methods were employed in 15 of the investigations, two used mixed-methods, and one study utilized a qualitative approach. For all 18 articles, the participants were current school counselors, and the number of participants ranged from 3 to 926. Effect sizes were categorized depending on the analysis into three groups (i.e., small, medium, and large) based on the effect size matrix from Sink and Stroh (2006), offering a better understanding of the results. Specifically, the effect size from independent samples t-test (2 groups; Cohen’s d) is interpreted as small for 0.2, medium for 0.5, and large for 0.8. For the effect size of other analyses listed in this review, including paired-samples t-tests (η²), multiple regression (R²), and analysis of variance (ANOVA; η²), 0.01 is considered as small, 0.06 as medium, and 0.14 as large.

Table 1

Summary of Quantitative/Mixed Studies Related to Professional School Counselor (PSC) Burnout

Study	Sample	Variables	Findings
Bain, Rueda, Mata-Villarreal, & Mundy (2011)	PSCs in rural districts of South Texas (N = 27) Convenient Sampling	Mental health awareness, the amount of time spent on academic advising	Feelings of burnout were reported by the majority of the PSCs (89%) in the study and many of them spent the greatest amount of time on administrative duties and the least on counseling.
Bardhoshi, Schweinle, & Duncan (2014)	PSCs (N = 212) Random Sampling	Non-counselor duties, school factors, five subscales of the CBI	Non-counseling duties and school factors were associated with PSC burnout. Non-counseling duties explained the variance of the three burnout subscales: Exhaustion (11%; medium effect size), NWE (6%; medium effect size), and DPL (8%; medium effect size). Non-counseling duties and other factors (e.g., caseload, principal support) explained the variance of the four burnout subscales: Exhaustion (21%; large effect size), Incompetence (9%; medium effect size), NWE (49%; large effect size), and DPL (17%; large effect size).
Butler & Constantine (2005)	PSCs (N = 533) Random Sampling	Collective self-esteem, burnout, demographics	Collective self-esteem explained 3% of the variance of PSC burnout (small effect size). In particular, PRCS (2%) and PUCS (1%) accounted for PA (both small effect sizes), and IICS explained 1% of feelings of DP and PA (both small effect sizes). Higher collective self-esteem was associated with lower PSC burnout. PSCs working in urban settings tended to have higher levels of burnout than the counterparts in other environmental settings. PSCs with experience of 20–29 years reported higher levels of burnout than the counterparts with 0–9 years of experience. PSCs with experience of 30 or more years reported higher levels of burnout than those with less experience.
Gnilka, Karpinski, & Smith (2015)	PSCs (N = 269) Convenient Sampling	Five subscales on the CBI	Effect size differences were found between PSCs and other professionals in the counseling fields (Exhaustion, d = .26, small effect size; DC, d = -.50, medium effect size). Effect size differences were noted between PSCs and sexual offender and sexual abuse therapists (Exhaustion, d = .27, small effect size; DPL, d = -.23, small effect size; DC, d = -.82, large effect size).
Lambie (2007)	PSCs (N = 218) Random Sampling	Ego maturity, three subscales on the MBI-HSS	PSCs with greater levels of ego maturity tended to have a higher level of PA than those with lower ego maturity. Ego maturity predicted PA (3.3%; small effect size). Occupational support and the subscales of burnout were correlated. Reported occupational support predicted EE (16%; large effect size), DP (12%; medium effect size), and PA (7.2%; medium effect size).
Limberg, Lambie, & Robinson (2016-2017)	PSCs (N = 437) Random Sampling/ Purposive Sampling	Altruistic motivation, altruistic behavior, burnout	PSCs with greater levels of altruism had lower levels of EE and higher feelings of PA. PSC altruism explained 31.36% of the variance in EE (large effect size), and 29.16% of the variance in PA (large effect size). Self-Efficacy accounted for 14.4% of the variance in EE (large effect size) and 9% of the variance in PA (medium effect size).
Moyer (2011)	PSCs (N = 382) Convenient Sampling	Non-guidance activities, supervision, student-to-counselor ratios, five subscales of the CBI	Non-guidance–related duties and clinical supervision were significant predictors of PSC burnout. Non-guidance duties (7.3%; medium effect size) and supervision (9%; medium effect size) predicted burnout.
Mullen, Blount, Lambie, & Chae (2017)	PSCs (N = 750) Random Sampling	Perceived stress, burnout, job satisfaction	Perceived stress predicted burnout positively (large effect size) and job satisfaction negatively (large effect size). Perceived stress and burnout predicted job satisfaction (large effect size). Burnout mediated the relationship between perceived stress and job satisfaction.
Mullen & Crowe (2018)	PSCs (N = 330) Convenient Sampling	Grit, stress, burnout	Grit was negatively related to burnout (small effect size) and stress (small to medium effect size).
Mullen & Gutierrez (2016)	PSCs (N = 926) Random Sampling	Burnout, perceived stress, direct student services	Burnout attributed to direct counseling activities (12%; medium effect size), direct curriculum activities (5%; small to medium effect size), and percentage of time at work providing direct services to students (6%; medium effect size).
Wachter, Clemens, & Lewis (2008)	PSCs (N = 249) Random Sampling	Demographics, stakeholder involvement, lifestyle themes, burnout	Burnout and lifestyle themes were associated. Perfectionism subscale was negatively related to burnout, and the Self-Esteem subscale was positively related to PSC burnout. About 15.1% of the variance in burnout was accounted for by the lifestyle themes of Self-Esteem and Perfectionism (large effect size).
Wilkerson & Bellini (2006)	PSCs in northeastern U.S. (N = 78) Systematic Random Sampling	Demographics, intrapersonal, and organizational factors; three subscales on the MBI-ES	Demographic (age, counseling experience, supervision, and student/counselor ratio), intrapersonal, and organizational factors significantly accounted for the amount of the variance in each subscale of burnout, including EE (45%; large effect size), DP (30%; large effect size), and PA (42%; large effect size).
Wilkerson (2009)	PSCs (N = 198) Random Sampling	Demographic and organizational stressors and individual coping strategies; three subscales on the MBI-ES	Demographic factors (years of experience and student/counselor ratio), organizational stress, and coping styles explained the variance of each subscale of burnout including EE (49%; large effect size), DP (27%; large effect size), and PA (36%; large effect size).

Table 2

Summary of Qualitative/Mixed Studies Related to Professional School Counselor Burnout

Study	Sample	Topic	Identified Themes
Bain, Rueda, Mata-Villarreal, & Mundy (2011)	PSCs in rural districts of South Texas (N = 27) Convenient Sampling	Helpful ways to better provide mental health services at school	Having access to additional staff and additional education and awareness in terms of helpful ways to provide mental health services at their school.
Bardhoshi, Schweinle, & Duncan (2014)	PSCs (N = 252) Random Sampling	a) Their experience of burnout b) The meaning of performing non-counseling duties	a) Lack of time, budgetary constraints, lack of resources, lack of organizational support, etc. b) Adverse personal/professional effects, a reality of the job, reframing the duties within the context of the job.
Sheffield & Baker (2005)	Female PSCs (N = 3) Purposive Sampling	Burnout experience	Important beliefs, burnout feelings, burnout attitude, (lack of) collegial support.

Table 3

Summary of Quantitative Studies Related to Professional School Counselor Occupational Stress

Study	Sample	Variables	Findings
Bryant & Constantine (2006)	Female PSCs (N = 133) Random Sampling	Role balance, job satisfaction, satisfaction with life, demographics	Multiple role balance ability and job satisfaction positively predicted overall life satisfaction. Role balance and job satisfaction explained the variance of life satisfaction (41%; large effect size).
Culbreth, Scarborough, Banks-Johnson, & Solomon (2005)	PSCs (N = 512)Stratified Random Sampling	Role conflict, role ambiguity, role incongruence, demographics	Perceived match between the job expectations and actual experiences predicted role-related job stress, including role conflict (7.6%; medium effect size); role incongruence (19.7%; large effect size); and role ambiguity (8.3%; medium effect size).
McCarthy, Van Horn Kerne, Calfa, Lambert, & Guzmán (2010)	PSCs in Texas (N = 227) Convenient Sampling	Demographics, job stress, resources and demands	Job stress was different between the resourced, balanced, and demand groups. The effect sizes were large in the differences between the demand group and the resourced group (1.62; large effect size) and the balanced group (0.70; large effect size).
Rayle (2006)	PSCs (N = 388)Convenient Sampling	Demographics, mattering, job-related stress	Thirty-five percent of the variance in overall job satisfaction was explained by mattering to others at work and job-related stress (large effect size). Mattering to others (19.36%; large effect size) and job-related stress (16.81%; large effect size) explained the variance in overall job satisfaction.

Three instruments were used to measure levels of school counselor burnout, including: (a) the Maslach Burnout Inventory (MBI; Maslach, Jackson, & Leiter, 1996), (b) the Counselor Burnout Inventory (CBI; S. M. Lee et al., 2007), and (c) the Burnout Measure Short Version (BMS; Malach-Pines, 2005). Maslach and Jackson (1981) defined burnout with three dimensions: Emotional Exhaustion (EE), Depersonalization (DP), and reduced Personal Accomplishment (PA). Emotional exhaustion is to exhaust one’s capacity to continuously involve with clients (R. T. Lee & Ashforth, 1996). Not being able to respond to clients’ needs may cause counselors to distance themselves from their job emotionally and cognitively, which is defined as depersonalization. Lastly, having a lower sense of effectiveness may reduce feelings of personal accomplishment (Maslach et al., 2001). Four studies used the MBI-Education Survey (MBI-ES), which was designed for the education population, and another study utilized the MBI-Human Services Survey (MBI-HSS), in which the word students from the MBI-ES is substituted with recipients in a third of the items (Sandoval, 1989).

Four studies used the CBI, which is a 20-item instrument with five subscales, including:
(a) Exhaustion, (b) Incompetence, (c) Negative Work Environment (NWE), (d) Devaluing Client (DC), and (e) Deterioration in Personal Life (DPL). Exhaustion is the condition of being physically and emotionally exhausted by the duties of a counselor, and incompetence focuses on counselors’ feelings of being incompetent. While negative work environment refers to the stress caused by the working environment, devaluing client is related to being unable to establish emotional connectedness with clients. Finally, deterioration in personal life assesses the level of deterioration in a counselor’s personal life. Sample items include “I feel exhausted due to my work as a counselor,” and “I feel I have poor boundaries between work and my personal life.” The internal consistency of the CBI ranged from .73 to .85 (S. M. Lee et al., 2007). In addition, three studies used the BMS (Malach-Pines, 2005), a 10-item scale in which participants rate their answers to the question “When you think about your work overall, how often do you feel the following?” in seven prompts, including: “Trapped,” “Hopeless,” and “Helpless.” The BMS is adapted from the original version of the Burnout Measure (Pines & Aronson, 1988). The internal consistency of the BMS ranged from .85 to .87 (Malach-Pines, 2005).

Researchers investigated different factors relating to school counselor burnout within the 18 published articles. One of the studies provided descriptive statistics of school counselor burnout, comparing school counselors to other mental health professionals and showing how burnout symptoms may emerge (N = 269; Gnilka, Karpinski, & Smith, 2015). School counselors had greater levels of Exhaustion (d = .26; small effect size) and lower levels of DC (d = -.50; medium effect size) than mental health professional participants. Furthermore, school counselors had greater levels of Exhaustion (d = .27; small effect size) and lower levels of DC (d = -.82; large effect size) compared to the mental health professional participants working with sex offenders and clients that have been sexually abused. Therefore, school counselors score higher in exhaustion as compared to other mental health professionals and score lower on devaluing their clients.

Individual Factors Related to Burnout

The two categories of individual factors relating to school counselor burnout were (a) psychological constructs and (b) demographic factors. The psychological constructs included ego maturity (Lambie, 2007), collective self-esteem (Butler & Constantine, 2005), altruism (Limberg, Lambie, & Robinson, 20162017), lifestyle themes (Wachter, Clemens, & Lewis, 2008), coping styles (Wilkerson, 2009), perceived stress (Mullen, Blount, Lambie, & Chae, 2017), and grit (Mullen & Crowe, 2018). The definitions of these psychological constructs related to school counselor burnout follow.

Ego maturity refers to the fundamental element of an individual’s personality, encompassing components of self, social, cognitive, character, and moral development (Loevinger, 1976). When individuals’ egos develop, they become more individualistic, autonomous, and highly aware of themselves (Loevinger, 1976). Collective self-esteem is individuals’ perception of their identification with the social group they belong to (Bettencourt & Dorr, 1997). Altruism is the behavior driven by values or goals individuals possess or their concerns for others, aside from external rewards (Eisenberg et al., 1999). A lifestyle is an individual’s way of perceiving self, others, and the world (Mosak & Maniacci, 2000), and lifestyle themes refer to common patterns people possess in relation to their lifestyles (Mosak, 1971). Coping is defined as cognitive and behavioral efforts to deal with specific demands that take up or exceed individuals’ resources (Lazarus & Folkman, 1984), and coping styles refer to individuals’ relatively stable patterns in handling stress (Heszen-Niejodek, 1997). Perceived stress represents the extent to which individuals evaluate their situations as stressful (Cohen, 1986). Grit is “perseverance and passion for long-term goals” (Duckworth, Peterson, Matthews, & Kelly, 2007, p. 1087). Specifically, grit refers to efforts to achieve a goal despite challenges. In addition to psychological constructs, the demographic factors category included years of experience in school counseling (Butler & Constantine, 2005; Wilkerson, 2009; Wilkerson & Bellini, 2006) and age (Wilkerson & Bellini, 2006).

Psychological constructs. Seven studies identified that psychological constructs relate to school counselors’ feelings of burnout. Five of seven factors had large effect sizes, including ego maturity, altruism, lifestyle themes, coping styles, and grit, and three of the factors with large effect sizes were associated with Emotional Exhaustion (EE) among the MBI (Maslach et al., 1996) subscale scores (i.e., ego maturity, altruism, and coping styles).

Specifically, Lambie (2007) examined the directional relationship between school counselors’
(N = 218) burnout and ego maturity, identifying that those counselors with higher levels of ego maturity were likely to have greater feelings of Personal Accomplishment (PA; R² = .033). The researcher also investigated the relationship between the school counselors’ reported occupational support and their MBI burnout subscales scores (Maslach & Jackson, 1996), identifying that each MBI subscale relates to the participants’ levels of reported occupational support; EE (large effect size; R² = .167); DP (medium effect size; R² = .120); and PA (medium effect size; R² = .072). The results indicated that school counselors scoring at higher ego maturity levels had lower feelings of burnout, and counselors experiencing high levels of occupational support had significantly lower burnout scores.

The relationship between burnout and collective self-esteem was investigated within a sample of school counselors (N = 533; Butler & Constantine, 2005). The Collective Self-Esteem Scale has four subscales (Luhtanen & Crocker, 1992), including (a) Private Collective Self-Esteem (PRCS), (b) Public Collective Self-Esteem (PUCS), (c) Membership Collective Self-Esteem (MCS), and (d) Importance to Identity Collective Self-Esteem (IICS). These subscales measure individuals’ perception of social groups they belong to, including how they feel about the group (PRCS), how they perceive others feel about the group (PUCS), how they perceive themselves being a good member of the group (MCS), and how important their social group is to their self-concept (IICS). These four Collective Self-Esteem Scale subscales explained 3% of the variance in the burnout subscales (Pillai’s trace = .08, F [12, 1584] = 3.48, p < .001, η²_M = .03; Maslach & Jackson, 1986).

In general, higher collective self-esteem relates to lower levels of burnout, and different dimensions of collective self-esteem relate to different components of burnout. Higher PRCS was associated with higher feelings of PA (η² = .02), and higher PUCS was related to lower levels of EE (η² = .01). The school counselors’ IICS subscale scores were related to their lower feelings of DP (η² = .01) and greater feelings of PA (η² = .01). Although a small amount of variance in burnout scores (.01–.02) was explained by the components of collective self-esteem, the positive relationship between higher PRCS and higher feelings of PA identified that positive perceptions of the group school counselors belong to might reduce their feelings of burnout. For instance, having a sense of pride as a school counselor by observing other school counselors’ hard work and good relationships with students may promote their sense of PRCS, which may lead to higher feelings of PA. Taken together, promoting school counselors’ collective self-esteem may decrease their feelings of burnout.

Limberg and colleagues (2016–2017) investigated the directional relationship between school counselors’ (N = 437) levels of altruism and burnout. The school counselors with greater levels of altruism had lower levels of EE and higher feelings of PA. Specifically, the altruism subscales of Positive Future Expectation (PFE) and Self-Efficacy from the Self-Report Altruism Scale (Rushton, Chrisjohn, & Fekken, 1981) and two subscales of burnout (MBI) correlated (χ² = 403.611, df = 216, χ² ratio = 1.869, p < .001). PFE and Self-Efficacy accounted for 31.36% of the variance in the EE subscale (large effect size), and 29.16% of the variance in the PA subscale (large effect size). The Self-Efficacy subscale, which involves individuals’ perceived competence in a certain skill, explained 14.4% of the variance in EE subscale scores (large effect size), and 9% of the variance in PA subscale scores (medium effect size). Therefore, the results identified that school counselors’ levels of altruism negatively contribute to their burnout scores.

Burnout was related to lifestyle themes among school counselors (N = 249; Wachter et al., 2008). Two subscales of lifestyle themes from the Kern Lifestyle Scale (Kern, 1996), Self-Esteem and Perfectionism, accounted for 15.1% of the variance in burnout (large effect size; R² = .151). Specifically, the Perfectionism subscale was negatively related to school counselor burnout scores (Burnout Measure: Short Version; BMS; Malach-Pines, 2005), and the Self-Esteem subscale was positively related to school counselor burnout. As a result, these findings identified school counselors’ personality factors relating to their risk of burnout, supporting that higher levels of perfectionism and lower levels of self-esteem may increase the likelihood of experiencing burnout.

Two studies employed hierarchical regression analyses to examine what factors may predict burnout subscale scores of the MBI, and one of the predicting variables was coping styles (Wilkerson, 2009; Wilkerson & Bellini, 2006). Wilkerson (2009) used four-step hierarchical regression models that included demographics, organizational stressors, and coping strategies, such as task-oriented, emotion-oriented, and avoidance-oriented coping (N = 198). The models with large effect sizes explained all three MBI burnout subscales. Specifically, 49% of the variance in the EE subscale was explained (large effect size; R² = .49); 27% of the variance in the DP subscale was accounted for (large effect size; R² = .27); and 36% of the variance of the PA subscale was explained (large effect size; R² = .36). The results identified school counselors’ stressor scores both at the individual and organizational levels; intrapersonal coping strategies contributed to feelings of burnout with large effect sizes in the final model. In other words, demographic factors (e.g., more school counseling experience), coping styles (e.g., more emotion-oriented and less task-oriented coping strategies), and organizational variables (e.g., lack of decision-making authority, role ambiguity, role incongruity, and role conflict) positively predicted the level of burnout among school counselors.

Wilkerson and Bellini (2006) used three-step hierarchical regression models including demographic, intrapersonal, and organizational factors to examine the relationship between the variables and burnout among school counselors (N = 78). The school counselors’ demographic data (e.g., age, counseling experience, supervision, and student/counselor ratio), and intrapersonal (i.e., coping strategies) and organizational factors (e.g., role conflict, role ambiguity, and counselor occupational stress) significantly accounted for the variance in their burnout subscale scores on the MBI. Specifically, 45% of the variance in the EE subscale was explained (large effect size; R² = .45), 30% of the variance in the DP subscale was accounted for (large effect size; R² = .30), and 42% of the variance in the PA subscale was explained (large effect size; R² = .42) by the final three-step model with the variables (i.e., counselor demographics, intrapersonal factors, and organizational factors). The findings indicated that school counselors’ emotion-oriented coping style predicted their three MBI subscale scores, supporting the importance of utilizing helpful strategies (i.e., task-oriented coping) to mitigate counselors’ feelings of burnout.

Another study examined how school counselors’ perceived stress and job satisfaction relate to burnout (Mullen et al., 2017). Specifically, perceived stress measured by the Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983) explained 52% of the variance in burnout (F (1, 749) = 808.55, p < .001; R² = .52) and 25% of the variance in job satisfaction (F (1, 749) = 243.36, p < .001; R² = .25). When both perceived stress and burnout were examined in order to test the relationship with job satisfaction, they explained 40% of the variance in job satisfaction (F (2, 747) = 246.48, p < .001; R² = .40). In addition, the results indicated that burnout mediated the relationship between perceived stress and job satisfaction (z = -21.47, p < .001), and burnout (r_s = .99) predicted job satisfaction better than perceived stress (r_s = .79). Overall, perceived stress predicted burnout positively (large effect size) and job satisfaction negatively (large effect size). Both perceived stress and burnout predicted job satisfaction (large effect size).

Finally, Mullen and Crowe (2018) investigated the relationship between grit, burnout, and stress among school counselors (N = 330). The researchers found that grit was negatively correlated with burnout (r = -.22, p < .001) and stress (r = -.28, p < .001). Specifically, perseverance of effort, one of the subscales from the Grit-S (Duckworth & Quinn, 2009), was negatively related with burnout (r = -.12,
p < .05) and stress (r = -.19, p < .001). Therefore, school counselors’ level of grit may be a protective factor for burnout and stress.

Demographic factors. School counselors’ individual factors, such as age (Wilkerson & Bellini, 2006) and years of experience (Butler & Constantine, 2005; Wilkerson, 2009), correlate with feelings of burnout. Age was negatively correlated to the DP subscale (r = -.19, p < .05); therefore, older school counselors were less likely to experience burnout as compared to younger counselors (Wilkerson & Bellini, 2006). Nevertheless, the correlation between school counselors’ years of experience and burnout was inconsistent. Wilkerson and Bellini (2006) indicated that years of experience negatively correlated with the EE (r = -.26, p < .01), and DP (r = -.24, p < .05) subscales, while Butler and Constantine (2005) identified that school counselors with more years of experience scored at higher levels of burnout (MBI scores). Specifically, school counselors with 20–29 years of experience had greater DP subscale scores than those with 0–9 years of experience (F (3, 529) = 3.38, p < .05); and counselors with 30 years or more of experience had lower PA subscale scores than those with less than 20 years of experience (F (3, 529) = 3.39, p < .05). Furthermore, Wilkerson (2009) also reported that the years of experience positively correlated with the EE (ß = .21, p < .01) and DP (ß = .26, p < .01) MBI subscales in the hierarchical regression models whose variables included counselor demographics and organizational and intrapersonal variables to explain the variance of the burnout scores. Possible reasons behind the incongruent results may relate to school counselors’ role ambiguity, as counselors with less experience may experience or perceive large workloads compared to more experienced counselors. The conflicting results also may be related to other school counselor factors, such as the level of social support counselors experience at their schools. The findings identified the need for more inquiry to increase our understanding of the relationship between school counselors’ years of experience and their feelings of burnout.

Organizational Factors Relating to School Counselors Levels of Burnout

Eight organizational factors appear to correlate with school counselors’ levels of burnout, including (a) workplace (Butler & Constantine, 2005), (b) non-counseling duties such as administrative and clerical tasks (Bardhoshi et al., 2014; Moyer, 2011), (c) caseloads (Bardhoshi et al., 2014), (d) AYP (Bardhoshi et al., 2014), (e) level of principal support (Bardhoshi et al., 2014), (f) clinical supervision (Moyer, 2011), (g) student-to-counselor ratio (Wilkerson, 2009; Wilkerson & Bellini, 2006), (h) perceived work environment (Wilkerson & Bellini, 2006), and (i) direct student services (Mullen & Gutierrez, 2016). We categorize these organizational factors into two domains: (a) job responsibilities and
(b) work environment factors.

Job responsibilities. Two studies examined the relationship between school counselors’ non-counseling duties and their burnout scores. First, Bardhoshi and colleagues (2014) examined school counselors’ (N = 212) non-counseling duties and identified a significant relationship between three of the CBI subscales: (a) 11% of the variance in Exhaustion was explained (medium effect size; R² = 0.11); (b) 6% of the variance in NWE was explained (medium effect size; R² = 0.06); and (c) 8% of the variance in DPL was explained (medium effect size; R² = 0.08). Taken together, the results identified that school counselors’ non-counseling duties positively predict their burnout scores.

Moyer (2011) examined how school counselors’ (N = 382) non-counseling duties (non-guidance duties) were correlated to their levels of burnout as measured by the CBI. School counselors’ non-counseling duties accounted for 7.3% of the variance in the burnout score (medium effect size; R² = .073, ß = .27, p < .01). Receiving supervision accounted for additional variance in school counselors’ burnout scores after controlling the variance explained by non-counseling activities (medium effect size; R² = .09, ß = -.14, p < .01). As a result, school counselors with more non-counseling duties and less clinical supervision had higher burnout scores. The findings identify the importance of clinical supervision to reduce burnout among school counselors, helping them improve their quality of counseling, which in turn may increase their sense of competence in the workplace.

Bain and colleagues (2011) investigated the mental health of school counselors in a rural setting and their percentage of workweek spent on counseling and administrative duties in South Texas (N = 27). Within this sample of school counselors, 89% had experienced feelings of burnout at least sometimes when trying to provide mental health services; specifically, 41% reported feelings of burnout, and 48% sometimes experienced burnout when providing mental health services to their students. School counselors also reported that they spent the greatest amount of time completing administrative duties and the least amount of time providing counseling services. About 48% of the counselors used more than 50% of their time completing administrative duties, such as organizing facts to report to administrators and preparing for assessments of knowledge and skills, and more than 70% of the participants spent less than 50% of their time providing counseling services. The sample size for this study was small; nevertheless, the results identified that approximately 90% of the school counselors experienced some levels of burnout and spent less time providing counseling services to their students and other stakeholders than completing administrative duties.

Finally, Mullen and Gutierrez (2016) investigated the relationship between burnout and direct student services of school counselors (N = 926). The results indicated that burnout negatively contributed to the frequency of direct counseling activities (ß = -.35, p < .001), direct curriculum activities (ß = -.22, p < .001), and percentage of time at work providing direct services to students (ß = -.24, p < .001). The findings suggest that school counselors experiencing feelings of burnout are likely to have lower numbers of direct counseling activities and curriculum activities, and spend less time offering direct services to students.

Work environment factors. School counselors’ levels of burnout may be different depending on the location of their workplace (Butler & Constantine, 2005). Specifically, school counselors working in urban settings scored higher on the EE subscale as compared to counselors in suburban, rural, and other settings (F (3, 529) = 24.66, p < .001). In addition, counselors in urban settings had higher DP subscale scores than those in other environmental settings (F (3, 529) = 13.67, p < .001). The results may relate to unique stressors school counselors in the urban settings face, including their expected proficiency in working with diverse students (Constantine et al., 2001). Overall, school counselors in urban settings were likely to experience greater feelings of burnout than those counselors in other settings, suggesting that more research is warranted to better understand possible contributors to these educators having higher MBI scores.

Factors relating to school counselors’ work correlating with their feelings of burnout include counselors’ caseloads, AYP status, principal support, and non-counseling duties. Specifically, school-related factors for counselors explained the variance of four burnout subscales of the CBI (Bardhoshi et al., 2014): (a) 21% of the variance in Exhaustion scores was explained (large effect size; R² = 0.21, p < .001); (b) 9% of the variance in Incompetence scores was explained (medium effect size; R² = 0.09, p < .01); (c) 49% of the variance in NWE scores was explained (large effect size; R² = 0.49, p < .001); and (d) 17% of the variance in DPL scores was explained (large effect size; R² = 0.17, p < .001). As a result, both school counselors’ work-related factors, such as caseloads and non-counseling duties, and their school environment (support from school staff and AYP status) correlate to their feelings of burnout. Therefore, providing sufficient support for school counselors, meeting the AYP, and reducing caseloads and non-counseling duties might mitigate feelings of burnout among school counselors.

Student-to-counselor ratio (Wilkerson, 2009) and perceived work environment (e.g., role conflict; Wilkerson & Bellini, 2006) were identified as predictive factors for school counselor burnout. Wilkerson (2009) found that the hierarchical regression models with variables of demographic data (e.g., years of experience), organizational stressors (e.g., counselor–teacher professional relationships), and coping strategies (e.g., task-oriented coping) explained all three subscale scores of the MBI in a sample of school counselors (N = 198): EE (R² = .49; large effect size), DP (R² = .27; large effect size), and PA (R² = 36; large effect size). Similarly, Wilkerson and Bellini (2006) identified that school counselors’ demographic, intrapersonal, and organizational factors accounted for variance in all three MBI subscale scores, including the EE, DP, and PA subscales (45%, 30%, and 42%, respectively; all large effect sizes). The findings from these studies support that environmental factors relate to school counselor burnout.

Identified Themes From Qualitative Studies

One qualitative study and two mixed-methods studies explored themes relating to school counselor burnout and ways to improve their service, which may offer ways to prevent burnout. Bardhoshi and colleagues (2014) examined how school counselors experienced burnout. Specifically, the emergent themes identified for school counselors’ feelings of burnout organized around four areas including (a) lack of time, (b) budgetary constraints, (c) lack of resources, and (d) lack of organizational support. When school counselors were asked about the meaning of performing non-counseling duties, they stated adverse personal and professional effects, the realities of practice, and reframing the duties within the context of the job. One participant described burnout stating, “It means that I am no longer helpful to my students. I feel like I’m extremely tired and overworked and consequently my effectiveness as a school counselor is negatively impacted” (p. 437).

These themes aligned with existing qualitative research examining school counselors’ feelings of burnout (N = 3; Sheffield & Baker, 2005), including (a) important beliefs, (b) burnout feelings, (c) burnout attitude, and (d) lack of collegial support. One of the participants stated, “I didn’t think I was doing any good for anybody . . . I just can’t go on this way” (p. 181). Another participant stated, “You get to the point where it is no longer fun coming to work or when you are just tired [and] don’t want to deal with anyone” (p. 182). Finally, Bain and colleagues (2011) explored helpful ways to better provide mental health services at school with 27 school counselors in rural districts of South Texas. The results identified that having access to more staff and additional education and awareness of mental health services at their school was needed. Overall, these studies identified common themes of school counselors’ need for collegial support and resources, such as a school climate encouraging collaboration, and identifying gaps in the needs and realities of school counselors (Bardhoshi et al., 2014), as well as reducing the amount of stressful, non-counseling–related work they perform.

Occupational Stress

Researchers examined which factors may influence school counselors’ job stress or job satisfaction, including (a) counselors’ perceived match between job expectations and their actual experiences (Culbreth et al., 2005), (b) the amount of resources in their work environment (McCarthy et al., 2010), (c) mattering to others (Rayle, 2006), and (d) role balance ability (Bryant & Constantine, 2006). Perceived match between initial expectations of the job and actual experiences as a school counselor was the most significant predictor of lower role stress demonstrated by each subscale score of the Role Questionnaire (N = 512; Culbreth et al., 2005): role conflict (medium effect size; R² = .076); role incongruence (large effect size; R² = .197); and role ambiguity (medium effect size; R² = .083). School counseling students reported not feeling trained enough because of the significant amount of non-counseling–related duties, which increased their sense of role conflict.

Graduating from a program accredited by the Council for Accreditation of Counseling and Related Educational Programs accounted for 1.2% of the variance in school counselors’ perceived readiness for the job (small effect size; r = .111, p < .05; Culbreth et al., 2005). School counselors’ balance between job demand and resources was another important factor for their job stress. Moreover, McCarthy and colleagues (2010) identified that perceived job stress and work environment in terms of demands and resources were correlated (N = 227; F (2, 206) = 44.77, p < .001). School counselors with resources, such as other counselors in general or as mentors, and support from administrators scored lower on levels of job stress. The effect size for the difference between the demand and the resourced groups was 1.62 (large effect size), and between the demand and balanced groups was 0.70 (large effect size). In other words, school counselors with more work-related resources were likely to experience lower levels of job stress.

Several factors are related to job satisfaction for school counselors. Rayle (2006) investigated the relationship between school counselors’ (N = 388) mattering to others at work scores and job-related stress scores, and their overall job satisfaction scores. The School Counselor Mattering Survey developed for this study included seven items asking participants to rate their perceived mattering to others, including their students, administrators, and the parents and teachers they worked with. School counselors’ mattering to others at work scores and job-related stress scores explained 35% of the variance in their overall job satisfaction (large effect size; ηp² = .62). Specifically, school counselors’ job satisfaction correlated with mattering to others at work scores (large effect size; r = .44, p < .001) and their job-related stress scores (large effect size; r = -.41, p < .001). In addition, school counselors’ mattering to others scores were negatively associated with their job-related stress scores (r = -.54, p < .001; large effect size). The findings suggest that school counselors’ perceived mattering to others at work and job-related stress predict their overall job satisfaction, and mattering to others at work relates to their job-related stress.

In addition, Bryant and Constantine (2006) investigated the relationship between female school counselors’ (N = 133) role balance, job satisfaction, and life satisfaction. After controlling for demographic information (age, years of school counseling experience, and location of school), role balance and job satisfaction scores correlated with their satisfaction with life scores (large effect size; R² = .41). As a result, school counselors’ multiple role balance ability and job satisfaction scores positively predicted their overall life satisfaction scores. In sum, these findings identified factors related to school counselors’ job satisfaction, including mattering to others at work, job-related stress, and life satisfaction.

Discussion

Because of the dearth of literature examining school counselor burnout or occupational stress, we reviewed 18 investigations based on the inclusion criteria and included articles focusing on the topic that were published between 2000 and 2018 in refereed journals and identified internal and external factors relating to the phenomena. Specific factors were identified relating to school counselor burnout or stress and their environment, including responsibilities not related to counseling, large caseloads, AYP status, and role confusion. The findings suggest the importance of school counselors asserting themselves to focus on mandated tasks (i.e., counseling) in order to experience less burnout. In addition, it is imperative to train school counseling students to understand the reality of practice, such as other job responsibilities and school climates, and inform them on the necessity of counselors advocating for themselves in order to overcome role confusion and avoid large caseloads. Furthermore, several resources were identified to mitigate burnout among school counselors. Clinical supervision from a competent supervisor is essential for school counselors to get support and learn how to intervene with their clients effectively. In addition, peer supervision or consultation from colleagues may benefit school counselors in sharing their difficulties and gaining other professionals’ perspectives (Butler & Constantine, 2005). Task-oriented coping skills which can be learned in the school counseling programs were also related to a reduced level of burnout among school counselors.

Limitations

Our review needs to be interpreted with some caution, as it is limited to the 18 published studies meeting the inclusion criteria. Therefore, additional research investigating school counselor burnout is needed to further our understanding of this significant construct that may influence the services school counselors provide to their stakeholders. In addition, the reviewed studies include methodological limitations (e.g., sample size, self-report data), further supporting the need for increased research examining the construct of burnout in school counseling. Moreover, no research was identified examining interventions to possibly reduce counselor feelings of burnout.

Implications for School Counseling

Although no studies were identified that investigated treatments for school counselor burnout, research from other similar professions may provide insight for developing coping strategies for school counselors addressing their feelings of burnout. Awa, Plaumann, and Walter (2010) reviewed 25 intervention studies for burnout prevention whose participants included employees from diverse occupations. Seventeen out of 25 studies employed person-directed interventions and indicated the positive effects of the interventions, including cognitive behavioral training (Gorter, Eijkman, & Hoogstraten, 2001), psychosocial skill training (Ewers, Bradshaw, McGovern, & Ewers, 2002), and recreational music making (Bittman, Bruhn, Stevens, Westengard, & Umbach, 2003). Two studies used organization-directed interventions, and one of the studies reduced burnout by using cognitive behavioral techniques, management skill training, and social support (Halbesleben, Osburn, & Mumford, 2006). The other six investigations explored the effects of combined (person- and organization-directed) interventions in reducing burnout. The examples of combined interventions to mitigate counselors’ feeling of burnout include professional supervision (Melchior et al., 1996); work schedule reorganization and lectures (Innstrand, Espnes, & Mykletun, 2004); and participatory action research, communication, social support, and coping skills (Le Blanc, Hox, Schaufeli, Taris, & Peeters, 2007). Overall, Awa and colleagues (2010) identified positive impacts of burnout intervention programs, suggesting potential benefits of these treatment programs for school counselors.

In addition, Krasner and colleagues (2009) reported the effectiveness of their continuing medical education program for physicians to reduce burnout, which involves mindfulness, self-awareness, and communication skills. Educating for mindfulness strategies, self-awareness, and communication skills also may be helpful for school counselors. Providing a supportive environment and acknowledging school counselors’ work may help them increase their sense of matter in their workplace. Lacking empirical studies identifying treatment outcomes for burnout in school counselors, research on decreasing the level of school counselor burnout should be examined both deeply and extensively. Furthermore, intervention programs to prevent and intervene with school counselors’ burnout and occupational stress at the individual and organizational levels are warranted. The efforts to prevent burnout may lead to school counselors providing better quality of services, benefitting the counselors and the students they serve.

Our review indicated that school counselors’ responsibilities, such as non-counseling duties and dealing with large caseloads, hindered counselors from maintaining their wellness. Additionally, experiencing role conflict and employing emotion-oriented coping skills increased their feelings of burnout. Therefore, school counselor preparation programs need to incorporate into their curriculum the characteristics of their future work environment that may involve potential risk factors for burnout. Furthermore, developing school counselors’ own strategies and practicing beneficial skills such as task-oriented coping skills may be helpful for them in decreasing their likelihood of experiencing burnout.

Conclusion

Preventing and reducing school counselors’ feelings of burnout is important to ensure counselors’ ability to provide ethical and effective services to their stakeholders. Failure to address work-related stress in school counselors may cause reduced quality of their service and increased counselor attrition from the profession. Although more investigations examining burnout in school counselors are warranted, this manuscript is the first systematic review of burnout in school counseling, offering increased insight into this significant job-related psychological phenomenon.

Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.

References

American Counseling Association. (2014). 2014 ACA code of ethics. Alexandria, VA: Author.

American School Counselor Association. (2015). Student-to-school-counselor ratio 2015–2016. Retrieved from https://www.schoolcounselor.org/asca/media/asca/home/Ratios15-16.pdf

American School Counselor Association. (2016). ASCA ethical standards for school counselors. Alexandria, VA: Author.

Armon, G., Shirom, A., Shapira, I., & Melamed, S. (2008). On the nature of burnout–insomnia relationships: A prospective study of employed adults. Journal of Psychosomatic Research, 65, 5–12.
doi:10.1016/j.jpsychores.2008.01.012

Awa, W. L., Plaumann, M., & Walter, U. (2010). Burnout prevention: A review of intervention programs. Patient Education and Counseling, 78, 184–190. doi:10.1016/j.pec.2009.04.008

Bain, S. F., Rueda, B., Mata-Villarreal, J., & Mundy, M.-A. (2011). Assessing mental health needs of rural schools in South Texas: Counselors’ perspectives. Research in Higher Education Journal, 14, 1–11.

Bardhoshi, G., Schweinle, A., & Duncan, K. (2014). Understanding the impact of school factors on school counselor burnout: A mixed-methods study. The Professional Counselor, 4, 426–443. doi:10.15241/gb.4.5.426

Bettencourt, B. A., & Dorr, N. (1997). Collective self-esteem as a mediator of the relationship between allocentrism and subjective well-being. Personality and Social Psychology Bulletin, 23, 955–964.

Bittman, B., Bruhn, K. T., Stevens, C., Westengard, J., & Umbach, P. O. (2003). Recreational music-making: A cost-effective group interdisciplinary strategy for reducing burnout and improving mood states in long-term care workers. Advances in Mind Body Medicine, 19(3/4), 4–15.

Bryant, R. M., & Constantine, M. G. (2006). Multiple role balance, job satisfaction, and life satisfaction in women school counselors. Professional School Counseling, 9, 265–271.

Burke, R. J., & Richardson, A. M. (2000). Psychological burnout in organizations. In R. T. Golembiewski (Ed.), Handbook of Organizational Behavior (pp. 327–368). New York, NY: Marcel Dekker, Inc.

Butler, S. K., & Constantine, M. G. (2005). Collective self-esteem and burnout in professional school counselors. Professional School Counseling, 9, 55–62.

Cohen, S. (1986). Contrasting the Hassles Scale and the Perceived Stress Scale: Who’s really measuring appraised stress? American Psychologist, 41, 716–718. doi:10.1037/0003-066X.41.6.716

Cohen, S., Kamarck, T., &Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 385–396. doi:10.2307/2136404

Coll, K. M., & Freeman, B. (1997). Role conflict among elementary school counselors: A national comparison with middle and secondary school counselors. Elementary School Guidance & Counseling, 31, 251–261.

Constantine, M. G., Arorash, T. J., Barakett, M. D., Blackmon, S. M., Donnelly, P. C., & Edles, P. A. (2001). School counselor’s universal-diverse orientation and aspects of their multicultural counseling competence. Professional School Counseling, 5, 13–18.

Culbreth, J. R., Scarborough, J. L., Banks-Johnson, A., & Solomon, S. (2005). Role stress among practicing school counselors. Counselor Education and Supervision, 45, 58–71.
doi:10.1002/j.1556-6978.2005.tb00130.x

Decker, P. J., & Borgen, F. H. (1993). Dimensions of work appraisal: Stress, strain, coping, job satisfaction, and negative affectivity. Journal of Counseling Psychology, 40, 470–478.

Ducharme, L. J., Knudsen, H. K., & Roman, P. M. (2007). Emotional exhaustion and turnover intention in human service occupations: The protective role of coworker support. Sociological Spectrum, 28, 81–104.

Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92, 1087–1101.

Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the Short Grit Scale (Grit-S). Journal of Personality Assessment, 91, 166–174. doi:10.1080/00223890802634290

Eisenberg, N., Guthrie, I. K., Murphy, B. C., Shepard, S. A., Cumberland, A., & Carlo, G. (1999). Consistency and development of prosocial dispositions: A longitudinal study. Child Development, 70, 1360–1372.

Ewers, P., Bradshaw, T., McGovern, J., & Ewers, B. (2002). Does training in psychosocial interventions reduce burnout rates in forensic nurses? Journal of Advanced Nursing, 37, 470–476.
doi:10.1046/j.1365-2648.2002.02115.x

Freudenberger, H. J. (1990). Caring for the caregiver: Recognizing and dealing with burnout. In J. Nottingham & H. Nottingham (Eds.), The professional and family caregiver—Dilemmas, rewards, and new directions (pp. 20–27). Americus, GA: Georgia Southwestern State University.

Gnilka, P. B., Karpinski, A. C., & Smith, H. J. (2015). Factor structure of the counselor burnout inventory in a sample of professional school counselors. Measurement and Evaluation in Counseling and Development, 48, 177–191. doi:10.1177/0748175615578758

Gorter, R. C., Eijkman, M. A., & Hoogstraten, J. (2001). A career counseling program for dentists: Effects on burnout. Patient Education and Counseling, 43, 23–30.

Halbesleben, J. R., Osburn, H. K., & Mumford, M. D. (2006). Action research as a burnout intervention: Reducing burnout in the Federal Fire Service. The Journal of Applied Behavioral Science, 42, 244–266. doi:10.1177/0021886305285031

Innstrand, S. T., Espnes, G. A., & Mykletun, R. (2004). Job stress, burnout and job satisfaction: An intervention study for staff working with people with intellectual disabilities. Journal of Applied Research in Intellectual Disabilities, 17, 119–126. doi:10.1111/j.1360-2322.2004.00189.x

Kern, R. M. (1996). Lifestyle questionnaire inventory. In D. Eckstein & L. Baruth (Eds.), The theory and practice of lifestyle assessment (pp. 243–256). Dubuque, IA: Kendall/Hunt.

Krasner, M. S., Epstein, R. M., Beckman, H., Suchman, A. L., Chapman, B., Mooney, C. J., & Quill, T. E. (2009). Association of an educational program in mindful communication with burnout, empathy, and attitudes among primary care physicians. JAMA, 302, 1284–1293.

Lambie, G. W. (2007). The contribution of ego development level to burnout in school counselors: Implications for professional school counseling. Journal of Counseling & Development, 85, 82–88. doi:10.1002/j.1556-6678.2007.tb00447.x

Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York, NY: Springer.

Le Blanc, P. M., Hox, J. J., Schaufeli, W. B., Taris, T. W., & Peeters, M. C. (2007). Take care! The evaluation of a team-based burnout intervention program for oncology care providers. Journal of Applied Psychology, 92, 213–227.

Lee, R. T., & Ashforth, B. E. (1996). A meta-analytic examination of the correlates of the three dimensions of job burnout. Journal of Applied Psychology, 81, 123–133.

Lee, S. M., Baker, C. R., Cho, S. H., Heckathorn, D. E., Holland, M. W., Newgent, R. A., . . . Yu, K. (2007). Development and initial psychometrics of the Counselor Burnout Inventory. Measurement and Evaluation in Counseling and Development, 40, 142–154. doi:10.1080/07481756.2007.11909811

Limberg, D., Lambie, G., & Robinson, E. H. (2016–2017). The contribution of school counselors’ altruism to their degree of burnout. Professional School Counseling, 20, 127–138. doi:10.5330/1096-2409-20.1.127

Loevinger, J. (1976). Ego development. San Francisco, CA: Jossey-Bass.

Luhtanen, R., & Crocker, J. (1992). A collective self-esteem scale: Self-evaluation of one’s social identity. Personality and Social Psychology Bulletin, 18, 302–318. doi:10.1177/0146167292183006

Malach-Pines, A. (2005). The burnout measure, short version. International Journal of Stress Management, 12, 78–88. doi:10.1037/1072-5245.12.1.78

Maslach, C. (2017). Finding solutions to the problem of burnout. Consulting Psychology Journal: Practice and Research, 69, 143–152. doi:10.1037/cpb0000090

Maslach, C., & Goldberg, J. (1998). Prevention of burnout: New perspectives. Applied and Preventive Psychology, 7, 63–74. doi:10.1016/S0962-1849(98)80022-X

Maslach, C., & Jackson, S. E. (1986). The Maslach Burnout Inventory (2nd ed.). Palo Alto, CA: Consulting Psychologists Press.

Maslach, C., & Jackson, S. E. (1996). Maslach Burnout Inventory–Human Service Survey (MBI-HSS). In C. Maslach, S. E. Jackson, & M. P. Leiter (Eds.), Maslach Burnout Inventory Manual (3rd ed., pp. 3–17). Palo Alto, CA: Consulting Psychologists Press.

Maslach, C., Jackson, S. E., & Leiter, M. P. (1996). The Maslach Burnout Inventory manual (3rd ed.). Palo Alto, CA: Consulting Psychologists Press.

Maslach, C., & Leiter, M. P. (2016). Understanding the burnout experience: Recent research and its implications for psychiatry. World Psychiatry, 15, 103–111. doi:10.1002/wps.20311

Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual Review of Psychology, 52, 397–422. doi:10.1146/annurev.psych.52.1.397

McCarthy, C. J., & Lambert, R. G. (2008). Counselor appraisal of resources and demands. Charlotte, NC: Center for Educational Measurement and Evaluation.

McCarthy, C., Van Horn Kerne, V., Calfa, N. A., Lambert, R. G., & Guzmán, M. (2010). An exploration of school counselors’ demands and resources: Relationship to stress, biographic, and caseload characteristics. Professional School Counseling, 13, 146–158.

Melchior, M. E., Phihpsen, H., Abu-Saad, H. H., Halfens, R. J., van de Berg, A. A., & Gassman, P. (1996). The effectiveness of primary nursing on burnout among psychiatric nurses in long-stay settings. Journal of Advanced Nursing, 24, 694–702.

Minnesota House of Representatives. (2003). Adequate yearly progress under the No Child Left Behind Act. House Research. Retrieved from http://www.house.leg.state.mn.us/hrd/pubs/ss/ssayp.pdf

Morse, G., Salyers, M. P., Rollins, A. L., Monroe-DeVita, M., & Pfahler, C. (2012). Burnout in mental health services: A review of the problem and its remediation. Administration and Policy in Mental Health and Mental Health Services Research, 39, 341–352. doi:10.1007/s10488-011-0352-1

Mosak, H. H. (1971). Lifestyle. In A. G. Nikelly (Ed.), Applications of Adlerian theory: Techniques for behavior change. Springfield, IL: Charles C. Thomas.

Mosak, H., & Maniacci, M. (2000). A primer of Adlerian psychology: The analytic–behavioral–cognitive psychology of Alfred Adler. Philadelphia, PA: Brunner/Mazel.

Moyer, M. (2011). Effects of non-guidance activities, supervision, and student-to-counselor ratios on school counselor burnout. Journal of School Counseling, 9(5), n5.

Mullen, P. R., Blount, A. J., Lambie, G. W., & Chae, N. (2017). School counselors’ perceived stress, burnout, and job satisfaction. Professional School Counseling, 21, 1–10. doi:10.1177/2156759X18782468

Mullen, P. R., & Crowe, A. (2018). A psychometric investigation of the short grit scale with a sample of school counselors. Measurement and Evaluation in Counseling and Development, 51, 151–162.
doi:10.1080/07481756.2018.1435194

Mullen, P. R., & Gutierrez, D. (2016). Burnout, stress and direct student services among school counselors. The Professional Counselor, 6, 344–359. doi:10.15241/pm.6.4.344

National Center for Education Statistics. (2016). Documentation to the 2014-15 Common Core of Data. Retrieved from https://nces.ed.gov/ccd/pdf/2016077_Documentation_062916.pdf

Oddie, S., & Ousley, L. (2007). Assessing burn-out and occupational stressors in a medium secure service. The British Journal of Forensic Practice, 9(2), 32–48.

Pines, A., & Aronson, E. (1988). Career burnout: Causes and cures. New York, NY: Free Press.

Rayle, A. D. (2006). Do school counselors matter? Mattering as a moderator between job stress and job satisfaction. Professional School Counseling, 9, 206–215. doi:10.1177/2156759X0500900310

Rushton, J. P., Chrisjohn, R. D., & Fekken, G. C. (1981). The altruistic personality and the self-report altruism scale. Personality and Individual Differences, 2, 293–302.

Sandoval, J. (1989). Review of the Maslach Burnout Inventory, second edition. In J. C. Conoley & J. J. Kramer (Eds.), The Tenth Mental Measurements Yearbook (pp. 475–476). Lincoln, NE: The University of Nebraska Press.

Scarborough, J. L. (2005). The school counselor activity rating scale: An instrument for gathering process data. Professional School Counseling, 8, 274–283.

Sheffield, D. S., & Baker, S. B. (2005). Themes from retrospective interviews of school counselors who experienced burnout. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 29, 177–186.

Sink, C. A., & Stroh, H. R. (2006). Practical significance: The use of effect sizes in school counseling

research. Professional School Counseling, 9, 401–411. doi:10.5330/prsc.9.4.283746k664204023

Stalker, C., & Harvey, C. (2002). Professional burnout: A review of theory, research, and prevention. Brantford, ON, Canada: Wilfrid Laurier University.

Stephan, J. B. (2005). School environment and counselor resources: A predictive model of school counselor burnout (Doctoral dissertation). Retrieved from https://libres.uncg.edu/ir/uncg/f/umi-uncg-1060.pdf

Wachter, C. A. (2006). Crisis in the schools: Crisis, crisis intervention training, and school counselor burnout (Doctoral dissertation). Retrieved from https://libres.uncg.edu/ir/uncg/f/umi-uncg-1190.pdf

Wachter, C. A., Clemens, E. V., & Lewis, T. F. (2008). Exploring school counselor burnout and school counselor involvement of parents and administrators through an Adlerian theoretical framework. Journal of Individual Psychology, 64, 432–449.

Wilkerson, K. (2009). An examination of burnout among school counselors guided by stress-strain-coping theory. Journal of Counseling & Development, 87, 428–437. doi:10.1002/j.1556-6678.2009.tb00127.x

Wilkerson, K., & Bellini, J. (2006). Intrapersonal and organizational factors associated with burnout among school counselors. Journal of Counseling & Development, 84, 440–450.
doi:10.1002/j.1556-6678.2006.tb00428.x

Young, M. E., & Lambie, G. W. (2007). Wellness in school and mental health systems: Organizational influences. The Journal of Humanistic Counseling, Education and Development, 46, 98–113. doi:10.1002/j.2161-1939.2007.tb00028.x

The Role of High School and College Counselors in Supporting the Psychosocial and Emotional Needs of Latinx First-Generation College Students

Nov 30, 2017 | Volume 7 - Issue 4

Angelica M. Tello, Marlise R. Lonn

Latinx first-generation college students (FGCS) are a growing population faced with unique challenges for college retention and graduation. Because their parents did not attend postsecondary education, this group of college students has not inherited the social or cultural capital common to many traditional college freshmen. Both high school and college counselors are in positions to support the psychosocial and emotional needs of Latinx FGCS, which may increase successful college completion rates. This article provides high school and college counselors with (a) an overview of FGCS’ characteristics, (b) information specific to Latinx culture, (c) an understanding of the college experiences of Latinx FGCS, and (d) a discussion of counseling implications for addressing the psychosocial and emotional needs of this population.

Keywords: first-generation college students, school counselors, college counselors, Latinx, retention

Although higher education is now more accessible to students from disadvantaged backgrounds, universities are still struggling with retention and graduation rates of first-generation college students (FGCS; Slaughter, 2009). In higher education, FGCS refers to students whose parents did not attend college or any postsecondary institution (Wang & Castañeda-Sound, 2008). In 2008, 15 million FGCS were enrolled in higher education, and approximately 4.5 million were from low-income backgrounds (The Pell Institute, 2008). Additionally, only 11% of FGCS earn a bachelor’s degree in six years compared to 55% of non-FGCS (The Pell Institute, 2008). Moreover, FGCS are 71% more likely to leave college in their first year than non-FGCS (Pratt, Harwood, Cavazos, & Ditzfeld, 2017). Beyond the general challenges faced by many FGCS, including lack of transmission of cultural capital (e.g., familiarity with the dominant culture; Lundberg, Schreiner, Hovaguimian, & Miler, 2007; Saenz, Hurtado, Barrera, Wolf, & Yeung, 2007), Latinx FGCS experience additional barriers to college completion such as institutional invalidation and microaggressions (Saunders & Serna, 2004; Tello, 2015). Professional counselors working in high school and college settings are in unique positions to engage with FGCS to foster a supportive transition from high school to college to degree completion. The focus of this article is to provide high school and college counselors with (a) an overview of FGCS’ characteristics, (b) information specific to Latinx culture, (c) an understanding of the college experiences of Latinx FGCS, and (d) a discussion of counseling implications for addressing the psychosocial and emotional needs of this population. The term Latinx, a gender neutral term for Latina/o (Castro & Cortez, 2017; Vélez, 2016), is used throughout this article and is used interchangeably with the term Hispanic in the case of information cited from reports (e.g., by the U.S. Department of Education or the Pew Hispanic Center).

First-Generation College Students

Various studies (Lundberg et al., 2007; Prospero & Vohra-Gupta, 2007; Saenz et al., 2007) have highlighted how FGCS differ from the traditional non-FGCS college population. Demographically, FGCS tend to be female ethnic minorities from low socioeconomic families, and older than non-FGCS (Prospero & Vohra-Gupta, 2007). The struggles that FGCS face have been well documented. FGCS are often less academically prepared, often work while attending college, are not as likely to participate in campus extracurricular activities, and have family obligations (Bergerson, 2007; Tym, McMillion, Barone, & Webster, 2004). FGCS also tend to lack the cultural capital that non-FGCS receive from their parents (Lundberg et al., 2007; Saenz et al., 2007). In higher education, cultural capital relates to knowledge and understanding of what it means to be in college. Additionally, this is knowledge that is acquired over a long period of time (Ward, Siegel, & Davenport, 2012). For non-FGCS, parents are the most common source of cultural and social capital regarding ways to navigate academia and college life. The lack of cultural and social capital experienced by FGCS translates to a lack of knowledge about college degrees, persistence, and retention resources. Furthermore, FGCS tend to report not receiving familial support in navigating higher education (Lowery-Hart & Pacheco, 2011; Stieha, 2010). Studies (Orbe, 2004, 2008) have begun to highlight that many FGCS also struggle with negotiating multiple identities. Being an FGCS is not the only identity that these students experience. Other personal identities, such as race, ethnicity, and class, also tend to interplay with FGCS status.

In the research on FGCS, there is a lack of understanding of the intersection of identities experienced by specific FGCS populations. Latinxs are the fastest growing and largest racial group in the United States (Passel, Cohn, & Hugo Lopez, 2011). They also are the fastest growing population accessing higher education (Santiago, Calderón Galdeano, & Taylor, 2015). In 2010, the Pew Hispanic Center reported that Latinxs enrolled in college reached an “all-time high” (Fry, 2011, p. 3). From 2009 to 2010, there was a 24% growth in Latinx college enrollment (Fry, 2011). This represents an increase of 349,000 compared with an increase of 88,000 African Americans and 43,000 Asian Americans (Fry, 2011). Although the gap in college enrollment is beginning to narrow, Latinx continue to be the least educated racial group in regards to bachelor’s degree achievement. In 2010, only 13% of Latinxs completed a bachelor’s degree (Fry, 2011). In 2013–2014, White students earned 68% and Latinx students earned 11% of all bachelor’s degrees awarded (vs. 7% in 2003–2004). While this was a significant increase, Latinxs are still underrepresented in comparison to their percentage of the population (Snyder, de Brey, & Dillow, 2016). In order to provide Latinx FGCS support, high school and college counselors need to begin understanding their experiences, which can aid in increasing their college retention and graduation rates.

There are benefits of having professional school and college counselors working with Latinx FGCS. High school and college counselors can play vital roles in helping to increase the college enrollment and persistence of underrepresented groups in higher education, including low-income students, FGCS, and students of color (Bishop, 2010; McDonough, 2005; McKillip, Rawls, & Barry, 2012). The retention and graduation rates for Latinx FGCS are significantly lower than traditional students’ rates (Slaughter, 2009). Many universities have recognized that students of color are an at-risk group for dropping out prior to graduation (Atherton, 2014). As a result, these universities are trying to find ways to provide the best support for this population. Research on the academic performance and persistence of FGCS has increased, but there are only a few studies that focus on the psychological well-being of these students (Wang & Castañeda-Sound 2008). A deeper understanding of Latinx culture will assist counselors as they consider how to work effectively with this population.

Latinx Culture

Understanding Latinx culture can help high school and college counselors in providing culturally competent services to Latinx FGCS. In Latinx culture, there is an emphasis placed on upholding interpersonal relationships (Hernández, Ramírez Garcia, & Flynn, 2010; Kuhlberg, Peña, & Zayas, 2010). Therefore, many Latinx cultural values revolve around supporting interpersonal relationships. Although many Latinx groups share cultural commonalities, there are between-group and within-group differences (Sue & Sue, 2016). The Latinx cultural values described in this section may vary based on the individual’s generational status (e.g., first-generation in the United States versus third generation or beyond) and level of acculturation. According to Sue and Sue (2016), three-fourths of Latinx in the United States are third-generation Americans or higher. In order to gain an understanding of some of the significant Latinx cultural values, a discussion below is provided on familismo, personalismo, simpático, and fatalismo.

Familismo

Familismo refers to family interdependence, cohesiveness, and loyalty, as well as placing family needs before personal needs (Baumann, Kuhlberg, & Zayas, 2010; Marín & Marín, 1991). For many Latinx, family also encompasses extended family (e.g., grandparents, aunts, uncles, and cousins), close friends, and godparents. The cultural value of familismo involves: “(a) perceived obligation to provide material and emotional support to members of the extended family, (b) reliance on relatives for help and support, and (c) the perception of relatives as behavioral and attitudinal referents” (Marín & Marín, 1991, pp. 13–14). Therefore, extended family and friends will be the first source of support for many Latinx. Seeking help from outside the family might only occur after no resources are provided by extended family and friends (Sue & Sue, 2016). Although familismo may be a source of support for many Latinx, it also can contribute to stress (Aguilera, Garza, & Muñoz, 2010). Family obligations and responsibilities may be placed above outside factors, such as school and work (Avila & Avila, 1995; Franklin & Soto, 2002). However, it is important for high school and college counselors to understand that placing family responsibilities above school does not mean education is not valued by Latinx students and their families. Counselors must tailor their approaches to take into account the client’s cultural expectations for assisting family in times of need.

Personalismo

Personalismo refers to a “personalized communication style that is characterized by interactions that are respectful, interdependent, and cooperative” (Sue & Sue, 2016, p. 534). In addition, a focus is placed on personal interactions in relationships instead of more formal approaches (Holloway, Waldrip, & Ickes, 2009). Counselors may consider attending to rapport building as an essential building block in the first session rather than the more formal interactions associated with completing paperwork and conducting initial assessments. Furthermore, relationships are not viewed as “means to another end” (Clauss-Ehlers, 2006, p. 412); instead, the focus is on privileging a sense of connectedness and warmth over individual achievements or material success. Maintaining positive relationships is central to the Latinx cultural value of personalismo (Clauss-Ehlers, 2006). As a result, high school and college counselors must work on being visible on their campuses and actively engaging with Latinx students.

Simpático

In Latinx culture, simpático is a relational style that “emphasizes the promotion and maintenance of harmonious and smooth interactions” (Holloway et al., 2009, p. 1012). In relationships, a space is created that is personal, hospitable, and courteous (Holloway et al., 2009). Holloway et al. (2009) described simpático as a self-schema where “one attempts (a) to treat other people in a gracious and accepting manner, (b) to think about others as deserving such treatment, and (c) to think about oneself as the kind of person who treats others in that manner” (p. 1013). In a study conducted by Holloway et al., their findings indicated Latinx reported significantly higher simpáctico-related traits than White participants. As a result, Latinx students may not want to bring up problems that are occurring on their campuses. High school and college counselors must work on creating a safe space for Latinx clients to feel comfortable to voice their concerns.

Fatalismo

Fatalismo, also known as fatalism, refers to the belief some Latinx hold related to fate. For Latinx who have traditional cultural values, they may “believe that life’s misfortunes are inevitable and feel resigned to their fate” (Sue & Sue, 2016, p. 532). Additionally, fatalismo is typically connected with religious and spiritual views (Hovey & Morales, 2006; Sue & Sue, 2016). Positive and negative life events can be viewed as controlled by “divine will” (Hovey & Morales, 2006, p. 410). When seeking counseling or mental health services, Latinx with fatalismo cultural values may seem to take a passive approach to problems or may not appear assertive in addressing the problem (Hovey & Morales, 2006; Sue & Sue, 2016). This does not mean the client does not want to address their presenting concern or problem. High school and college counselors will need to tailor their approaches for Latinx clients who hold this cultural belief.

In examining the psychosocial experiences of Latinx FGCS, an understanding of Latinx culture is necessary. Even though there are within-group differences, Latinx college students can sometimes share common cultural values and educational experiences. For many Latinx, supporting interpersonal relationships is an important cultural value (Hernández et al., 2010; Kuhlberg et al., 2010). However, the current literature on Latinx college students brings attention to the cultural incongruence this population experiences in higher education and the negative impact it has on their college persistence (Gloria & Rodriguez, 2000; Hurtado, 1994). In addition, many Latinx college students experience racial tensions on their campus, such as racism and microaggressions, which also negatively impact college retention (Yosso, Smith, Ceja, & Solórzano, 2009).

Factors That Impact the Retention of Latinx FGCS

Latinx college students often face similar challenges as the general FGCS population. They also face barriers in terms of cultural capital, socioeconomic status, and sociocultural experiences (Delgado Gaitan, 2013; Hurtado, Carter, & Spuler, 1996). The existing literature on Latinx college students identified the university environment, social support, and self-beliefs as factors that impacted the retention of Latinx college students (Cerezo & Chang, 2013; Gloria, Castellanos, Lopez, & Rosales, 2005; Hurtado et al., 1996).

University Environment

Several researchers have discussed the impact a university’s environment can have on the persistence of Latinx college students (Gloria et al., 2005; Hurtado & Carter, 1997; Hurtado, Milem, Clayton-Pedersen, & Allen, 1998; Rendón, 1994). Many Latinx college students navigate higher education by balancing their cultural upbringing and the culture of college (Gloria & Rodriguez, 2000; Hurtado, 1994). However, some Latinx students experience a cultural incongruence (i.e., lack of cultural fit between the student and his or her university), and the difficulties that arise can lead to issues in college persistence (Gloria & Rodriguez, 2000; Hurtado, 1994). Recent studies have supported that the cultural congruency of Latinx college students is positively associated with academic achievement and persistence (Cerezo & Chang, 2013; Edman & Brazil, 2009). Latinx students who experience a cultural fit with their university perceive fewer barriers to their education (Gloria, Castellanos, Scull, & Villegas, 2009). According to Hurtado and Carter (1997), Latinx college students attending predominately White universities described that “feeling at ‘home’ in the campus community is associated with maintaining interactions both within and outside the college community” (p. 338). Furthermore, Latinx college students reported experiencing negative stereotypes, prejudices, marginalization, and microaggressions (Gonzales, Blanton, & Williams, 2002; Rodriguez, Guido-DiBrito, Torres, & Talbot, 2000; Valencia, 2002; Yosso et al., 2009).

Microaggressions

Victims of racial and gender microaggressions have identified these as one of the most direct forms of verbal and/or physical assault (Pierce, 1995; Storlie, Moreno, & Portman, 2014). Moreover, microaggressions are more pervasive and occur at a more frequent rate than many realize. While these preconscious or unconscious slights, insults, and degradations may seem harmless or subtle, it is important to be aware that “the cumulative burden of a lifetime of microaggressions can theoretically contribute to diminished mortality, augmented morbidity, and flattened confidence” (Pierce, 1995, p. 281).

Yosso et al. (2009) interviewed 37 Latinx college students attending predominately White institutions that were classified as Carnegie Doctoral/Research Universities-Extensive to understand Latinx students’ experiences of microagressions. Focus groups were completed with three to six students at a time (Yosso et al., 2009). The researchers reported that the Latinx college students in the study experienced three types of microaggressions: (a) interpersonal microaggressions (i.e., verbal and nonverbal racial insults or slights that were directed to the students by faculty, staff, and students), (b) racial jokes, and (c) institutional microaggressions (i.e., a hostile campus climate created by racially marginalized actions through a university’s structure, discourses, and practices toward students of color; Yosso et al., 2009).

The interpersonal microaggressions experienced by the participants included White professors allowing for flexibility in rules with White students but not Latinx students, and Latinx students feeling their professors had low expectations for them or were uncomfortable talking to them (Yosso et al., 2009). For some of the students, racial jokes reduced their sense of belonging and decreased their participation in campus activities (Yosso et al., 2009). In terms of institutional microaggressions, some students felt they were only visible to administrators during culturally related programs on their campuses, but at other times they were neglected by administrators (Yosso et al., 2009). Moreover, the microagressions experienced by the students led them to doubt “their academic merits and capabilities, demean their ethnic identity, and dismiss their cultural knowledge” (Yosso et al., 2009, p. 667). As a result, the students felt rejected by their universities. Yosso et al. (2009) reported that the students engaged in community-building found “counterspaces” on their campuses (student-run spaces such as campus multicultural centers, community outreach programs, or cultural floors in residence halls) where they experienced their cultures as “valuable strengths” (Yosso et al., 2009, p. 677). These findings were similar to those identified in a content analysis of Latinx college student experiences conducted by Storlie et al. (2014).

The Strengths of Latinx FGCS

Researchers have examined the coping strategies and resiliency of Latinx college students (Cavazos, Johnson, Fielding, et al., 2010; Cavazos, Johnson, & Sparrow, 2010). Historically, the literature on Latinx college students focused on the challenges they experienced in higher education (Delgado Gaitan, 2013; Hurtado et al., 1996). However, researchers also can learn from the cultural assets, strengths, and resiliency of Latinx students (Borrero, 2011). Morales (2008) noted that a “deeper understanding of achievement processes can be attained” by examining the experiences of successful Latinx students (p. 25). Latinx FGCS have experienced success as students; they are the first in their families to attend college. Taking a strengths-based approach in evaluating the experiences of Latinx FGCS also aligns with the tenets of the counseling profession (American Counseling Association, 2014).

Coping Strategies

Cavazos, Johnson, and Sparrow (2010) conducted a qualitative study examining the coping responses of high-achieving Latinx college students. The researchers interviewed 11 Latinx college students attending a Hispanic-serving institution. Nine of the participants were low-income FGCS. When faced with barriers and stressors, the Latinxs interviewed in the study reported using the following coping strategies: (a) positive reframing (e.g., staying positive through optimism and self-confidence), (b) acceptance (e.g., challenges were unavoidable and a part of life), (c) positive self-talk, (d) long-term goal setting, (e) gaining motivation from low expectations, (f) self-reflection (e.g., learning from life experiences), (g) taking action, and (h) seeking support (e.g., reaching out to family members and falling back on religious views; Cavazos, Johnson, and Sparrow, 2010). Although Cavazos, Johnson, and Sparrow (2010) did not overtly discuss how Latinx cultural values integrated into the participants’ coping responses, it appears that many of the themes aligned with Latinx culture. For instance, the theme of acceptance had similar characteristics to fatalismo, and seeking support reflected the qualities of familismo.

Resiliency

Cavazos, Johnson, Fielding, et al. (2010) discussed the resiliency of Latinx college students. The researchers built upon the Cavazos, Johnson, and Sparrow (2010) study that examined the coping responses of Latinx students. Cavazos, Johnson, Fielding, et al. (2010) reported that Latinx participants experienced the following resiliency factors: (a) goal setting (e.g., they had clear and specific goals),
(b) interpersonal relationships (e.g., receiving high expectations and encouragement from family),
(c) intrinsic motivation (e.g., pursing majors that would allow them to help others), (d) internal locus of control, and (e) self-efficacy (Cavazos, Johnson, and Sparrow, 2010). Counselors working with Latinx FGCS on the high school or college levels need to be aware of these resiliency factors so they can provide culturally competent support.

Implications for High School and College Counselors

High school and college counselors can play important roles in the college transition and persistence of Latinx FGCS (Adelman, 1999; Avery, 2010; Bishop, 2010; McDonough, 2005; McKillip et al., 2012). Counselors can provide FGCS with college information and support, which is the cultural capital that most FGCS lack. Therefore, an implication for school counselors includes identifying college-bound Latinx FGCS and tailoring college information to these students. Counselors can design interventions at both the individual and school-wide levels to use the strengths inherent in Latinx cultural norms. Counselors may consider leveraging familismo and intentionally design outreach programs and psychoeducation related to college preparation, information, activities, and expectations to include students’ families and friends. Engaging in informal interactions and hosting events in the community (as opposed to within school buildings) may enhance participant comfort with attending events. Topics may include: (a) helping family members have realistic expectations of academia and campus life, (b) addressing the potential of students feeling isolated or stretched between campus and family life, and (c) fostering a college-going mentality by providing information on course rigor, careers, college admission, and the financial aid process.

A similar implication can be directed toward college counselors. It is important for college counselors to have a presence on their campus beyond the counseling center. In particular, they can develop and support initiatives on campus directed toward the psychosocial needs of Latinx FGCS. Thus, college counselors having an increased presence on their campus can help Latinx FGCS understand the support counseling can offer in assisting with college persistence. College counselors can time outreach, interventions, and services to target developmental windows when FGCS’ identity is most salient for students—typically when entering college and when approaching graduation (Orbe, 2004). Additionally, counselors are equipped to provide social and emotional support for negotiating and navigating new and multiple identities and addressing feelings of isolation, both on the college campus and with family. When conceptualizing clients, understanding and framing cultural expressions and values as strengths is critical. For example, fatalismo is reframed from the idea of accepting defeat to moving toward acceptance and using this as a strength that allows the client to move forward in new directions.

Many Latinx students also experience negative stereotypes, prejudices, marginalization, and microaggressions (Gonzales et al., 2002; Rodriguez et al., 2000; Valencia, 2002; Yosso et al., 2009) on their campuses. These experiences may lead many Latinx FGCS to question their sense of belonging on their campuses. High school and college counselors can develop and encourage initiatives supporting diversity on their campuses. Furthermore, high school and college counselors can help Latinx FGCS develop positive coping strategies for dealing with the lack of diversity on their campuses and the internal struggles that arise with their sense of belonging. Counselors should continue to maintain awareness of unconscious bias, engage in accessing diversity and advocacy continuing education, and act as allies. Adopting the habit of framing the unique cultural context of individual Latinx clients as strengths, fostering connections, and identifying culturally applicable adjunct supportive services (e.g., spiritual or religious supports) are within the purview of professional counselors.

The general consensus in college student development theory is that to successfully adjust to college, students need to break from their own culture in order to conform to higher education culture (Nora, 2001; Rendón, 1994). To address this, universities typically provide programming designed to help students adapt to and adopt the existing institutional culture (Rendón, 1994). Alternately, college counselors are in positions that can challenge the privileging of traditional assumptions and values of the academy and influence the recognition and valuing of multiple cultures and ways of being. Rather than requiring students to negotiate overt and covert norms that assume prior knowledge or familiarity with the culture of higher education, counselors can help students identify counterspaces within the institution. For Latinx FGCS, this might include connecting with diverse faculty who could serve as mentors, participating in programs from the multicultural affairs office, or participating in student organizations centered on Latinx culture and identities. Developing relationships with key members of the campus Latinx community and moving access to counseling services outside of the traditional, potentially restrictive environment of the university counseling center may enhance service access and delivery for this underrepresented student population.

Areas for Future Research

Researchers are beginning to examine the concept of cultural wealth (O’Shea, 2016; Yosso, 2005) as it applies to FGCS. Examining Latinx FGCS and the college experience from this lens fits with the strengths-based perspective inherent in counseling and provides an opportunity for professional counselors to reframe their interventions. Further research is warranted on the high school and college experiences of Latinx FGCS. All Latinx cultures tend to be lumped together. Researchers could investigate the experiences of FGCS from an ethnic-specific Latinx group (e.g., Mexicans, Puerto Ricans, or Cubans). Moreover, research could examine the counseling experiences of Latinx FGCS. Examining the counseling experiences of Latinx FGCS can help professional counselors gain a better understanding of their counseling needs. Another possible direction for future research includes examining the microaggressions experienced by Latinx FGCS; future studies need to fully investigate the impact of microaggressions on the college persistence of Latinx FGCS. The findings from these studies can help high school and college counselors understand how they can begin to address the concerns that negatively impact Latinx FGCS.

Conclusion

Latinx FGCS are a growing demographic on college campuses. However, it is clear that these students are not receiving the support needed to assist in their transition from high school to college. The psychosocial and emotional needs of Latinx FGCS are often overlooked in the literature. Latinx students who feel culturally incongruent on their campuses struggle with their sense of belonging (Edman & Brazil, 2009; Hurtado & Carter, 1997). High school and college counselors have the skills to help address the psychosocial and emotional needs of Latinx FGCS. Furthermore, high school and college counselors can work together to share knowledge and bridge the gap between high school and college expectations, institutional culture, and provision of counseling services in ways that would benefit Latinx FGCS.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest or funding contributions for the development of this manuscript.

References

Adelman, C. (1999). Answers in the tool box: Academic intensity, attendance patterns, and bachelor’s degree attainment. Washington, DC: U.S. Department of Education.

Aguilera, A., Garza, M. J., & Muñoz, R. F. (2010). Group cognitive-behavioral therapy for depression in Spanish: Culture-sensitive manualized treatment in practice. Journal of Clinical Psychology, 66, 857–867. doi:10.1002/jclp.20706

American Counseling Association. (2014). 2014 ACA code of ethics. Retrieved from http://www.counseling.org/docs/ethics/2014-aca-code-of-ethics.pdf?sfvrsn=4

Atherton, M. C. (2014). Academic preparedness of first-generation college students: Different perspectives. Journal of College Student Development, 55, 824–829. doi:10.1353/csd.2014.0081

Avery, C. (2010). The effects of college counseling on high-achieving, low-income students. Cambridge, MA: National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w16359.pdf

Avila, D. L., & Avila, A. L. (1995). Mexican-Americans. In N. A. Vacc, S. B. DeVaney, & J. Wittmer (Eds.), Experiencing and counseling multicultural and diverse populations (3rd ed., pp. 119–146). Bristol, PA: Accelerated Development.

Baumann, A. A., Kuhlberg, J. A., & Zayas, L. H. (2010). Familism, mother-daughter mutuality, and suicide attempts of adolescent Latinas. Journal of Family Psychology, 24, 616–624. doi:10.1037/a0020584

Bergerson, A. A. (2007). Exploring the impact of social class on adjustment to college: Anna’s story. International Journal of Qualitative Studies in Education, 20, 99–119. doi:10.1080/09518390600923610

Bishop, J. B. (2010). The counseling center: An undervalued resource in recruitment, retention, and risk management. Journal of College Student Psychotherapy, 24, 248–260. doi:10.1080/87568225.2010.509219

Borrero, N. (2011). Shared success: Voices of first-generation college-bound Latino/as. Multicultural Education, 18(4), 24–30.

Castro, E. L., & Cortez, E. (2017). Exploring the lived experiences and intersectionalities of Mexican community college transfer students: Qualitative insights toward expanding a transfer receptive culture. Community College Journal of Research and Practice, 41(2), 77–92. doi:10.1080/10668926.2016.1158672

Cavazos, J., Jr., Johnson, M. B., Fielding, C., Cavazos, A. G., Castro, V., & Vela, L. (2010). A qualitative study of resilient Latina/o college students. Journal of Latinos and Education, 9(3), 172–188. doi:10.1080/15348431003761166

Cavazos, J., Jr., Johnson, M. B., & Sparrow, G. S. (2010). Overcoming personal and academic challenges: Perspectives from Latina/o college students. Journal of Hispanic Higher Education, 9, 304–316.

Cerezo, A. C., & Chang, T. (2013). Latina/o achievement at predominantly white universities: The importance of culture and ethnic community. Journal of Hispanic Higher Education, 12, 72–85. doi:10.1177/1538192712465626

Clauss-Ehlers, C. S. (2006). Religious/spiritual beliefs: Personalismo. In Y. Jackson (Ed.), Encyclopedia of multicultural psychology (pp. 411–412). Thousand Oaks, CA: Sage.

Delgado Gaitan, C. (2013). Creating a college culture for Latino students: Successful programs, practices, and strategies. Thousand Oaks, CA: Corwin.

Edman, J. L., & Brazil, B. (2009). Perceptions of campus climate, academic efficacy and academic success among community college students: An ethnic comparison. Social Psychology of Education, 12, 371–383. doi:10.1007/s11218-008-9082-y

Franklin, C. G., & Soto, I. (2002). Keeping Hispanic youths in school. Children & Schools, 24, 139–143. doi:10.1093/cs/24.3.139

Fry, R. (2011). Hispanic college enrollment spikes, narrowing gaps with other groups. Washington, DC: Pew Hispanic Center.

Gloria, A. M., Castellanos, J., Lopez, A. G., & Rosales, R. (2005). An examination of academic nonpersistence decisions of Latino undergraduates. Hispanic Journal of Behavioral Sciences, 27, 202–223. doi:10.1177/0739986305275098

Gloria, A. M., Castellanos, J., Scull, N. C., & Villegas, F. J. (2009). Psychological coping and well-being of male Latino undergraduates: Sobreviviendo la universidad. Hispanic Journal of Behavioral Sciences, 31, 317–339.

Gloria, A. M., & Rodriguez, E. R. (2000). Counseling Latino university students: Psychosociocultural issues for consideration. Journal of Counseling & Development, 78, 145–154. doi:10.1002/j.1556-6676.2000.tb02572.x

Gonzales, P. M., Blanton, H., & Williams, K. J. (2002). The effects of stereotype threat and double-minority status on the test performance of Latino women. Personality and Social Psychology Bulletin, 28, 659–670. doi:10.1177/0146167202288010

Hernández, B., Ramírez Garcia, J. I., & Flynn, M. (2010). The role of familism in the relation between parent-child discord and psychological distress among emerging adults of Mexican descent. Journal of Family Psychology, 24(2), 105–114. doi:10.1037/a0019140

Holloway, R. A., Waldrip, A. M., & Ickes, W. (2009). Evidence that a simpático self-schema accounts for differences in the self-concepts and social behavior of Latinos versus Whites (and Blacks). Journal of Personality and Social Psychology, 96, 1012–1028. doi:10.1037/a0013883

Hovey, J. D., & Morales, L. R. (2006). Religious/spiritual beliefs: Fatalismo. In Y. Jackson (Ed.), Encyclopedia of multicultural psychology (pp. 409–410). Thousand Oaks, CA: Sage.

Hurtado, S. (1994). The institutional climate for talented Latino students. Research in Higher Education, 35, 21–41.

Hurtado, S., & Carter, D. F. (1997). Effects of college transition and perceptions of the campus racial climate on Latino college students’ sense of belonging. Sociology of Education, 70, 324–345.

Hurtado, S., Carter, D. F., & Spuler, A. (1996). Latino student transition to college: Assessing difficulties and factors in successful college adjustment. Research in Higher Education, 37, 135–157.

Hurtado, S., Milem, J. F., Clayton-Pederson, A. R., & Allen, W. R. (1998). Enhancing campus climates for racial/ ethnic diversity: Educational policy and practice. The Review of Higher Education, 21, 279–302. doi:10.1353/rhe.1998.0003

Kuhlberg, J. A., Peña, J. B., & Zayas, L. H. (2010). Familism, parent-adolescent conflict, self-esteem, internalizing behaviors, and suicide attempts among adolescent Latinas. Child Psychiatry & Human Development, 41, 425–440. doi:10.1007/s10578-010-0179-0

Lowery-Hart, R., & Pacheco, G., Jr. (2011). Understanding the first-generation student experience in higher education through a relational dialectic perspective. New Directions for Teaching & Learning, 2011(127), 55–68. doi:10.1002/tl.457

Lundberg, C. A., Schreiner, L. A., Hovaguimian, K., & Miller, S. S. (2007). First generation status and student race/ethnicity as distinct predictors of student involvement and learning. NASPA Journal, 44, 57–83.

Marín, G., & Marín, B. V. (1991). Research with Hispanic populations: Applied social science research methods series volume 23. Thousand Oaks, CA: Sage Publications.

McDonough, P. M. (2005). Counseling and college counseling in America’s high schools. Alexandria, VA: National Association for College Admission Counseling. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.543.5670&rep=rep1&type=pdf

McKillip, M. E. M., Rawls, A., & Barry, C. (2012). Improving college access: A review of research on the role of high school counselors. Professional School Counseling, 16, 49–58.

Morales, E. E. (2008). Academic resilience in retrospect: Following up a decade later. Journal of Hispanic Higher Education, 7, 228–248. doi:10.1177/1538192708317119

Nora, A. (2001). The depiction of significant others in Tinto’s ‘‘Rites of Passage’’: A reconceptualization of the influence of family and community in the persistence process. Journal of College Student Retention: Research, Theory & Practice, 3, 41–56. doi:10.2190/BYT5-9F05-7F6M-5YCM

Orbe, M. P. (2004). Negotiating multiple identities within multiple frames: An analysis of first-generation college students. Communication Education, 53, 131–149. doi:10.1080/03634520410001682401

Orbe, M. P. (2008). Theorizing multidimensional identity negotiation: Reflections on the lived experiences of first-generation college students. New Directions for Child and Adolescent Development, 2008(120), 81–95. doi:10.1002/cd.217

O’Shea, S. (2016). Avoiding the manufacture of ‘sameness’: First-in-family students, cultural capital and the higher education environment. Higher Education, 72, 59–78. doi:10.1007/s10734-015-9938-y

Passel, J. S., Cohn, D., & Hugo Lopez, M. (2011). Hispanics account for more than half of nation’s growth in past decade: Census 2010: 50 million Latinos. Washington, DC: Pew Hispanic Center.

The Pell Institute. (2008). Moving beyond access: College success for low-income first-generation students. Retrieved from https://files.eric.ed.gov/fulltext/ED504448.pdf

Pierce, C. M. (1995). Stress analogs of racism and sexism: Terrorism, torture, and disaster. In C. V. Willie, P. P. Rieker, B. M. Kramer, & B. S. Brown (Eds.), Mental health, racism, and sexism (pp. 277–293). Pittsburgh, PA: University of Pittsburgh Press.

Pratt, I. S., Harwood, H. B., Cavazos, J. T., & Ditzfeld, C. P. (2017). Should I stay or should I go? Retention of first-generation college students. Journal of College Student Retention: Research, Theory & Practice, 36, 1–14. doi:10.1177/1521025117690868

Prospero, M., & Vohra-Gupta, S. (2007). First generation college students: Motivation, integration, and academic achievement. Community College Journal of Research and Practice, 31, 963–975. doi:10.1080/10668920600902051

Rendón, L. I. (1994). Validating culturally diverse students: Toward a new model of learning and student development. Innovative Higher Education, 19, 33–51.

Rodriguez, A. L., Guido-DiBrito, F., Torres, V., & Talbot, D. (2000). Latina college students: Issues and challenges for the 21st century. NASPA Journal, 37, 511–527. doi:10.2202/1949-6605.1111

Saenz, V. B., Hurtado, S., Barrera, D., Wolf, D. S., & Yeung, F. P. (2007). First in my family: A profile of first-generation college students at four-year institutions since 1971: The Foundation for Independent Education. Retrieved from https://heri.ucla.edu/PDFs/resSummary051807-FirstGen.pdf

Santiago, D. A., Calderón Galdeano, E., & Taylor, M. (2015). Factbook 2015: The condition of Latinos in education. Retrieved from http://www.edexcelencia.org/research/2015-factbook

Saunders, M., & Serna, I. (2004). Making college happen: The college experiences of first-generation Latino students. Journal of Hispanic Higher Education, 3, 146–163. doi:10.1177/1538192703262515

Slaughter, J. B. (2009). It’s time to get angry about underserved students. Chronicle of Higher Education, 55(20), A68.

Snyder, T. D., de Brey, C., & Dillow, S. A. (2016). Digest of education statistics 2015 (NCES 2016-014). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC. Retrieved from https://nces.ed.gov/pubs2016/2016014.pdf

Stieha, V. (2010). Expectations and experiences: The voice of a first-generation first-year college student and the question of student persistence. International Journal of Qualitative Studies in Education, 23, 237–249. doi:10.1080/09518390903362342

Storlie, C. A., Moreno, L. S., & Portman, T. A. A. (2014). Voices of Hispanic college students: A content analysis of qualitative research within the Hispanic Journal of Behavioral Sciences. Hispanic Journal of Behavioral Sciences, 36, 64–78. doi:10.1177/0739986313510283

Sue, D. W., & Sue, D. (2016). Counseling the culturally diverse: Theory and practice (7th ed.). Hoboken, NJ: John Wiley & Sons, Inc.

Tello, A. M. (2015). The psychosocial experiences of Latina first-generation college graduates who received financial and cultural capital support: A constructivist grounded theory (Doctoral dissertation). Retrieved from ProQuest. (3702397)

Tym, C., McMillion, R., Barone, S., & Webster, J. (2004). First generation college students: A literature review. Austin, TX: Research and Analytical Services. Retrieved from https://www.tgslc.org/pdf/first_generation.pdf

Valencia, R. R. (2002). Mexicans don’t value education!: On the basis of the myth, mythmaking, and debunking. Journal of Latinos and Education, 1(2), 81–103. doi:10.1207/S1532771XJLE0102_2

Vélez, V. N. (2016). Organizing for change: Latinx im/migrant parents, school decision-making, and the racial politics of parent leadership in social reform. Association of Mexican American Educators Journal, 10(3), 108–125. Retrieved from https://eric.ed.gov/?id=EJ1124412

Wang, C.-C. D. C., & Castañeda-Sound, C. (2008). The role of generational status, self-esteem, academic self-efficacy, and perceived social support in college students’ psychological well-being. Journal of College Counseling, 11(2), 101–118.

Ward, L., Siegel, M. J., & Davenport, Z. (2012). First-generation college students: Understanding and improving the experience from recruitment to commencement. San Francisco, CA: Jossey-Bass.

Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race Ethnicity and Education, 8, 69–91. doi:10.1080/1361332052000341006

Yosso, T. J., Smith, W. A., Ceja, M., & Solórzano, D. G. (2009). Critical race theory, racial microaggressions, and
campus racial climate for Latina/o undergraduates. Harvard Educational Review, 79, 659–690.

Angelica M. Tello, NCC, is an assistant professor at the University of Houston-Clear Lake. Marlise R. Lonn, NCC, is an assistant professor at Bowling Green State University. Correspondence can be addressed to Angelica Tello, 2700 Bay Area Blvd., Houston, TX 77058-1002, tello@uhcl.edu.

Career Counseling in Middle Schools: A Study of School Counselor Self-Efficacy

Aug 27, 2017 | Volume 7 - Issue 3

Carrie Sanders, Laura E. Welfare, Steve Culver

Students in K–12 schools benefit from career counseling as a means to improve their readiness for academic and career success. This quantitative study explored the career counseling self-efficacy of 143 practicing middle school counselors using the Career Counseling Self-Efficacy Scale-Modified and a subscale of the School Counselor Self-Efficacy Scale. Although school counselors were confident overall, evidence of specific areas of concern and limited time for career counseling was found. Results related to the importance of prior teaching experience in relation to career counseling self-efficacy also were highlighted. Implications for school counselors and policymakers include examining the amount of time school counselors spend on providing career counseling in comparison to time spent on non-counseling–related duties.

Keywords: career counseling, middle schools, school counselors, self-efficacy, time

All students in K–12 do not have the same exposure to career opportunities. Providing avenues for students to learn about and identify ways to access a variety of careers is the responsibility of counselors in the school setting. School counselors contribute to students’ development in the domains of academic, career, and social and emotional development through comprehensive school counseling programs (American School Counselor Association [ASCA], 2014). ASCA published ASCA Mindsets and Behaviors for Student Success: K–12 College and Career Readiness Standards for Every Student (2014), which offers a framework of desired mindsets and behaviors for college and career readiness. This resource and others highlight the importance of a school counselor’s work in the career domain. However, school counselors’ knowledge and self-efficacy in the career counseling field may impact their ability to be effective in this aspect of their work (O’Brien, Heppner, Flores, & Bikos, 1997; Perrone, Perrone, Chan, & Thomas, 2000). This quantitative study explored the career counseling self-efficacy of practicing middle school counselors. As students move through elementary and secondary school, they continuously learn valuable knowledge and skills to explore postsecondary options and prepare to enter into the world of work. Middle school is an important time in this continuum for students as they consider their future academic and career plans and identify pathways to achieve their goals. The results of this study, as well as results related to the amount of time middle school counselors spend providing career counseling, yielded valuable implications for school counselors, K–12 stakeholders, and counselor educators.

The Importance of Career Counseling

Students begin to develop career awareness in elementary school, explore careers during middle school, and move into career preparation and planning in high school. Career counseling connects the experiences students have in school to their future, which enhances academic motivation and provides meaning to and purpose for the work they are doing in school (Curry, Belser, & Binns, 2013; Scheel & Gonzalez, 2007). As children and adolescents learn about themselves and the world of work, they are more likely to make informed career decisions, value school, succeed academically, and engage in school offerings (Kenny, Blustein, Haase, Jackson, & Perry, 2006; Orthner, Jones-Sanpei, Akos, & Rose, 2013; Perry, Liu, & Pabian, 2010).

Career counseling is needed in middle school in order to inspire young adolescents to make preliminary career decisions, to prepare them to take desired high school classes, and to equip them for future career pathways (Akos, 2004; Osborn & Reardon, 2006). Curriculum that integrates postsecondary college and career options in middle school has the potential to provide support and motivation for students (Curry et al., 2013). This type of curriculum connects directly to the comprehensive school counseling program. In schools with fully implemented comprehensive counseling programs that include career counseling, students self-reported higher grades, perceived they are better prepared for the future, recognized the relevance of school, and experienced a sense of belonging and safety, more so than in schools with less comprehensive school counseling programs (Lapan, Gysbers, & Petroski, 2001; Lapan, Gysbers, & Sun, 1997). In summary, establishing connections between a student’s academic preparation and possible career options benefits students in various ways, and school counselors are essential guides in the career exploration process.

Career Counseling in Schools

Despite this empirical evidence of its importance (Anctil, Smith, Schenck, & Dahir, 2012; Barker & Satcher, 2000; Osborn & Baggerly, 2004), school counselors can face barriers to implementing career counseling, including limited time because of competing demands, negative perceptions about career counseling, and low school counselor self-efficacy. For example, school counselors are often called upon to perform non-counseling tasks that take time away from providing a comprehensive school counseling program. School counselors desire to be engaged in promoting positive student outcomes and would prefer to spend less time on non-counseling–related activities (Orthner et al., 2013; Scarborough & Culbreth, 2008). There is some evidence that the desire to spend more time on counseling applies directly to career counseling, as found in a study of school counselors at all levels (Osborn & Baggerly, 2004). But, other studies have found that some school counselors are uncertain about the importance of career counseling (Perrone et al., 2000). These findings may indicate that although there is a desire to spend more time providing career counseling, there is uncertainty about its value.

Another potential barrier that is a focus of this study is individual school counselor self-efficacy. Self-efficacy, a core construct in this study, centers on the belief one has in his or her ability to perform a task (Bandura, 1986, 1997; Eccles & Wigfield, 2002). Self-efficacy of school counselors would be defined as beliefs about their abilities to provide effective counseling services (Larson & Daniels, 1998). High self-efficacy among school counselors would promote adaptive delivery of school counseling services to meet the needs of diverse student populations (Bodenhorn & Skaggs, 2005; Larson & Daniels, 1998). Social cognitive career theory (Lent & Brown, 2006; Lent, Brown, & Hackett, 2000) offers a framework for understanding self-efficacy in action—that is, how it impacts the interactions between individuals, their behaviors, and their environments. O’Brien and Heppner (1996) explored social cognitive career theory as it applies to interest, engagement, and performance of career counseling.

The interaction between people, their behavior, and their environment provides a highly dynamic relationship. Performance in educational activities is the result of ability, self-efficacy beliefs, outcome expectations, and established goals. School counselors have varied training experiences and personal self-efficacy beliefs that impact the delivery of a career counseling program. A school counselor’s self-efficacy in career counseling can increase through four primary sources: personal performance, vicarious learning, social persuasion, and physiological and affective states (Bandura, 1997). School counselor self-efficacy may be influenced by many things such as graduate training, service learning, internships, professional development, and years of experience (Barbee, Scherer, & Combs, 2003; Lent, Hill, & Hoffman, 2003; O’Brien et al., 1997). Teaching is a related experience that may impact career counseling self-efficacy. Some authors have highlighted prior teaching experience as helpful in the preparation of school counselors; others have not found such evidence (Baker, 1994; Peterson & Deuschle, 2006; Smith, Crutchfield, & Culbreth, 2001). Skills school counselors use to provide classroom guidance, which is one delivery method for career counseling services, are similar skills to those used by effective teachers (Akos, Cockman, & Strickland, 2007; Bringman & Lee, 2008; Peterson & Deuschle, 2006), so it is reasonable to expect that school counselors without teaching experience may be less comfortable managing a classroom of students than those with teaching experience (Geltner & Clark, 2005; Peterson & Deuschle, 2006).

There are two studies that have explored self-efficacy of school counselors with and without prior teaching experience. Scoles (2011) compared self-efficacy of 129 school counselors serving across all grade levels and did not find a statistically significant difference between those with and without teaching experience. In contrast, Bodenhorn and Skaggs (2005) found that respondents with teaching experience (n = 183) reported significantly stronger self-efficacy than those without teaching experience (n = 42). These conflicting findings about the importance of prior teaching experience suggest that further study is warranted.

Purpose for the Study

Given the importance of beginning career exploration early and the essential role school counselors play in that process, this study focused on career counseling in the middle school setting. Understanding practicing school counselors’ self-efficacy and their time spent providing career counseling will help administrators and policymakers better understand ways to increase career counseling in middle schools. As such, the following research questions were posed: (1) What are middle school counselors’ levels of self-efficacy in career counseling? (2) How does middle school counselor self-efficacy in career counseling vary with previous K–12 teaching experience? and (3) What is the relationship between middle school counselor self-efficacy in career counseling and the amount of time spent providing career counseling?

Method

A quantitative research design was used for this study. The researcher examined school counselor self-efficacy in the career counseling domain. A school counselor was invited to participate if he or she was a current middle school (sixth, seventh, or eighth grade) counselor in Virginia at the time of the study and his or her email information was provided on a district or school website. The electronic survey included three instruments: an information questionnaire that was used to collect data about personal experiences and training, the Career Counseling Self-Efficacy Scale-Modified (CCSES-Modified; O’Brien et al., 1997), and a subscale of the School Counselor Self-Efficacy Scale (SCSE-Subscale; Bodenhorn & Skaggs, 2005).

Descriptive statistics were compiled by computing means, standard deviations, and minimum and maximum scores for total career counseling self-efficacy, as identified by both the CCSES-Modified and the SCSE-Subscale independently. Means and standard deviations of the 25 items of the CCSES-Modified and the seven items of the SCSE-Subscale also were calculated.

Two analyses of variance (ANOVA) and a t-test were used to determine if there were statistically significant differences among means. Participants were given the opportunity to report their years of counseling experience both full- and part-time, and the researcher combined these to get a total number. This number was obtained by taking the total reported number of years as a full-time school counselor and adding that to .5 multiplied by the reported number of years as a part-time school counselor. Then, the researcher created discrete levels to represent groups of experience once the data had been collected in order to conduct the analysis. Identifying the range of experience of the sample and using a scale appropriate for the sample determined the discrete levels. These three levels represented those who had the least experience, those in the middle, and those with the most experience as a school counselor. The researcher conducted an ANOVA with these groups and the SCSE-Subscale mean and a separate ANOVA with the identified groups and the CCSES-Modified mean.

The researcher obtained an answer of “yes” or “no” to indicate previous teaching experience. A separate value was given to answers of “yes” and “no” and the values were used to run a t-test with the mean for the SCSE-Subscale and the CCSES-Modified mean.

Participants indicated the total number of hours of conference presentations, workshops, or trainings that focused primarily on career counseling within the last 3 years. First, the researcher identified the range of the number of hours of training participants reported receiving in career counseling within the last 3 years. Then, the researcher created discrete levels to represent groups of recent training once the data was collected in order to conduct the analysis.

The third research question required a correlation to analyze the relationship between school counselor self-efficacy in career counseling and the amount of time (measured in percent) spent providing career counseling.

Participants

The participants for this study were practicing middle school counselors, defined as counselors working in a school housing students in grades 6 through 8 at the time the survey was completed. The data cleaning procedures described below resulted in 143 participants out of 567 invitations, which is a 25% response rate. Of the 143 participants, 23 (16.1%) were male and 117 (81.8%) were female (three participants omitted this item). Regarding race, 110 participants (76.9%) identified as White/Caucasian, 20 (14.0%) as African American, four (2.8%) as Hispanic/Latino, and one (0.7%) as Multiracial, while five (3.5%) preferred not to answer and three participants omitted this item. Participants’ ages ranged from 25 to over 65 years with an average age of 45 years (SD = 11; respondents who reported being 65 and over were coded as 65).

Regarding training, the participants reported their highest level of education: 125 participants (87.4%) reported having a master’s degree as their highest level of education, 11 (7.7%) had an education specialist degree, six (4.2%) reported having a doctoral degree, and one participant omitted this item. Participants reported a mean of 13.3 years (SD = 7.4) of experience providing school counseling. Regarding full-time teaching experience in a K–12 school, 47 (32.9%) participants had experience, while 94 (65.7%) did not have this experience, and two people omitted this item.

Instruments

The 49-item online survey included 17 items to gather demographic and professional information, the 25-item CCSES-Modified (O’Brien et al., 1997), and seven items from the Career and Academic Development subscale of the SCSE (Bodenhorn & Skaggs, 2005).

Career Counseling Self-Efficacy Scale-Modified. The CCSES-Modified (O’Brien et al., 1997) was used to assess overall career counseling self-efficacy. Participants were asked to indicate their level of confidence in their ability to provide career counseling. For this study, the terms “client” and “career client” were replaced with the term “student” to be more congruent with school counselor terminology. Permission was granted from the first author of the scale to the researcher to make these changes (K. O’Brien, personal communication, January 7, 2013). The CCSES-Modified contains 25 items that are rated on a 5-point Likert-type scale (0 = Not Confident, 4 = Highly Confident). Within the CCSES-Modified, there are four subscales: Therapeutic Process and Alliance Skills, Vocational Assessment and Interpretation Skills, Multicultural Competency Skills, and Current Trends in the

World of Work, Ethics, and Career Research. The full scale has a reported internal consistency reliability coefficient of .96 (O’Brien et al., 1997).

School Counselor Self-Efficacy Scale-Subscale. One subscale from the SCSE (Bodenhorn & Skaggs, 2005) was included in this study. The SCSE Career and Academic Development subscale was designed for school counselors to examine self-efficacy in the career domain. Using a 5-point Likert-type scale (1 = Not Confident, 5 = Highly Confident), participants indicated their level of confidence on each of the seven items. Bodenhorn and Skaggs (2005) reported a subscale internal consistency reliability coefficient of .85.

Indices of Reliability in the Present Study

The internal consistency reliability in this sample for the CCSES-Modified was α = 0.941 and the SCSE-Subscale was α = 0.871. The CCSES-Modified had four subscales: Therapeutic Process and Alliance Skills (10 items, α = 0.820), Vocational Assessment and Interpretation skills (6 items, α = 0.855), Multicultural Competency Skills (6 items, α = 0.913), and Current Trends in the World of Work, Ethics, and Career Research (3 items, α = 0.747). All of these exceed the common threshold for reliability for similar measures. The CCSES-Modified total score and the SCSE-Subscale score had a strong positive 2-tailed Pearson correlation (0.792), which was statistically significant at the 0.01 level. This strong positive relationship suggests these two measures captured related information from the participants.

Procedure

The original sampling frame consisted of 576 middle school counselors with publicly available email addresses, which were collected from public school websites in all counties in Virginia. After Institutional Review Board approval was secured, participants were sent an email invitation with the informed consent and link to the web survey. One week later, participants were sent a reminder email. Upon completion of the survey, participants were given the opportunity to vote for one of five organizations to receive a $100 donation as a token of appreciation for their time completing the survey. After the recruitment email was sent, there were nine people who indicated they were not eligible to participate. These included three individuals who sent a return email indicating that they were out of the office during the survey administration, three who were not currently middle school counselors, two who reported needing school division approval, and one person who had difficulty accessing the survey. This reduced the actual sampling frame to 567.

Data Cleaning

One hundred and sixty-one respondents answered the survey items. There were 18 respondents who omitted 15% or more of the items from the CCSES-Modified or the SCSE-Subscale and were therefore removed from the study. This changed the total number of remaining respondents to 143. Of the 143 remaining, there were eight respondents who each omitted one item that was used to measure career counseling self-efficacy on the CCSES-Modified or the SCSE-Subscale. Each omitted item was replaced with the individual’s scale mean (e.g., mean imputation; Montiel-Overall, 2006), and those respondents were included in the analyses. When the omitted item was part of an analysis for Research Question 2 or 3, the respondent was removed from the affected analysis. Omissions on the demographic questionnaire are noted above in the description of the participants.

Results

RQ1: What are school counselors’ levels of self-efficacy in career counseling?

Overall, middle school counselors who participated in this study were moderately confident, confident, or highly confident in their ability to provide career counseling services. According to the CCSES-Modified, counselors felt least confident in the subscales of Multicultural Competency Skills and Current Trends in the World of Work, Ethics, and Career Research, while they reported the most confidence in their Therapeutic Process and Alliance Skills. Specific areas of school counselor self-efficacy deficits were related to special issues present for lesbian, gay, and bisexual students in the workplace and in career decision-making, as well as special issues related to gender and ethnicity in the workplace and in career decision-making. Table 1 provides descriptive statistics and reliability for each subscale and the total scale.

Table 1 Career Counseling Self-Efficacy Scale-Modified Subscale Scores (N = 143)
Subscales	Min	Max	M	SD	α	Item M	Item SD
Therapeutic Process andAlliance Skills(10 items)	21	40	35.24	4.05	0.82	3.52	0.40
Vocational Assessment andInterpretation Skills(6 items)	5	24	18.08	4.21	0.86	3.01	0.70
Multicultural Competency Skills(6 items)	0	24	16.52	4.79	0.91	2.75	0.80
Current Trends in the World of Work,Ethics, and Career Research(3 items)	3	12	8.09	2.44	0.75	2.69	0.81
Total ScaleTotal Instrument Score (25 items)	32	99	77.94	13.60	0.94	3.12	0.54

Note. 1 = Not Confident and 4 = Highly Confident.

The means and standard deviations for the SCSE-Subscale are listed in Table 2. On average, participants were confident or highly confident in their abilities to attend to student career and academic development.

Table 2
School Counselor Self-Efficacy Scale-Subscale Individual Item Responses (N = 143)
	% Response
	1	2		3	4	5	M	SD
1. Implement a program which enables all students to make informed career decisions.	1		3	20	34	43	4.16	.89
2. Deliver age-appropriate programs through which students acquire the skills needed to investigate the world of work.	—		2	18	34	46	4.24	.81
3. Foster understanding of the relationship between learning and work.	—		0	9	40	51	4.42	.65
4. Teach students to apply problem-solving skills toward their academic, personal, and career success.	—		1	8	36	55	4.45	.69
5. Teach students how to apply time and task management skills.	—		2	6	35	57	4.46	.71
6. Offer appropriate explanations to students, parents, and teachers of how learning styles affect school performance.	—		2	15	39	44	4.24	.79
7. Use technology designed to support student successes and progress through the educational system.	—		6	22	44	29	3.96	.86
Total Subscale Score							29.93	4.08

Note. 1 = Not Confident, 3 = Moderately Confident, 5 = Highly Confident.

RQ2: How does school counselor self-efficacy in career counseling vary with previous K–12 teaching experience?

Two t-tests were conducted to identify if there was a difference between career counseling self-efficacy among participants with and without previous experience as a teacher. Separate means and standard deviations were calculated for the two groups—those who had teaching experience (n = 47) scored higher on the CCSES-Modified (M = 82.2, SD = 9.7) and the SCSE-Subscale (M = 30.9, SD = 3.4) than those without teaching experience (n = 94), CCSES-Modified (M = 75.8, SD = 14.7) and SCSE-Subscale (M = 29.4, SD = 4.3).

Independent t-tests were performed to determine if the differences between the groups were statistically significant. For the CCSES-Modified, the assumption of homogeneous variances was not satisfied (Levene’s test, F = 7.13, p < .05); therefore, the more conservative t-test was used to assess for a statistically significant difference (t = -3.06, p = .003). The mean score for the teaching experience group (M = 82.2, SD = 9.7) was statistically higher than the mean score for those without teaching experience (M = 75.8, SD = 14.7). For the SCSE-Subscale, the assumption of homogeneous variances was satisfied (Levene’s test, F = 3.71, p = .055, d = .51). The mean score of the group with teaching experience (M = 30.9, SD = 3.4, d = .39) was statistically different from the mean score of the group without teaching experience (M = 29.4, SD = 4.3), t = -2.03, p = .045. Cohen’s d is a valuable index of effect size for statistically significant mean differences (Cohen, 1988). The Cohen’s d of .51 for the CCSES-Modified and .39 for SCSE-Subscale both represent medium effect sizes.

RQ3: What is the relationship between middle school counselor self-efficacy in career counseling and the amount of time spent providing career counseling?

The third research question required a correlation to analyze the relationship between school counselor self-efficacy in career counseling and the percent of work time spent providing career counseling. Participants reported the percentage of time they spend providing responsive services to students in the three school counseling domains, as well as testing coordination and other non-counseling–related activities, which is represented in Table 3. The averages and standard deviations of the percentage of time spent in each subscale were: personal/social counseling (M = 36.25, SD = 15.39), academic counseling (M = 23.32, SD = 10.47), career counseling (M = 12.15, SD = 6.98), Virginia State Standards of Learning (SOL) testing coordination (M = 11.83, SD = 12.88), and other non-counseling–related activities (M = 16.44, SD = 12.55). One participant omitted this item; therefore, N = 142 in Table 3. There was no statistically significant relationship between the CCSES-Modified and time providing career counseling (r = .160, p = .057) and a statistically significant weak positive relationship (r = .286, p = .001) between the SCSE-Subscale and time providing career counseling.

Table 3 Self-Efficacy and Time Providing Career Counseling
		% Career Counseling
Career Counseling Self-Efficacy Scale-Modified	Pearson Correlation	.160
	Sig. (2-tailed)	.057
	N	142
School Counselor Self-Efficacy Scale-Subscale	Pearson Correlation	.286^*
	Sig. (2-tailed)	.001
	N	142

Note. *Correlation is significant at the 0.01 level (2-tailed)

Discussion

There were several key findings from this study of middle school counselors’ self-efficacy with career counseling. First, it is important to note that there was a wide range in the total self-efficacy scores for middle school counselors. As a group, these counselors were the most confident in their Therapeutic Process and Alliance Skills, and least confident in Multicultural Competency Skills and Current Trends in the World of Work, Ethics, and Career Research. Specifically, special issues related to gender, ethnicity, and sexual orientation in career decision-making and in the workplace were areas of concern. School counselors who had previous K–12 teaching experience were significantly more confident providing career counseling than those without, as assessed by both measures. Finally, a Pearson correlation indicated there was a weak positive correlation between the SCSE-Subscale and the percentage of time school counselors indicated they spend providing career counseling. There was not a statistically significant relationship between the CCSES-Modified and time spent providing career counseling.

In this study, results indicate that middle school counselors spend more time doing non-counseling–related activities than providing career counseling, which is alarming. Career development is one of the three primary domains of a comprehensive school counseling program, and it is important for school counselors to create career development opportunities for students. The majority of school counselors report the importance of career counseling; however, middle school counselors acknowledge they spend less time on career counseling than they prefer (Osborn & Baggerly, 2004). There is a need to reprioritize career counseling, which includes recognizing and acknowledging how career counseling intersects with academic and personal and social counseling in K–12 schools (Anctil et al., 2012).

Career counseling is valuable and evidence needs to be provided to indicate how non-counseling–related tasks take time away from school counselors’ ability to offer adequate career counseling for students. Test coordination is time-consuming and an example of a non-counseling duty that some school counselors perform. Considering the amount of time this role requires, school counselors would find more time to provide career counseling services for students without this obligation. School counselors should gather evidence and provide accountability reports about how career counseling efforts contribute to student engagement and success.

Implications for School Counselors, K–12 Stakeholders, and Counselor Educators

In general, the practicing school counselors in this study had ample self-efficacy with regard to providing career counseling. However, there were certain items on the CCSES-Modified and the SCSE-Subscale that reveal discrepancies in middle school counselors’ levels of confidence. Counselors felt least confident in the subscales of Multicultural Competency Skills and Current Trends in the World of Work, Ethics, and Career Research. Specifically, they reported lower self-efficacy addressing special issues related to gender, ethnicity, and sexual orientation in relation to the world of work. In light of these findings, counselor preparation programs need to further investigate what is being taught in career counseling courses, how the content is being delivered, possible gaps in curriculum, and opportunities for outreach to current school counselors through continuing education. Given the powerful movement for advocacy related to these important social issues, it is in some ways confirming that the practicing counselors in this study felt less confident in these areas. Perhaps the national attention on issues of privilege and oppression related to gender, ethnicity, and sexual orientation has shed light on individual or systemic challenges these school counselors face as they try to serve diverse young adolescents in a dynamic phase of their development.

There are opportunities to increase career counseling self-efficacy related to gender, ethnicity, and sexual orientation in relation to the world of work. Bandura (1997) highlighted personal performance, vicarious learning, and social persuasion as particularly effective strategies for increasing self-efficacy. Continuing education, supervision, and professional organization engagement may be the best opportunities for continued development in these areas (Tang et al., 2004). In-service training and continuing education could be offered to provide school counselors relevant information to support their professional development and promote an increase in career counseling self-efficacy. Gaining up-to-date knowledge about the experiences of students with varied gender identities, ethnicities, and sexual orientations will best prepare school counselors to serve the entire student body. Observing advocacy approaches modeled by other leaders may inspire school counselors to use their voices in their own systems. Relatedly, this finding makes it apparent that K–12 school systems need clear and powerful policies and leadership around gender-, ethnicity-, and sexual orientation-related issues. School counselors are well positioned to partner with principals and superintendents in this important change process.

The second research question provided additional information about a somewhat contentious issue in previous research. School counselors who had teaching experience had higher career counseling self-efficacy than those who did not have teaching experience. This finding contradicts the findings of a study conducted with school counselors in Ohio (Scoles, 2011) and supports the findings of the national study conducted by Bodenhorn and Skaggs (2005), as described above. Contradictory findings like these beg for more research. Perhaps the higher self-efficacy of those with previous teaching experience is related to the preparation in specific academic disciplines that teachers receive. It could be that because these school counselors were previously trained in a specific academic area, they are more confident in talking with students about careers in that particular career cluster (e.g., science teachers who become school counselors may be more prepared to discuss careers in science, technology, engineering, and mathematics with students). Conversely, this potentially narrow view of career opportunities may limit the career exploration of students if school counselors do not include a wide array of career options. An excellent area for further research would be to identify how previous teaching experience may specifically impact school counselor self-efficacy.

School counselors without teaching experience, although lower in self-efficacy than those school counselors with teaching experience, still had high career counseling self-efficacy. This suggests that school counselors without teaching experience have confidence in their ability to provide career counseling. If, as Peterson and Deuschle (2006) suspected, the advantage of those with prior teaching experience is because of the increased training and practice in classroom management and lesson preparation, one would expect that effect to diminish as years of school counseling experience are accumulated. A larger sample than the one in this study would be necessary to test that empirically. If, however, the impetus for the significant impact of teaching experience is more general, those newer school counselors without teaching experience may be adjusting to the setting and to new ways of managing their time, balancing multiple roles and responsibilities, incorporating community involvement, working with parents, fostering collaborative relationships, and becoming familiar with local resources. All of these tasks take time and effort and could impact a school counselor’s self-efficacy to provide adequate services to students. It may be helpful for school counselors without teaching experience to ask for support and suggestions from seasoned school counselors in the district to learn from their experiences. In addition, professional development programming could be established for school counselors to become more familiar with the specific roles and responsibilities related to the career information, education, and counseling needs within a particular community.

Finally, the third focus of the study was on how school counselors use their time and if self-efficacy is related to that allocation. Most alarming about these findings was that school counselors are spending less time providing career counseling than they are doing non-counseling–related duties. A large percentage of middle school counselors’ time was reported to be spent coordinating testing or doing other non-counseling–related tasks, which is not the most efficient use of school counselors’ strengths. School counselors are uniquely trained to provide supplemental support for students in the academic, personal and social, and career domains in order to promote student success; therefore, it would be advantageous if they were able to utilize their time in a way that is consistent with the needs of students. One option to address the time constraint, particularly in this day of tighter budgets, is to utilize someone with an administrative background for the non-counseling duties in order for the school counselor to have time to incorporate adequate career counseling into their school counseling program. This is particularly important for middle school counselors providing career counseling because middle school students are preparing academic and career plans that will serve as a guide through high school and postsecondary educational endeavors (Trusty, Niles, & Carney, 2005; Wimberly & Noeth, 2005).

The world of work is continually changing, which makes it important to be aware of the current trends in this area. As these changes happen, marginalized populations face unique issues in the area of career exploration and planning. Counselors need to be trained adequately to provide career counseling to clients. In addition to providing relevant information, promoting thoughtful reflection, and facilitating discussions for counselors-in-training, counselor educators could provide outreach and continuing education opportunities focused on career counseling.

Just as career counseling may be infused with academic and personal and social counseling for school counselors, counselor educators may consider infusing career counseling concepts throughout other courses and experiences during a training program. Counselor educators could model this authentic type of integration. Counselor educators could talk more about various career clusters and the value of career counseling throughout a training program rather than just in one specific course. Counselor educators may also facilitate discussions with counselors-in-training about their own career counseling experiences, allowing trainees time to reflect on their experience. In addition, trainees could talk about how they have worked with people in roles other than a counselor through the career exploration and planning process.

Counselors need to consider ways to utilize and increase the support of administration and teachers to identify what needs to change in order for them to reallocate their time so they are able to provide more career counseling. Providing evidence of the positive impact of their work may be an effective strategy. There are many approaches to this, such as utilizing current research studies to communicate support for the value of career counseling efforts. In addition, school counselors can gather data from current students, parents, and alumni regarding their perception of and desire for career counseling services through surveys or focus groups. Once specific programs are implemented, school counselors can evaluate the outcomes of the career counseling efforts through both formal and informal assessment procedures with students, teachers, and parents. Administrators should continue to express support for the career counseling efforts of school counselors and show support by advocating for more personnel in order for students to receive adequate career counseling and to meet the demand of the non-counseling tasks that counselors are assigned.

Limitations

The findings should be considered in light of the limitations of the study. Because of the nature of instruments that involve self-report, the results are based on the current perception of the participants and not objective assessments of the effectiveness of their work. Also, it may be more socially and professionally desirable to have confidence in personal abilities and, therefore, some participants may have answered the way they thought they should. This study was limited to those middle school counselors who had publically available e-mail addresses and were working in Virginia. Non-respondents and middle school counselors outside of Virginia are not represented in these findings; therefore, generalizing the findings should be considered with caution. Furthermore, the 406 non-respondents and the 18 respondents who did not complete the entire survey may be systematically different from the 143 respondents who were included.

Conclusion

This study has provided important new information about the self-efficacy of school counselors in the middle school setting as related to career counseling. Career counseling self-efficacy was high overall, with specific areas of deficit related to gender, ethnicity, and sexual orientation. Those school counselors who had previous teaching experience had even higher career counseling self-efficacy than those who did not. High self-efficacy in school counselors had little or no impact on the time spent providing career counseling services. Tailoring continuing education opportunities in career counseling and providing clear administrative leadership would further strengthen practicing school counselor self-efficacy. Utilizing support personnel for non-counseling–related duties may allow school counselors to use their career counseling skills and training to help middle school students explore and connect with careers, thereby improving academic and life outcomes.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest or funding contributions for the development of this manuscript.

References

Akos, P. (2004). Investing in experiential training appropriate for preservice school counselors. The Journal for Specialists in Group Work, 29, 327–342. doi:10.1080/01933920490515973

Akos, P., Cockman, C. R., & Strickland, C. A. (2007). Differentiating classroom guidance. Professional School Counseling, 10, 455–463.

American School Counselor Association. (2014). ASCA mindsets and behaviors for student success: K-12 college- and career-readiness standards for every student. Alexandria, VA: Author.

Anctil, T., Smith, C., Schenck, P., & Dahir, C. (2012). Professional school counselors’ career development practices and continuing education needs. The Career Development Quarterly, 60, 109–121.
doi:10.1002/j.2161-0045.2012.00009.x

Baker, S. B. (1994). Mandatory teaching experience for school counselors: An impediment to uniform certification standards for school counselors. Counselor Education and Supervision, 33, 314–326. doi:10.1002/j.1556-6978.1994.tb00297.x

Bandura, A. (1986). Social foundations of thoughts and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman and Company.

Barbee, P. W., Scherer, D., & Combs, D. C. (2003). Prepracticum service-learning: Examining the relationship with counselor self-efficacy and anxiety. Counselor Education and Supervision, 43(2), 108–119. doi:10.1002/j.1556-6978.2003.tb01835.x

Barker, J., & Satcher, J. (2000). School counselors’ perceptions of required workplace skills and career development competencies. Professional School Counseling, 4, 134–139.

Bodenhorn, N., & Skaggs, G. (2005). Development of the School Counselor Self-Efficacy Scale. Measurement and Evaluation in Counseling and Development, 38, 14–28.

Bringman, N., & Lee, S. M. (2008). Middle school counselors’ competence in conducting developmental classroom lessons: Is teaching experience necessary? Professional School Counseling, 11, 380–385.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Curry, J. R., Belser, C. T., & Binns, I. C. (2013). Integrating postsecondary college and career options in the middle level curriculum. Middle School Journal, 44(3), 26–32.

Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132. doi:10.1146/annurev.psych.53.100901.135153

Geltner, J. A., & Clark, M. A. (2005). Engaging students in classroom guidance: Management strategies for middle school counselors. Professional School Counseling, 9, 164–166.

Kenny, M. E., Blustein, D. L., Haase, R. F., Jackson, J., & Perry, J. C. (2006). Setting the stage: Career development and the student engagement process. Journal of Counseling Psychology, 53, 272–279. doi:10.1037/0022-0167.53.2.272

Lapan, R. T., Gysbers, N. C., & Petroski, G. F. (2001). Helping seventh graders be safe and successful: A statewide study of the impact of comprehensive guidance and counseling programs. Journal of Counseling & Development, 79, 320–330. doi:10.1002/j.1556-6676.2001.tb01977.x

Lapan, R. T., Gysbers, N. C, & Sun, Y. (1997). The impact of more fully implemented guidance programs on the school experiences of high school students: A statewide evaluation study. Journal of Counseling & Development, 75, 292–302. doi:10.1002/j.1556-6676.1997.tb02344.x

Larson, L. M., & Daniels, J. A. (1998). Review of the counseling self-efficacy literature. The Counseling Psychologist, 26, 179–218. doi:10.1177/001000098262001

Lent, R. W., & Brown, S. D. (2006). On conceptualizing and assessing social cognitive constructs in career research: A measurement guide. Journal of Career Assessment, 14, 12–35. doi:10.1177/1069072705281364

Lent, R. W., Brown, S. D., & Hackett, G. (2000). Contextual supports and barriers to career choice: A social cognitive analysis. Journal of Counseling Psychology, 47, 36–49. doi:10.1037/0022-0167.47.1.36

Lent, R. W., Hill, C. E., & Hoffman, M. A. (2003). Development and validation of the counselor activity self-efficacy scales. Journal of Counseling Psychology, 50, 97–108.

Montiel-Overall, P. (2006). Implications of missing data in survey research. Canadian Journal of Information and Library Science, 30, 241–269.

O’Brien, K. M., & Heppner, M. J. (1996). Applying social cognitive career theory to training career counselors. The Career Development Quarterly, 44, 367–377. doi:10.1002/j.2161-0045.1996.tb00452.x

O’Brien, K. M., Heppner, M. J., Flores, L., & Bikos, L. (1997). The career counseling self-efficacy scale: Instrument development and training applications. Journal of Counseling Psychology, 44, 20–31. doi:10.1037/0022-0167.44.1.20

Orthner, D. K., Jones-Sanpei, H., Akos, P., & Rose, R. A. (2013). Improving middle school student engagement through career-relevant instruction in the core curriculum. The Journal of Educational Research, 106, 27–38. doi:10.1080/00220671.2012.658454

Osborn, D. S., & Baggerly, J. N. (2004). School counselors’ perceptions of career counseling and career testing: Preferences, priorities, and predictors. Journal of Career Development, 31, 45–59.

Osborn, D. S., & Reardon, R. C. (2006). Using the Self-Directed Search: Career Explorer with high-risk middle school students. The Career Development Quarterly, 54, 269–273. doi:10.1002/j.2161-0045.2006.tb00158.x

Perrone, K. M., Perrone, P. A., Chan, F., & Thomas, K. R. (2000). Assessing efficacy and importance of career counseling competencies. The Career Development Quarterly, 48, 212–225.
doi:10.1002/j.2161-0045.2000.tb00287.x

Perry, J. C., Liu, X., & Pabian, Y. (2010). School engagement as a mediator of academic performance among urban youth: The role of career preparation, parental career support, and teacher support. The Counseling Psychologist, 38, 269–295. doi:10.1177/0011000009349272

Peterson, J. S., & Deuschle, C. (2006). A model for supervising school counseling students without teaching experience. Counselor Education and Supervision, 45, 267–281. doi:10.1002/j.1556-6978.2006.tb00003.x

Scarborough, J. L., & Culbreth, J. R. (2008). Examining discrepancies between actual and preferred practice of school counselors. Journal of Counseling & Development, 86, 446–459.
doi:10.1002/j.1556-6678.2008.tb00533.x

Scheel, M. J., & Gonzalez, J. (2007). An investigation of a model of academic motivation for school counseling. Professional School Counseling, 11, 49–56.

Scoles, M. A. (2011). An examination of the impact of prior teaching experience on the self-efficacy of school counselors in the state of Ohio (Doctoral dissertation). Retrieved from OhioLINK ETD Center.

Smith, S. L., Crutchfield, L. B., & Culbreth, J. R. (2001). Teaching experience for school counselors: Counselor educators’ perceptions. Professional School Counseling, 4, 216–224.

Tang, M., Addison, K. D., LaSure-Bryant, D., Norman, R., O’Connell, W., & Stewart-Sicking, J. A. (2004). Factors that influence self-efficacy of counseling students: An exploratory study. Counselor Education and Supervision, 44, 70–80. doi:10.1002/j.1556-6978.2004.tb01861.x

Trusty, J., Niles, S. G., & Carney, J. V. (2005). Education-career planning and middle school counselors. Professional School Counseling, 9, 136–143.

Wimberly, G. L., & Noeth, R. J. (2005). College readiness begins in middle school. ACT Policy Report. Iowa City, IA: American College Testing ACT, Inc.

Carrie Sanders is a visiting assistant professor at Virginia Tech. Laura E. Welfare, NCC, is an associate professor at Virginia Tech. Steve Culver is Director of Assessment and Analytics at North Carolina A&T State University. Correspondence may be addressed to Carrie Sanders, 1750 Kraft Drive, Suite 2005, Blacksburg, VA 24061, cbrill@vt.edu.

Burnout, Stress and Direct Student Services Among School Counselors

Oct 15, 2016 | Article, Volume 6 - Issue 4

Patrick R. Mullen, Daniel Gutierrez

The burnout and stress experienced by school counselors is likely to have a negative influence on the services they provide to students, but there is little research exploring the relationship among these variables. Therefore, we report findings from our study that examined the relationship between practicing school counselors’ (N = 926) reported levels of burnout, perceived stress and their facilitation of direct student services. The findings indicated that school counselor participants’ burnout had a negative contribution to the direct student services they facilitated. In addition, school counselors’ perceived stress demonstrated a statistically significant correlation with burnout but did not contribute to their facilitation of direct student services. We believe these findings bring attention to school counselors’ need to assess and manage their stress and burnout that if left unchecked may lead to fewer services for students. We recommend that future research further explore the relationship between stress, burnout and programmatic service delivery to support and expand upon the findings in this investigation.

Keywords: burnout, stress, school counselors, student services, service delivery

The American School Counselor Association (ASCA; 2012) recommends that school counselors enhance the personal, social, academic and career development of all students through the organization and facilitation of comprehensive programmatic counseling services. Delivery of student services is part of a larger framework articulated by ASCA’s National Model (2012) that also includes management, accountability and foundation components of school counseling programs. However, ASCA notes that school counselors should “spend 80 percent or more of their time in direct and indirect services to students” (ASCA, 2012, p. xii). ASCA defines indirect student services as services that are in support of students and involve interactions (e.g., referrals, consultations, collaborations and leadership) with stakeholders other than the student (e.g., parents, teachers and community members). On the other hand, direct student services are interactions that occur face-to-face and involve the facilitation of curriculum (e.g., classroom guidance lessons), individual student planning and responsive services (e.g., individual, group and crisis counseling). In either case, ASCA charges school counselors with prioritizing the delivery of student services.

As a part of their work, school counselors often incur high levels of stress that may result from multiple job responsibilities, role ambiguity, high caseloads, limited resources for coping and limited clinical supervision (DeMato & Curcio, 2004; Lambie, 2007; McCarthy, Kerne, Calfa, Lambert, & Guzmán, 2010). In addition, burnout can result from the ongoing experience of stress (Cordes & Dougherty, 1993; Maslach, 2003; Schaufeli & Enzmann, 1998) and can result in diminished or lower quality rendered services (Lawson & Venart, 2005; Maslach, 2003). While research on burnout is common in the school counseling literature (Butler & Constantine, 2005; Lambie, 2007; Wachter, Clemens, & Lewis, 2008; Wilkerson & Bellini, 2006), studies have not focused on the relationship between burnout and school counselors’ service delivery. Yet, burnout has the potential to produce negative consequences for the work rendered by school counselors and could result in fewer services for students (Lambie, 2007; Lawson & Venart, 2005; Maslach, 2003). Therefore, the purpose of this research was to examine the contribution of school counselors’ levels of burnout and stress to their delivery of direct student services.

School Counselors and the Delivery of Student Services

Research on school counselors’ delivery of student services has produced positive findings. In a meta-analysis that included 117 experimental studies, Whiston, Tai, Rahardja, and Eder (2011) identified that, in general, school counseling services have a positive influence on students’ problem-solving and school behavior. Furthermore, in schools where school counselors completed higher levels of student services focused on improving academic success, personal and social development, and career and college readiness, students experienced a variety of positive outcomes, such as increased sense of belongingness, increased attendance, fewer hassles with other students, and less bullying (Dimmitt & Wilkerson, 2012). Moreover, researchers have shown that the higher occurrence of school counselor-facilitated services is beneficial for students’ educational experience and academic outcomes (Carey & Dimmitt, 2012; Lapan, Gysbers, & Petroski, 2001; Wilkerson, Pérusse, & Hughes, 2013). Overall, the services conducted by school counselors have a positive impact on student success. As such, research investigating the factors related to higher incidence of school counselors’ direct student services could provide significant educational benefits to schools.

Researchers have examined a variety of topics that relate to increased student services. Clemens, Milsom, and Cashwell (2009) found that if school counselors had a good relationship with their principal and were engaged in higher levels of advocacy, they were likely to have increased implementation of programmatic counseling services. Another study concluded that school counselors’ values were not associated with the occurrence of service delivery, but researchers did find counselors with higher levels of leadership practices also delivered more school counseling services (Shillingford & Lambie, 2010). Other factors related to increased levels of school counselors’ service delivery are increased job satisfaction (Baggerly & Osborn, 2006; Pyne, 2011) and higher self-efficacy (Ernst, 2012; Mullen & Lambie, 2016). These studies provided notable contributions to the literature; however, at this time no known studies have examined the relationship among school counselors’ burnout, perceived stress and direct student services.

Stress and Burnout Among School Counselors

Stress is a significant issue that relates to the impairment of work performance (Salas, Driskell, & Hughes, 1996) and is a likely problem for school counselors. The construct of stress has a rich history in scientific literature dating back to the 1930s (Cannon, 1935; Selye, 1936). Selye (1980) articulated one of the first broad definitions of stress by defining it as the “nonspecific results of any demand upon the body” (p. vii). Over time, various authors developed an assortment of definitions (Ivancevich & Matteson, 1980; Janis & Mann, 1977; McGrath, 1976), but Lazarus and Folkman’s (1984) definition of stress is common among scholars (Driskell & Salas, 1996; Lazarus, 2006). In their Transactional Model of Stress and Coping, Lazarus and Folkman (1984) defined stress as a “particular relationship between the person and the environment that is appraised by the person as taxing or exceeding his or her resources and endangering his or her wellbeing” (p. 19). Lazarus and Folkman conceptualized that stress results from an imbalance between one’s perception of demands or threats and their ability to cope with the perceived demands or threats. Consequently, one’s appraisal of demands and their assessment of their coping ability becomes a critical issue in relationship to whether or not the demand will trigger a stress response.

McCarthy et al. (2010) applied Lazarus and Folkman’s model of stress (1984) to school counselors using an instrument that measures the demands and resources experienced by school counselors called the Classroom Appraisal of Resources and Demands–School Counselor Version (McCarthy & Lambert, 2008). McCarthy et al. (2010) found that school counselors who reported challenging demands as a part of their job also had higher levels of stress. This finding is troubling considering that school counselors oftentimes encounter ambiguous job duties, inconsistent job roles and conflicts in their job expectations (Burnham & Jackson, 2000; Culbreth, Scarborough, Banks-Johnson, & Solomon, 2005; Lambie, 2007; Scarborough & Culbreth, 2008). An additional concern is that stress occurring over an extended period of time can lead to emotional and physical health problems (Sapolsky, 2004) along with increased likelihood of leaving the profession (DeMato & Curcio, 2004). Fortunately, prior research reveals that school counselors have reported low stress levels (McCarthy et al., 2010; Rayle, 2006). Still, research on school counselors’ stress and its effects on the services they provide is important.

An additional factor that we believe may have an impact on direct student services is burnout. Burnout was first recognized in the 1970s (Freudenberger, 1974; Maslach, 1976) and is considered to have significant consequences for counseling professionals (Butler & Constantine, 2005; Lambie, 2007; Lawson, 2007; Lee et al., 2007). The topic of burnout is common in the literature across many disciplines (Schaufeli, Leiter, & Maslach, 2009) and has been given particular attention in school counseling research (Butler & Constantine, 2005; Lambie, 2007; Wachter et al., 2008; Wilkerson & Bellini, 2006). Freudenberger (1974, 1986) suggested that burnout results from depleted energy and the feelings of being overwhelmed that emerge from the exposure to diverse issues related to helping others, which over time affects one’s attitude, perception and judgment. Pines and Maslach (1978) described burnout as an ailment “of physical and emotional exhaustion, involving the development of negative self-concept, negative job attitude, and loss of concern and feelings for clients” (p. 234). In 1981, the Maslach Burnout Inventory (MBI) was developed as a method to measure one’s experience of burnout in the helping and human service field (Maslach & Jackson, 1981).

More recently, Lee et al. (2007) expanded the measurement of burnout and presented the construct of counselor burnout, which they defined as “the failure to perform clinical tasks appropriately because of personal discouragement, apathy to symptom stress, and emotional/physical harm” (p. 143). Within their model, Lee and associates found that counselor burnout includes the constructs of exhaustion, negative work environment, devaluing clients, incompetence and deterioration in personal life. These constructs correlate with the factors measured by the MBI (Maslach & Jackson, 1981), but provide a definition consistent with the work of school counselors (Gnilka, Karpinski, & Smith, 2015).

Many researchers have explored factors related to school counselor burnout. Overall, scholars have found that school counselors report low levels of burnout (Butler & Constantine, 2005; Gnilka et al., 2015; Lambie, 2007; Wachter et al., 2008; Wilkerson & Bellini, 2006). Nonetheless, researchers also reported that higher collective self-esteem is associated with a higher sense of personal accomplishment and lower emotional exhaustion (Butler & Constantine, 2005), whereas higher levels of ego development are associated with higher personal accomplishment (Lambie, 2007). Moreover, Wilkerson and Bellini (2006) discovered that school counselors who handle stressors with emotion-focused coping are at a higher risk of experiencing burnout symptoms, and Wilkerson (2009) established that school counselors’ emotion-focused coping increases their likelihood of experiencing symptoms of burnout. Yet, there is no research on the connection between school counselors’ burnout and the direct student services they provide despite a high likelihood that burnout is the cause of fewer and deteriorated services for students (Maslach, 2003).

The purpose of this study was to build upon existing literature regarding school counselors’ stress, burnout and their facilitation of direct student services. The guiding research questions were: (a) Do practicing school counselors’ levels of burnout and perceived stress contribute to their levels of service delivery? and (b) Do practicing school counselors’ levels of stress correlate with their burnout? Consequently, the following research hypotheses were examined: (a) School counselors’ degree of burnout and perceived stress contributes to their facilitation of direct student services, and (b) School counselors’ degree of perceived stress correlates positively with their level of burnout.

Method

Procedures

To answer the research questions associated with this study, we employed a cross-sectional research design (Gall, Gall, & Borg, 2007). Furthermore, this study utilized online survey data collection procedures. Prior to any data collection, we received approval from the Institutional Review Board at the first author’s university. During the first step in the data collection process, we retrieved the name and e-mail address of every school counselor listed in the ASCA online directory of membership. Next, we generated a simple random sample of school counselors. Then, we sent the sample selected from the ASCA online directory a series of three e-mails that aligned with tailored design method (Dillman, Smyth, & Christian, 2009) recommendations for survey research. Each e-mail contained a brief description of the survey and a link to the online survey managed by Qualtrics (2013). If a participant wished to take the survey, he or she was directed to the Web site that posted the explanation of the study. If they agreed to participate, they would move forward and complete the survey. Participants were screened as to whether they were practicing school counselors or not (e.g., student, counselor educator or retired). Of the 6,500 participants sampled, 41 indicated they were not a practicing school counselor. In addition, 312 e-mails were not working at the time of the survey. Out of the 6,147 practicing school counselors surveyed, 1,304 (21.21% visit response rate) visited the survey Web site and 926 completed the survey in its entirety, which resulted in a 15.06% useable response rate. The response rate received for this study is high in comparison to studies using similar methods (e.g., 14%, Harris, 2013; 11.4%, Mullen, Lambie & Conley, 2014).

Participant Characteristics

Participants (N = 926) were practicing school counselors in private, public and charter K–12 educational settings from across the United States. The mean age was 43.27 (SD = 10.03) and included 816 (88.1%) female and 110 (11.9%) male respondents. The participants’ ethnicity included 50 (5.4%) African Americans, 5 (.5%) Asian Americans, 29 (3.1%) Hispanic Americans, 11 (1.2%) Multiracial, 2 (.2%) Native Americans, 4 (.4%) Pacific Islanders, 811 (87.6%) European Americans, and 13 (1.5%) participants who identified their ethnicity as “Other.” On average, participants had 10.97 (SD = 6.92) years of experience and 401.45 (SD = 262.05) students on their caseload. The geographical location of the participants’ work setting favored suburban (n = 434, 46.9%) and rural communities (n = 321, 34.7%) with fewer school counselors working in urban settings (n = 171, 18.5%). Most participants reported that they worked in the high school grade levels (n = 317, 34.2%) closely followed by elementary (n = 270, 29.2%) and middle school or junior high school (n = 203, 21.9%) grade levels, with 136 (14.7%) respondents working in another grade level format (e.g., grades K–12, K–8, or 6–12).

Measures

This study used the (a) Counselor Burnout Inventory (CBI; Lee et al., 2007), (b) the School Counselor Activity Rating Scale (SCARS; Scarborough, 2005), and (c) the Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983). Participants also completed a researcher-created demographics form regarding their personal characteristics (e.g., age, gender and ethnicity) and work-related characteristics (e.g., location type, grade level, caseload, experience as a school counselor and percentage of time they directly work with students).

CBI. The CBI (Lee et al., 2007) is a 20-item self-report measure that examines counselor burnout across five domains. The domains that make up the CBI include: (a) exhaustion, (b) incompetence, (c) negative work environment, (d) devaluing client, and (e) deterioration in personal life. The CBI makes use of a 5-point Likert rating scale that ranges from 1 (never true) to 5 (always true) and examines emotional states and behaviors representative of burnout. Some sample items include “I feel exhausted due to my work as a counselor” (exhaustion), “I feel I am an incompetent counselor” (incompetence), “I feel negative energy from my supervisor” (negative work environment), “I have little empathy for my clients” (devaluing client), and “I feel I have poor boundaries between work and my personal life” (deterioration in personal life). Lee et al. (2007) demonstrated the construct validity of the CBI through an exploratory factor analysis that identified a five-factor solution in addition to a confirmatory factor analysis that supported the five-factor model with an adequate fit to the data.

Gnilka et al. (2015) found support for the five-factor structure of the CBI (Lee et al., 2007) with school counseling using confirmatory factor analysis, which supports the CBI as an appropriate measure for school counselor burnout. Lee et al. (2007) established convergent validity for the CBI based upon the correlations between the subscales on the Maslach Burnout Inventory-Human Services Survey (Maslach & Jackson, 198l) and the CBI. In prior research, the Cronbach’s alphas of the CBI subscales indicated good internal consistency (Streiner, 2003) with score ranges of .80 to .86 for exhaustion, .73 to .81 for incompetence, .83 to .85 for negative work environment, .61 to .83 for devaluing client, and .67 to .84 for deterioration in personal life (Lee et al., 2007; Lee, Cho, Kissinger, & Ogle, 2010; Puig et al., 2012). The internal consistency coefficients of the CBI in this investigation also were good (Streiner, 2003) with Cronbach’s alphas of .87 for exhaustion, .79 for incompetence, .84 for negative work environment, .79 for devaluing client, and .81 for deterioration in personal life.

SCARS. The SCARS (Scarborough, 2005) is a 48-item verbal frequency measure that examines the occurrence that school counselors actually perform and prefer to perform components of the ASCA National Model (2012). The SCARS measures school counselors’ ratings of activities based on the four levels of interventions articulated by ASCA (1999) and the ASCA National Model (2003). Unfortunately, a more recent version of the SCARS that articulates the new ASCA National Model (2012) does not exist. Nevertheless, this study utilized two SCARS scales (counseling and curriculum) that measure the incidence of direct student services. To the benefit of this investigation, the direct services measured on the SCARS have not changed in the new edition of the ASCA National Model (2003, 2012). Similar to Shillingford and Lambie (2010) and Mullen and Lambie (2016), this investigation utilized the actual scale, but not the prefer scale, on the SCARS (Scarborough, 2005) because this study sought to examine the frequency that school counselors delivered direct student services, not their preferences and not the difference between their preference and actuality. The subscales that measure direct student services used in this study included the counseling (e.g., group and individual counseling interventions; 10 items) and curriculum (e.g., classroom guidance interventions; 8 items) subscales, whereas the coordination, consultation and other activities scales were not used because they measure indirect activities.

The SCARS (Scarborough, 2005) assesses the frequency of school counselor service delivery with a 5-point Likert rating scale that ranges from 1 (I never do this) to 5 (I routinely do this). Scores on the SCARS can be total scores or mean scores. Some sample items from the counseling subscale are “Counsel with students regarding school behavior” and “Provide small group counseling for academic issues.” Some sample items from the curriculum subscale are “Conduct classroom lessons addressing career development and the world of work” and “Conduct classroom lessons on conflict resolution.” Scarborough (2005) examined the validity by investigating the variances in score on the actual scale based on participant grade level and found that participants’ grade level had a statistically significant effect across the scales with small to large effect sizes (e.g., ranging from .11 to .68[ω²]), which supported the convergent validity of the SCARS. Additionally, construct validity was supported using factor analysis. In prior research using the SCARS, the internal consistency of the counseling and curriculum scales was strong with Cronbach’s alphas of .93 for the curriculum actual scale and .85 for the counseling actual scale (Scarborough, 2005). The internal consistency coefficients of the SCARS actual subscales in this investigation were good (Streiner, 2003) with Cronbach’s alphas of .77 for the counseling scale and .93 for the curriculum scale.

PSS. The PSS (Cohen et al., 1983) is a 10-item self-report measure that examines the participants’ appraisal of stress by asking about feelings and thoughts during the past month. The PSS uses a 5-point Likert scale that ranges from 0 (never) to 4 (very often) and includes four positively stated items that are reverse coded. Some sample items include, “In the last month, how often have you felt that you were on top of things?” (reverse coded), and “In the last month, how often have you been upset because of something that happened unexpectedly?” The PSS has been shown to have acceptable internal consistency with Cronbach’s alphas ranging from .84 to .91 (Chao, 2011; Cohen et al., 1983; Daire, Dominguez, Carlson, & Case-Pease, 2014). The internal consistency coefficient of the PSS in this study also was acceptable (Streiner, 2003) with a Cronbach’s alpha of .88.

Results

Preliminary Analysis

Initial screening of the data included the search for outliers (e.g., data points three or more standard deviations from the mean) using converted z-scores (Osborne, 2012), which resulted in identifying 21 cases that had at least one variable with an extreme outlier. To accommodate for these outliers, the researchers utilized a Windorized mean based on adjacent data points (Barnett & Lewis, 1994; Osborne & Overbay, 2004). Next, the assumptions associated with structural equation modeling (SEM) were tested (e.g., normality and multicollinearity; Hair, Black, Babin, Anderson, & Tatham, 2006; Tabachnick & Fidell, 2007). Multicollinearity was not present with these data; however, the data violated the assumption of normality of a single composite variable (e.g., devaluing clients scale on the CBI). Researchers conducted descriptive analyses of the data using the statistical software SPSS. Table 1 presents the means, standard deviations and correlations for the study variables.

Model Testing

This correlational investigation utilized a two-step SEM method (Kline, 2011) to examine the research hypothesis employing AMOS (version 20) software. The first step included a confirmatory factor analysis (CFA) to inspect the measurement model of burnout and its fit with the data. Then, a structural model was developed based on the measurement model. The measurement model and structural model were appraised using model fit indices, standardized residual covariances, standardized factorial loadings and standardized regression estimates (Byrne, 2010; Kline, 2011). Modifications to the models were made as needed (Kline, 2011). Both the measurement and the structural models employed the use of maximum likelihood estimation technique despite the presence of non-normality based on recommendations from the literature (Curran, West, & Finch, 1996; Hu, Bentler, & Kano, 1992; Lei & Lomax 2005; Olsson, Foss, Troye, & Howell, 2000).

Table 1 Correlations among measures of direct student services, perceived stress, and burnout
	M	SD	1	2	3	4	5	6	7	8	9
Counseling	3.02	.60	–
Curriculum	2.77	1.16	.44	–
Percent of Time	59	78	.36	.27	–
Perceived Stress	1.56	.63	-.15	-.11	-.14	–
Exhaustion	3.04	.86	-.15	-.11	-.11	.61	–
Incompetence	2.29	.68	-.31	-.14	-.18	.49	.44	–
NEW	2.56	.87	-.23	-.19	-.22	.46	.53	.39	–
DC	1.39	.50	-.20	-.17	-.14	.32	.28	.45	.64	–
DPL	2.39	.80	-.19	-.12	-.16	.58	.66	.41	.47	.30	–
Note. N = 926. All correlations (r) were statistically significant (p < .001). Counseling = frequency of direct counseling services, curriculum = frequency of direct curriculum services, percent of time = percent of time in direct services to students, NEW = negative work environment, DC = devaluing client, DPL = deterioration in personal life.

Multiple fit indices were examined to determine the goodness of fit for the measurement model and structural model (Hu & Bentler, 1999; Kline, 2011; Weston & Gore, 2006). The fit indices that were used include: (a) chi-square, (b) comparative fit index (CFI), (c) goodness of fit (GFI), (d) standardized root mean square residual (SRMSR), and (e) root mean square error of approximation (RMSEA). Furthermore, we consulted the normed fit index (NFI) and Tucker-Lewis index (TLI) because they are more robust to non-normal data as compared to other indices (Lei & Lomax, 2005). For a detailed description of these fit indices, readers can review the works of Hu and Bentler (1999), Kline (2011), and Weston and Gore (2006). We used these fit indices to establish a diverse view of model fit.

Measurement model. First, we employed a CFA model to examine the latent variable representing burnout (Lee et al., 2007). The research team totaled each subscale on the CBIs to develop a composite score for each domain. The initial measurement model for burnout produced acceptable standardized factor loadings ranging from .41 (devaluing client) to .57 (incompetence), .62 (negative work environment), .77 (deterioration in personal life), and .82 (exhaustion). Furthermore, all fit indices for the measurement model indicated an adequate fitting model except chi-square, RMSEA, and TLI: χ2 (df = 5, N = 926) = 107.07, p < .001; GFI = .96; CFI = .92; RMSEA = .15; SRMR = .06; NFI = .92; TLI = .85. Therefore, we consulted the modification indices and standardized residual covariance matrix and tested a new CFA based upon these consultations.

The modifications indices indicated the need to correlate the error terms for incompetence and devaluing client. The resulting model produced a model in which all fit indices indicated an adequate fitting model: χ² (df = 4, N = 926) = 12.03, p = .02; GFI = .99; CFI = .99; RMSEA = .05; SRMR = .02; NFI = .99; TLI = .99. Further inspection of the standardized factor loadings for the model indicated they were all acceptable except for the factor loading for devaluing client, which dropped to .36 (below .40; Stevens, 1992). While these modifications improved the overall fit of the CFA, the correlation of incompetence and devaluing client has no theoretical justification (Byrne, 2010). In addition, the correlation of the error terms for incompetence and devaluing client produced a standardized factor loading below the noted standard of .40 (Kline, 2011; Stevens, 1992). Subsequently, we removed the subscale of devaluating client given: (a) the low factor loading produced after modification of the initial model, and (b) the lack of normality in the composite score.

Next, we examined the new modified measurement model that included the removal of the subscale devaluing client. The resulting model (see Figure 1) produced a model in which all fit indices indicated a good fitting model: χ² (df = 2, N = 926) = 8.25, p = .02; GFI = .99; CFI = .99; RMSEA = .06; SRMR = .02; NFI = .99; TLI = .98. The modified measurement model for burnout produced acceptable standardized factor loadings ranging from .53 (incompetence) to .63 (negative work environment), .77 (deterioration in personal life), and .85 (exhaustion). In review of the model fit indices and standardized factor loadings, we deemed the measurement model acceptable for use in the structural model.

Structural model. We developed the structural model (see Figure 1) based on a review of the literature, and it was theorized in this model that school counselors’ perceived stress correlates to school counselors’ burnout and contributes to the frequency with which they provide direct student services. In addition, this model tested the hypothesized model that school counselors’ burnout contributes to their frequency of direct student services. The structural model includes the measurement model previously tested that consisted of the latent variable of burnout. School counselors’ perceived stress and burnout were defined as exogenous or independent variables. Perceived stress was a manifest variable consisting of participants’ composite scores on the PSS (Cohen et al., 1983).

Additionally, we defined the manifest variables of percentage of time at work providing direct services to students, direct curriculum activities, and direct counseling activities as the endogenous or dependent variables that measure participants’ facilitation of direct student services. The variable of percentage of time at work providing direct services to students was a single demographic item reported by participants, while direct curriculum activities and direct counseling activities were the participants’ composite scores derived from subscales on the SCARS (Scarborough, 2005). In addition, the error terms of the direct student services variables—percentage of time at work providing direct services to students, direct curriculum activities and direct counseling activities—were correlated given that they measure similar constructs.

An examination of the structural model indicated a strong goodness of fit for all fit indices except for chi-square: χ² (df = 14, N = 926) = 108.37, p < .001; GFI = .97; CFI = .96; RMSEA = .07; SRMR = .04; NFI = .95; TLI = .91. The researchers deemed the structural model as suitable with these data despite the significant chi-square (Henson, 2006; Kline, 2011; Weston & Gore, 2006). A closer examination of the standardized regression weights identified that school counselors’ burnout scores contributed to 12% (β = -.35, p < .001) of the variance in their direct counseling activities and 5% (β = -.22, p < .001) of the variance in their direct curriculum activities. Furthermore, school counselors’ burnout scores contributed to 6% (β = -.24, p < .001) of the variance in percentage of time at work providing direct services to students. Perceived stress did not contribute to direct counseling activities (β = .11, p = .04), direct curriculum activities (β = .06, p = .31), and percentage of time at work providing direct services to students (β = .04, p = .51). In addition, perceived stress and burnout produced a statistically significant correlation (β = .75, p < .001; 56% of the variance explained).

The structural model (Figure 1) indicates that school counselors’ level of counselor burnout had a negative contribution to the frequency of their direct counseling activities, direct curriculum activities and percentage of time at work providing direct services to students. However, it should be noted that the effect sizes of these findings were small to medium (Sink & Stroh, 2006). An additional finding from this investigation was that the perceived stress correlated with burnout with a large effect size (Sink & Stroh, 2006); however, perceived stress did not have a statistically significant contribution to school counselors’ direct counseling activities, direct curriculum activities, and percentage of time at work providing direct services to students.

Figure 1. Final hypothesized structural model depicting the relationship between school counselors’ (N = 926) perceived stress, burnout, and direct student services.

Discussion

This study examined the relationship between school counselors’ reported burnout, perceived stress and frequency of direct student services. The findings indicated burnout was a statistically significant contributor to the frequency of direct counseling services (β = -.35; medium effect size) and direct curriculum services (β = -.22; small to medium effect size). Furthermore, the findings identified that burnout was a significant contributor to the participants’ report of the percentage of time they spend on their job working directly with students (β = -.24; small to medium effect size). Although the results should be interpreted with some level of caution, we found that burnout also had a statistically significant relationship to frequency of direct student services with increased levels of burnout relating to lower levels of direct student services. Nonetheless, these findings are not surprising considering the literature on burnout emphasizes the important role burnout plays on the effort one places on their job, with individuals presenting with higher burnout typically having lower investment interest in their job (Garman, Corrigan, & Morris, 2002; Landrum, Knight, & Flynn, 2012; Maslach, 2003). While the findings support the literature on the role of burnout, they also bring attention to the possibility that burnout does not have a strong relationship to school counselors’ facilitation of direct counseling services as noted by the small effect size.

An interesting finding was that school counselors’ degree of perceived stress did not contribute to the direct student services variables and yet did correlate with burnout. In fact, the relationship between perceived stress and counselor burnout had a large effect size, with 56% of the variance among these variables explained by their relationship. This finding accentuates the difference between the constructs of burnout and stress because burnout had a statistically significant relationship with the direct student services variables and stress did not, despite the strength of the relationship between burnout and stress. One interpretation of this finding is that school counselors’ ability to manage and cope with stress permits them to complete their job functions, whereas burnout may be more challenging to overcome. Furthermore, scholars state that prolonged exposure to stress worsens or cultivates burnout (Cordes & Dougherty, 1993; Schaufeli & Enzmann, 1998). This finding is logical given the theory behind burnout (Lee et al., 2007; Maslach, 2003); yet, this is one of only a few studies (McCarthy et al., 2010; Wilkerson & Bellini, 2006) in the school counseling literature to examine this relationship. However, these results need further exploration. As McCarthy et al. (2010) noted, the construct of stress is multidimensional (includes appraisal of resources and demands) and the PSS (Cohen et al., 1983) is a single-dimension scale. Therefore, a scale that examines stress in a multifaceted manner may produce different results.

An additional finding worth discussion involves the measurement model of the CBI (Lee et al., 2007). Specifically, this study found that the construct of devaluing client did not fit with the data. Furthermore, participants reported low scores regarding the devaluing client scale, as indicated by the descriptive statistics. The devaluing client subscale also was the only subscale on the CBI that was not normally distributed. These results were similar to Gnilka et al.’s (2015) findings that indicated school counselors are likely to maintain high levels of empathy and positive regard for their students. These findings may indicate that the devaluing clients subscale may not reflect symptoms of burnout for school counselors. This is a promising finding as it suggests that school counselors do not develop a negative perspective of students because of the negative consequences of their job.

The descriptive statistics from this investigation also provide some noteworthy information. First, participants reported moderate to low levels of burnout across the five factors of the CBI (Lee et al., 2007), with exhaustion having the highest mean score. These results are consistent with prior research (Butler & Constantine, 2005; Lambie, 2007; Wachter et al., 2008; Wilkerson & Bellini, 2006) on burnout and indicate that, overall, school counselors report low levels of burnout. An additional finding was that school counselors reported a low level of perceived stress, which is surprising given the challenge of role ambiguity, confusion and conflict (Burnham & Jackson, 2000; Culbreth et al., 2005; Lambie, 2007; Scarborough & Culbreth, 2008). However, school counselors have reported low levels of stress in other research (e.g., McCarthy et al., 2010; Rayle, 2006). The last noteworthy finding from the descriptive statistics was the measures of direct student services. This investigation was one of the first to focus specifically on the topic of direct student services versus other aspects of school counselors’ roles. This study found that school counselors reported that, on average, they spend over half their time working directly with students. In addition, they reported high frequencies for facilitating both curriculum and counseling activities. These findings are promising and consistent with other research examining these constructs (Mullen & Lambie, 2016; Scarborough & Culbreth, 2005; Shillingford & Lambie, 2010). Overall, the results from this study provide new and novel information for the school counseling discipline.

Limitations and Implications for Future Research

Readers should interpret these findings within the context of their limitations. Some limitations from this study include: (a) associational research using correlation statistics does not establish cause and effect relationships; (b) the response rate, although high as compared to other studies with similar methods, is low; and (c) the generalizability of these findings is limited by the sampling procedures (e.g., only sampled ASCA members; Gall et al., 2007). In addition, participants who respond to surveys may have different characteristics as compared to those school counselors who chose not to participate (Gall et al., 2007).

The findings from this study have implications for future research. A prominent direction for future research is the examination of the relationship between stress and programmatic service delivery, including direct student services. This study identified that perceived stress has no relationship with direct service delivery, but a multidimensional measure of stress (McCarthy & Lambert, 2008) may produce different results. Similarly, this study found that perceived stress relates to higher levels of burnout and supports the theory that chronic stress relates to increased burnout. Future research might further confirm these findings.

Another relevant future research implication is exploring factors that prevent or mediate the contribution of burnout to school counselor service delivery, considering this investigation found a significant relationship between these constructs. A variety of mechanisms may serve as buffers between burnout and programmatic service delivery, such as coping skills, career-sustaining behaviors, emotional intelligence, grit, or self-efficacy. Nonetheless, the identification of preventative skills or personal traits that inhibit the effects of burnout may lead to interventions to support school counselors’ work. Future research also can examine training interventions that target school counselors’ susceptibility to burnout or stress. A final research implication is the need to replicate and confirm our findings. Researchers might consider replicating this study with similar or different measures and data collection methods.

Implications for School Counseling Practitioners and Supervisors

The degree of perceived stress for participants in this study had a positive correlation with their degree of burnout. Furthermore, participants’ burnout negatively contributed to their level of direct student services. While this study included several limitations, these findings provide more evidence for the positive relationship between stress and burnout, in addition to the negative contribution burnout can have on the job functions of school counselors. In an effort to support direct student services, it would behoove school counselors to take steps to increase their awareness about their well-being, including symptoms of burnout, and seek support to address concerns as they arise. Additionally, school counselors’ failure to address burnout is an ethical concern (American Counseling Association, 2014). School counselors could utilize a self-assessment (i.e., Counselor Burnout Inventory [Lee et al., 2007] or Professional Quality of Life Scale [Stamm, 2010]) to examine their level of burnout and subsequently address their work functions and lifestyle to alleviate symptoms.

As Moyer (2011) pointed out, supervision plays a vital role in school counselor development and can be a way to alleviate burnout. Thus, supervisors can provide opportunities for school counselors to learn ways to assess their well-being with the aim of developing career-sustaining behaviors to prevent burnout. For example, supervisors can inform school counselors of available screening measures and provide resources to aid in the development of career-sustaining behaviors. Similarly, supervisors can create activities (Lambie, 2006) that assess school counselors’ well-being, which allows counselors to address negative feelings. Efforts made to prevent burnout may increase the chances of school counselors performing direct student services. Higher rates of direct student services, such as individual and group counseling, also may lead to better educational outcomes for students (Lapan, 2012).

In an effort to reduce school counselors’ burnout and potentially increase their delivery of direct student services, practitioners and supervisors can initiate wellness-related activities. Butler and Constantine (2005) noted that peer supervision or consultation along with social support from colleagues and administrators might be helpful for reducing the effects of burnout. Furthermore, Lawson and Myers (2011) reported on the highest rated career-sustaining behavior, which provides potential to support the wellness of school counseling practitioners. As Meyer and Ponton (2006) noted, counselors as a whole tend to put their own wellness to the side in order to provide services to their clients. Therefore, another consideration for school districts and school counseling organizations is to offer wellness-focused training that could raise attention to counselors’ level of stress and burnout and provide strategies to enhance their wellness. Additionally, school counselors should remember to advocate for the profession and for themselves (Young & Lambie, 2007). It is important that administrators understand the critical wellness needs of school counselors, and school counselors should be among the first to advocate for this cause. As these findings indicate, there is a relationship between burnout and the quality of services offered by school counselors. Therefore, it is important that counselors “learn to be their own advocates and help dysfunctional workplaces become well” (Young & Lambie, 2007, p. 99).

In summary, this study examined the association of practicing school counselors’ degree of burnout, perceived stress and frequency of direct student services. The findings indicated that higher levels of burnout contribute to a decreased frequency of direct student services. Furthermore, school counselors’ perceived stress does not contribute to their facilitation of direct student services, but was positively associated with burnout. Overall, these findings are encouraging because the descriptive statistics indicate that school counselors operate at a low level of burnout and perceived stress and provide a moderate to high frequency of direct student services.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

References

American Counseling Association. (2014). 2014 ACA code of ethics. Alexandria, VA: Author.

American School Counselor Association. (1999). The role of the professional school counselor. Alexandria, VA: Author.

American School Counselor Association. (2003). The ASCA National Model: A framework for school counseling programs (1st ed.). Alexandria, VA: Author.

American School Counselor Association. (2012). The ASCA National Model: A framework for school counseling programs (3rd ed.). Alexandria, VA: Author.

Baggerly, J., & Osborn, D. (2006). School counselors’ career satisfaction and commitment: Correlates and predictors. Professional School Counseling, 9, 197–205. doi:10.5330/prsc.18.1.428818712j5k8677

Barnett, V., & Lewis, T. (1994). Outliers in statistical data (Vol. 3). New York, NY: Wiley.

Burnham, J. J., & Jackson, C. M. (2000). School counselor roles: Discrepancies between actual practice and exis-ting models. Professional School Counseling, 4, 41–49.

Butler, S. K., & Constantine, M. G. (2005). Collective self-esteem and burnout in professional school counselors. Professional School Counseling, 9, 55–62. doi:10.5330/prsc.9.1.17n4415l163720u5

Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. New York, NY: Routledge.

Cannon, W. B. (1935). Stresses and strains of homeostasis. The American Journal of the Medical Sciences, 189, 13–14.

Carey, J., & Dimmitt, C. (2012). School counseling and student outcomes: Summary of six statewide studies. Professional School Counseling, 16, 146–153. doi:10.5330/PSC.n.2012-16.146

Chao, R. C. (2011). Managing stress and maintaining well-being: Social support, problem-focused coping, and avoidant coping. Journal of Counseling & Development, 89, 338–348.

doi:10.1002/j.1556-6678.2011.tb00098.x

Clemens, E. V., Milsom, A., & Cashwell, C. S. (2009). Using leader-member exchange theory to examine prin-cipal-school counselor relationships, school counselors’ roles, job satisfaction, and turnover intentions. Professional School Counseling, 13, 75–85. doi:10.5330/PSC.n.2010-13.75

Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 385–396.

Cordes, C. L., & Dougherty, T. W. (1993). A review and an integration of research on job burnout. The Academy of Management Review, 18, 621–656.

Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29.

Daire, A. P., Dominguez, V. N., Carlson, R. G., & Case-Pease, J. (2014). Family Adjustment Measure: Scale construction and validation. Measurement and Evaluation in Counseling and Development, 47, 91–101. doi:10.1177/0748175614522270

DeMato, D. S., & Curcio, C. C. (2004). Job satisfaction of elementary school counselors: A new look. Professional School Counseling, 7, 236–245.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method (3rd ed.). Hoboken, NJ: Wiley.

Dimmitt, C., & Wilkerson, B. (2012). Comprehensive school counseling in Rhode Island: Access to services and student outcomes. Professional School Counseling, 16, 125–135. doi:10.5330/PSC.n.2012-16.125

Ernst, K. A. (2012). Self-efficacy, attachment, and school counselor service delivery. Unpublished Doctoral Dissertation, George Washington University.

Freudenberger, H. J. (1974). Staff burn-out. Journal of Social Issues, 30, 159–165. doi:10.1111/j.1540-4560.1974.tb00706.x

Freudenberger, H. J. (1986). The issues of staff burnout in therapeutic communities. Journal of Psychoactive Drugs, 18, 247–251. doi:10.1080/02791072.1986.10472354

Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: An introduction (8th ed.). Boston, MA: Pearson.

Garman, A. N., Corrigan, P. W., & Morris, S. (2002). Staff burnout and patient satisfaction: Evidence of relation-ships at the care unit level. Journal of Occupational Health Psychology, 7, 235–241.
doi:10.1037/1076-8998.7.3.235

Gnilka, P. B., Karpinski, A. C., & Smith, H. J. (2015). Factor structure of the Counselor Burnout Inventory in a sample of professional school counselors. Measurement and Evaluation in Counseling and Development, 48, 177–191.

Hair, J. F., Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Pearson.

Harris, H. L. (2013). A national survey of school counselors’ perceptions of multiracial students. Professional School Counseling, 17, 1–19. doi:10.5330/PSC.n.2013-17.1

Henson, R. K. (2006). Effect-size measures and meta-analytic thinking in counseling psychology research. The Counseling Psychologist, 34, 601–629. doi:10.1177/0011000005283558

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

Hu, L., Bentler, P. M., & Kano, Y. (1992). Can test statistics in covariance structure analysis be trusted? Psycho-logical Bulletin, 112, 351–362.

Ivancevich, J. M., & Matteson, M. T. (1980). Stress and work: A managerial perspective. Glenview, IL: Scott Foresman.

Janis, I. L., & Mann, L. (1977). Decision making: A psychological analysis of conflict, choice, and commitment. New York, NY: Free Press.

Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.

Lambie, G. W. (2006). Burnout prevention: A humanistic perspective and structured group supervision activity. The Journal of Humanistic Counseling, Education and Development, 45, 32–44.

Lambie, G. W. (2007). The contribution of ego development level to burnout in school counselors: Implications

for professional school counseling. Journal of Counseling & Development, 85, 82–88.

doi:10.1002/j.1556-6678.2007.tb00447.x

Landrum, B., Knight, D. K., & Flynn, P. M. (2012). The impact of organizational stress and burnout on client engagement. Journal of Substance Abuse Treatment, 42, 222–230. doi:10.1016/j.jsat.2011.10.011

Lapan, R. T. (2012). Comprehensive school counseling programs: In some schools for some students but not in all schools for all students. Professional School Counseling, 16, 84–88. doi:10.5330/PSC.n.2012-16.84

Lapan, R. T., Gysbers, N. C., & Petroski, G. F. (2001). Helping seventh graders be safe and successful: A state-wide study of the impact of comprehensive guidance and counseling programs. Journal of Counseling & Development, 79, 320–330. doi:10.1002/j.1556-6676.2001.tb01977.x

Lawson, G. (2007). Counselor wellness and impairment: A national survey. The Journal of Humanistic Counseling, Education and Development, 46, 20–34. doi:10.1002/j.2161-1939.2007.tb00023.x

Lawson, G., & Myers, J. E. (2011). Wellness, professional quality of life, and career-sustaining behaviors: What keeps us well? Journal of Counseling & Development, 89, 163–171. doi:10.1002/j.1556-6678.2011.tb00074.x

Lawson, G., & Venart, B. (2005). Preventing counselor impairment: Vulnerability, wellness, and resilience. In G. R. Walz & R. K. Yep (Eds.), Vistas: Compelling perspectives on counseling 2005 (pp. 243–246). Alexandria, VA: American Counseling Association.

Lazarus, R. S. (2006). Stress and emotion: A new synthesis. New York, NY: Springer.

Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York, NY: Springer.

Lee, S. M., Baker, C. R., Cho, S. H., Heckathorn, D. E., Holland, M. W., Newgent, R. A., . . . Yu, K. (2007). Devel-opment and initial psychometrics of the Counselor Burnout Inventory. Measurement and Evaluation in Counseling and Development, 40, 142–154.

Lee, S. M., Cho, S. H., Kissinger, D. and Ogle, N. T. (2010). A typology of burnout in professional counselors. Journal of Counseling & Development, 88, 131–138. doi:10.1002/j.1556-6678.2010.tb00001.x

Lei, M., & Lomax, R. G. (2005). The effect of varying degrees of nonnormality in structural equation modeling. Structural Equation Modeling, 12, 1–27. doi:10.1207/s15328007sem1201_1

Maslach, C. (1976). Burned-out. Human Behavior, 5(9), 16–22.

Maslach, C. (2003). Burnout: The cost of caring. Cambridge, MA: Malor Books.

Maslach, C., & Jackson, S. E. (1981). MBI research edition manual. Palo Alto, CA: Consulting Psychologists Press.

McCarthy, C., Kerne, V. V. H., Calfa, N. A., Lambert, R. G., & Guzmán, M. (2010). An exploration of school counselors’ demands and resources: Relationship to stress, biographic, and caseload characteristics. Professional School Counseling, 13, 146–158. doi:10.5330/PSC.n.2010-13.146

McCarthy, C. J., & Lambert, R. G. (2008). Counselor appraisal of resources and demands. Charlotte, NC: Center for Educational Measurement and Evaluation.

McGrath, J. E. (1976). Stress and behavior in organizations. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 1351–1395). Chicago, IL: Rand McNally.

Meyer, D., & Ponton, R. (2006). The healthy tree: A metaphorical perspective of counselor well-being. Journal of Mental Health Counseling, 28, 189–201. doi:10.17744/mehc.28.3.0341ly2tyq9mwk7b

Moyer, M. (2011). Effects of non-guidance activities, supervision, and student-to-counselor ratios on school counselor burnout. Journal of School Counseling, 9(5). Retrieved from http://jsc.montana.edu/articles/v9n5.pdf

Mullen, P. R., & Lambie, G. W. (2016). The contribution of school counselors’ self-efficacy to their programmatic service delivery. Psychology in the Schools, 53, 306–320.

Mullen, P. R., Lambie, G. W., & Conley, A. H. (2014). Development of the Ethical and Legal Issues in Counsel-

ing Self-Efficacy Scale. Measurement and Evaluation in Counseling and Development, 47, 62–78. doi:10.1177/0748175613513807

Olsson, U. H., Foss, T., Troye, S. V., & Howell, R. D. (2000). The performance of ML, GLS, and WLS estimation in structural equation modeling under conditions of misspecification and nonnormality. Structural Equation Modeling, 7, 557–595. doi:10.1207/S15328007SEM0704_3

Osborne, J. W. (2012). Best practices in data cleaning: A complete guide to everything you need to do before and after collecting your data. Thousand Oaks, CA: Sage.

Osborne, J. W., & Overbay, A. (2004). The power of outliers (and why researchers should always check for them). Practical Assessment, Research & Evaluation, 9(6). Retrieved from http://PAREonline.net/getvn.asp?v=9&n=6

Pines, A., & Maslach, C. (1978). Characteristics of staff burnout in mental health settings. Psychiatric Services, 29, 233–237. doi:10.1176/ps.29.4.233

Puig, A., Baggs, A., Mixon, K., Park, Y. M., Kim, B. Y., & Lee, S. M. (2012). Relationship between job burnout and personal wellness in mental health professionals. Journal of Employment Counseling, 49, 98–109. doi:10.1002/j.2161-1920.2012.00010.x

Pyne, J. R. (2011). Comprehensive school counseling programs, job satisfaction, and the ASCA National Model. Professional School Counseling, 15, 88–97. doi:10.5330/PSC.n.2011-15.88

Qualtrics. (2013). Qualtrics software (Version 37,892) [Computer software]. Provo, UT: Qualtrics Research Suite.

Rayle, A. D. (2006). Do school counselors matter? Mattering as a moderator between job stress and job satisfac-tion. Professional School Counseling, 9(3), 206–215. doi:10.5330/prsc.9.3.w23j476w45727537

Salas, E., Driskell, J. E., & Hughes, S. (1996). Introduction: the study of stress and human performance. In J. E. Driskell and E. Salas (Eds.), Stress and human performance (pp. 1–46). Mahwah, NJ: Erlbaum.

Sapolsky, R. M. (2004). Why zebras don’t get ulcers (3rd ed.). New York, NY: Holt.

Scarborough, J. (2005). The school counselor activity rating scale: An instrument for gathering process data. Professional School Counseling, 8, 274–283.

Scarborough, J. L., & Culbreth, J. R. (2008). Examining discrepancies between actual and preferred practice of
school counselors. Journal of Counseling & Development, 86, 446–459.
doi:10.1002/j.1556-6678.2008.tb00533.x

Schaufeli, W. B., & Enzmann, D. (1998). The burnout companion to study and practice: A critical analysis. Washington, DC: Taylor & Francis.

Schaufeli, W. B., Leiter, M. P., & Maslach, C. (2009). Burnout: 35 years of research and practice. Career Develop-ment International, 14, 204–220. doi:10.1108/13620430910966406

Selye, H. (1936). A syndrome produced by diverse nocuous agents. Nature, 138, 32.

Selye, H. (1980). The stress concept today. In I. L. Kutach & L. B. Schlesinger (Eds.), Handbook on stress and anxiety:

Contemporary knowledge, theory, and treatment (pp. 127–143). San Francisco, CA: Jossey-Bass.

Shillingford, M. A., & Lambie, G. W. (2010). Contribution of professional school counselors’ values and leader-

ship practices to their programmatic service delivery. Professional School Counseling, 13, 208–217. doi:10.5330/PSC.n.2010-13.208

Sink, C. A., & Stroh, H. R. (2006). Practical significance: The use of effect sizes in school counseling research. Pro-fessional School Counseling, 9, 401–411. doi:10.5330/prsc.9.4.283746k664204023

Stamm, B. H. (2010). The Concise ProQOL Manual. Pocatello, ID: Author.

Stevens, J. P. (1992). Applied multivariate statistics for the social sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

Streiner, D. L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80, 99–103. doi:10.1207/S15327752JPA8001_18

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Pearson.

Weston, R., & Gore, P. A., Jr. (2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34, 719–751. doi:10.1177/0011000006286345

Whiston, S. C., Tai, W. L., Rahardja, D., & Eder, K. (2011). School counseling outcome: A meta-analytic examin-ation of interventions. Journal of Counseling & Development, 89, 37–55.
doi:10.1002/j.1556-6678.2011.tb00059.x

Wilkerson, K., & Bellini, J. (2006). Intrapersonal and organizational factors associated with burnout among school counselors. Journal of Counseling & Development, 84, 440–450.
doi:10.1002/j.1556-6678.2006.tb00428.x

Wilkerson, K., Pérusse, R., & Hughes, A. (2013). Comprehensive school counseling programs and student achievement outcomes: A comparative analysis of RAMP versus non-RAMP schools. Professional School Counseling, 16, 172–184. doi:10.5330/PSC.n.2013-16.172

Young, M. E., & Lambie, G. W. (2007). Wellness in school and mental health systems: Organizational influences.
The Journal of Humanistic Counseling, 46, 98–113. doi:10.1002/j.2161-1939.2007.tb00028.x

Patrick R. Mullen, NCC, is an Assistant Professor at the College of William and Mary. Daniel Gutierrez, NCC, is an Assistant Professor at the University of North Carolina – Charlotte. Correspondence can be addressed to Patrick Mullen, School of Education, P.O. Box 8795, College of William & Mary, Williamsburg, VA 23188, prmullen@wm.edu.

« Older Entries

Next Entries »

The Technology Acceptance Model (TAM): Exploring School Counselors’ Acceptance and Use of Naviance

Burnout and Implications for Professional School Counselors

The Role of High School and College Counselors in Supporting the Psychosocial and Emotional Needs of Latinx First-Generation College Students

Career Counseling in Middle Schools: A Study of School Counselor Self-Efficacy

Burnout, Stress and Direct Student Services Among School Counselors

Recent Publications