The U.S. Workforce from 1960 to 2010: A RIASEC View

Feb 9, 2015 | Article, Volume 5 - Issue 1

Mary-Catherine McClain, Robert C. Reardon

In this article, the authors analyze ways of categorizing civilian occupations and employment data collected by the U.S. Census Bureau over 6 decades (1960–2010) with respect to six kinds of work (Holland’s RIASEC classification), occupational titles used, employment and income. O*NET provided data for the 2010 census regarding employment and income. The authors discuss the distribution of employment changes over time and the examination of findings in relation to science, technology, engineering and mathematics fields. The article concludes with practical implications for counseling and guidance practice.

Keywords: Holland, RIASEC, census, employment, occupations, income

Holland’s (1997) RIASEC theory is generally recognized as one of the most important and influential in the field of counseling and career development. Foutch, McHugh, Bertoch, and Reardon (2014) sought to verify such an observation by using bibliographic research tools and identified all publications based on this theory from 1953–2011. They found over 1,970 reference citations to Holland’s theory and applications, and categorized them in terms of practice, specific populations (e.g., K–12), instruments, diverse populations and theory. These citations appeared in 275 publications (e.g., books, journals, periodicals, reports) produced in varied professional fields and disciplines worldwide.

Many counselors know relatively more about Holland’s RIASEC personality typology than corresponding environmental models (Reardon & Lenz, 1998). From the outset, Holland believed that the environmental aspects of the typology needed further examination (Weinrach, 1980). Occupations, fields of study or academic disciplines, organizations, leisure activities, and jobs (positions) are aspects of the environment included in the theory. In this article, we address the interaction between RIASEC theory and the environment by examining 2010 census data and updating prior studies of occupational employment in 1960, 1970, 1980, 1990 and 2000 in relation to RIASEC codes and Holland’s theory (Reardon, Bullock, & Meyer, 2007).

Various people contemplating career decisions can benefit from understanding the scope and nature of the labor force and employment from this psychological, counseling-based point of view. Moreover, given characteristics of the contemporary U.S. economy, it is important to know how the distribution of jobs is changing over time. For example, the distribution of jobs across the RIASEC categories has changed from 1960–2010 in some ways, but not in others. An analysis of occupational employment, then, can be beneficial to counselors and career services providers assisting those who are unemployed, displaced or exploring the labor force. This work is important for both theoretical and practical reasons. For example, the number of annual job openings is strongly related to the number of people currently working in an occupation, so knowing the number employed is of practical importance in job hunting because of the need to replace workers.

Authors of recent literature have identified concerns about the use of outmoded concepts such as occupation in career/life counseling at a time of unprecedented socioeconomic change in the global economy. For example, Savickas et al. (2009) noted that new social arrangements for work and the digital revolution have led to unstable occupations and frequent job transitions for individuals: “Today, occupational prospects seem far less definable and predictable, with job transitions more frequent and difficult” (Savickas et al., 2009, p. 240). Sampson and Reardon (2011) summarized these ideas: “Occupations have changed in fundamental ways as technology and globalization have reshaped the workplace. Occupations have become fluid and organizations are evolving rapidly, adapting their workforce to respond to a rapidly evolving marketplace” (p. 41). We agree that some occupations are changing but conclude that the concept of an occupation remains common and useful in the social sciences as a way of categorizing work activities and employment.

In contrast to this view, Murray (2012) suggested that the workplace has not transformed for the 82% of American workers in occupations other than managerial professional positions. Teachers, police, plumbing contractors, insurance agents and carpenters have the same duties and routines that these occupations have always required, although some of the work tasks may have been affected by technology. Sampson and Reardon (2011) noted that the perception of massive occupational change has been exacerbated by inaccuracies in media presentations and the failure to use career theory to examine occupational changes.

In the present article, we examine occupational information using Holland’s (1997) RIASEC theory. This theory rests on four basic assumptions: (a) individuals can be categorized into Realistic (R), Investigative (I), Artistic (A), Social (S), Enterprising (E) and Conventional (C) types; (b) environments (i.e., occupations) also can be categorized into these same six types; (c) individuals tend to choose environments that fit their personality types; and (d) behavior is determined by the fit between an individual’s personality and environment. Examination of the occupational titles used to describe current work in the United States, including information about employment and income, can increase understanding of the workplace from this theoretical perspective.

Holland’s (1997) typological theory specifies a theoretical connection between vocational personalities and work environments that makes it possible to use the same RIASEC classification system for both persons and occupations. Many inventories and assessment tools also use the typology to enable individuals to categorize their interests and personal characteristics in terms of the six types and combinations of the types. These six types are briefly defined as follows:

1. Realistic (R) types are found in occupations such as auto mechanic, surveyor, electrician and farmer. The R type usually has mechanical and athletic abilities, likes to work outdoors and with tools and machines, and might be described as conforming, hardheaded, honest, humble, materialistic, practical and thrifty.
2. Investigative (I) types like occupations such as a biologist, chemist, geologist, anthropologist and medical technician. The I type usually has math and science abilities, and likes to work alone and to solve problems. The I type might be described as analytical, critical, curious, independent, intellectual, pessimistic and rational.
3. Artistic (A) types are found in occupations such as musician, dancer, interior decorator, actor and writer. The A type usually has artistic skills, enjoys creating original work and has a good imagination. The A type may be described as disorderly, emotional, idealistic, imaginative, impulsive, independent, introspective and original.
4. Social (S) types like occupations such as teacher, speech therapist, counselor, clinical psychologist and nurse. The S type generally likes to help, teach and counsel people, and may be described as friendly, generous, helpful, idealistic, kind, responsible, tactful, understanding and warm.
5. Enterprising (E) types like occupations such as buyer, sports promoter, business executive, salesperson, supervisor and manager. The E type usually has leadership and public speaking abilities, is interested in money and politics, and likes to influence people. The E type is described as acquisitive, ambitious, domineering, extroverted, optimistic, self-confident and sociable.
6. Conventional (C) types are found in occupations such as bookkeeper, financial analyst, banker and secretary. The C type has clerical and math abilities, likes to work indoors and to organize things. The C type is described as conforming, efficient, obedient, orderly, persistent, practical and unimaginative.

The six RIASEC types are optimally represented by a circular order, also commonly referred to as the hexagonal model. Holland’s (1997) structure of the six types as a hexagon is one of the most well-replicated findings in the history of vocational psychology (Rounds, 1995). The six domains are arranged according to their relative similarity in a hexagonal formation of R-I-A-S-E-C. For example, according to Holland’s theory, the Social and Enterprising types appear in adjacent positions on the hexagon because they are alike; in contrast, the Social and Realistic types are dissimilar and appear in opposite positions from one another on the hexagon.

Prior Studies

In the early 1970s, researchers began to examine the U.S. labor market using the RIASEC classification system (Reardon et al., 2007), and the present study is a continuation of that line of research. Using data provided by the decennial census in 1960, 1970 and 1980, researchers (G. D. Gottfredson & Daiger, 1977; G. D. Gottfredson & Holland, 1996; G. D. Gottfredson, Holland, & Gottfredson, 1975; L. S. Gottfredson, 1978; L. S. Gottfredson, 1980; L. S. Gottfredson & Brown, 1978) analyzed U.S. employment patterns using Holland’s theory. These studies examined a number of variables with respect to the Holland RIASEC classification, including the percentages of men and women working in hundreds of occupations, salaries earned during the preceding year by incumbents, educational and training levels associated with occupations, occupational prestige, and the education levels or cognitive complexity ratings for occupations. These studies provided practitioners and scholars with more theory-based, detailed information about work environments and the characteristics of workers.

After a 15-year hiatus in research on census employment and Holland codes, Reardon, Vernick, and Reed (2004) analyzed the 1990 census data in relation to data from 1960, 1970 and 1980. They considered the variables of gender, income and cognitive complexity and reported stability in the census data for occupational titles and six kinds of work from 1960–1990. For example, the Realistic area included many more named occupations in the census than the other five areas, averaging between 46% and 50% of all named occupations over the 40-year period.

Reardon et al. (2004) found that while employment declined by 18% in the Realistic area relative to other Holland types, it remained the largest area of employment and actually increased in real numbers through 1990. Only 1% of employment was in the Artistic area. Reardon et al. (2004) also reported marked differences in employment between men and women across the six areas from 1960–1990. Reardon et al. (2004) further examined income and gender by six kinds of work and found that the average income profile ranging from highest to lowest was IESARC. The discrepancy across the six areas was very large, with the average Investigative income being two times the average Conventional income.

In a later study, Reardon et al. (2007) examined trends in labor market characteristics using census data from 1960–2000. They found stability in occupational constructs for six kinds of work from 1960–2000; for instance, the Realistic area included many more named occupations in the census than the other five areas, ranging between 43% and 50% of all occupations included over the five census periods. Reardon et al. (2007) reported that although employment in the Realistic area declined by 25% from 1960–2000, this area remained the largest area of employment and actually increased in real numbers from 1960–2000. As before, only 1% of employment was in the Artistic area. Finally, Reardon et al. (2007) examined income and gender by kinds of work and found that the average income profile for six kinds of work ranging from highest to lowest was IESARC in 1990 and ISEARC in 2000. The discrepancy across the six areas was very large, with the average Investigative income about twice as large as the average Conventional income.

In summary, the data included in these studies (Reardon et al., 2007; Reardon et al., 2004) were unique in several ways and have special implications for counselors. First, as an independent branch of the federal government, the U.S. Census Bureau reported actual numbers of people working in different occupations based on an accounting of persons in households. Second, these data provided a retrospective look at the labor markets, and by examining them over time it was possible to view changes in the economic lives of persons in the United States. Third, the occupational titles included in the census have remained constant over the years, reinforcing the use of the occupational schema in matching persons and environments. Fourth, these studies were conducted by researchers in the counseling field rather than economists or sociologists, which helps counselors use occupational data organized by Holland codes to illustrate and explain where jobs exist in relation to their clients’ interests. For example, a client may have a strong interest in Artistic occupations, and census data may help a counselor explain the relatively small number of persons working in Artistic fields.

The Present Study

We examined the employment trends reported in earlier research and added a new analysis based on the 2010 census and O*NET data. Research questions included the following:

1. What were the numbers of occupational titles reported in the census from 1960–2010 relative to the six areas of work?
2. What were the numbers and percentages of occupational employment in 1960, 1970, 1980, 1990, 2000 and 2010 in relation to six kinds of work?
3. What were the mean incomes for six different kinds of work in 1990, 2000 and 2010?

Methods

Procedures and Research Tools

Varied procedures have been used to collect occupational data for the decennial census over the past 6 decades.

1960, 1970, 1980 census. In the 1960 census, the sampling unit was the housing unit, or the person in the case of group housing. This method provided information about 297 detailed occupational categories. L. S. Gottfredson and Brown (1978) described the methods they used to derive Holland codes for the 1960 census data using 1970 census data as a point of reference. In the 1970 census, the sampling unit again was the housing unit, and 440 detailed occupational titles were included in these data, 143 more than in 1960. As with the 1960 census, the data included only employed persons and excluded members of the armed forces. G. D. Gottfredson, Holland, and Gottfredson (1975) analyzed data from the 1970 census involving 424 occupations, and excluded men (5.6%) and women (6.6%) not classified according to one of the detailed occupations. Information about the 1980 census was taken primarily from G. D. Gottfredson and Holland (1989) and G. D. Gottfredson (1984). The 1980 analysis was based on 503 selected occupations.

1990 census. Comprehensive information about the 1990 census was provided by the U.S. Census Bureau (1992a, 1992b), and was based on 500 selected occupations. G. D. Gottfredson and Holland (1996) indicated that this classification was most closely related to the Standard Occupational Classification (SOC; U.S. Department of Commerce, 1980). The U.S. population count in 1990 was 283,928,233 (U.S. Census Bureau, 1992b). Four new categories of work were added to the 1990 census while six from the 1980 census were eliminated.

2000 census. The 2000 census counted 281,421,906 people in the 50 states and the District of Columbia. As in the past, short and long forms were used with about 17% (1 in 6 households) receiving the latter (U.S. Census Bureau, 2002). The 2000 census included 471 occupations classified using the SOC (U.S. Department of Labor, 2000).

2010 census. The 2010 census counted 308,745,538 people in the 50 states and the District of Columbia. This census included 539 occupations, including those with “all other” titles representing occupations with a wide range of characteristics not fitting into one of the O*NET detailed occupations. However, as in prior studies, the focus was on the detailed occupations in the 2010 census (N = 494) and excluded military-based occupations.

The information collected in the 2010 census was based on the short form rather than the long form, which means that demographic information about gender, salary and age was not collected relative to occupations. Lowe (2010) noted that the introduction of the American Community Survey by the U.S. Census Bureau provided the most sweeping change in census data collection in 60 years. The American Community Survey is a nationwide, continuous survey designed to provide reliable and timely demographic housing, social and economic data every year, in contrast to the long form, which provided data only at the beginning of each decade.

After locating the 2010 census data from the U.S. Census Bureau with lists of occupations categorized with census and SOC codes, we organized the information into a spreadsheet using the following headings: Occupation 2010 category description (e.g., management occupations), Occupation (e.g., chief executive), 2010 Census Code (e.g., 0010) and 2010 SOC Code (e.g., 11-1011). Additional columns were created to incorporate Holland code information for 2010 employment data and mean annual wages. The SOC (U.S. Department of Labor, 2010) was used to collect employment and salary information from the O*NET system (http://online.onetcenter.org/). O*NET is a comprehensive database that provides information on 780 occupations, worker skills and job training requirements. O*NET is sponsored by the U.S. Department of Labor’s Employment and Training Administration. The Self-Directed Search Occupations Finder—Revised Edition (OF; Holland & PAR Staff, 2010) and the Dictionary of Holland Occupational Codes (DHOC; G. D. Gottfredson & Holland, 1996) also were used to obtain the first-letter Holland code for each specific occupation. Employment data and mean annual wages for census occupations were found in O*NET.

A Note about Data Analysis

Previous studies of employment using census data and Holland codes have reported frequency and percentage distributions, and this study continued that approach. As earlier researchers have noted (G. D. Gottfredson & Daiger, 1977), the sample sizes are so large that the magnitude of observed differences is more important than statistical differences. Rounded numbers are used in this report to the nearest percent or thousand in order to avoid communicating a misplaced sense of precision in the findings.

Results

Occupational Titles in the Census for Six Kinds of Work, 1960–2010

For the first question, we examined the number of occupational titles used in the census and O*NET, and categorized these in relation to the six areas of work. Occupational titles provide schemas for career exploration using Holland’s (1997) RIASEC codes—tools for the exploration and examination of occupational information. As in previous studies (Reardon et al., 2007), the Realistic area included many more named occupations in the census than the other five areas (see Table 1, updated with 2010 data). For example, the 2010 census specified 211 occupations in the Realistic area and 283 occupations in the other five areas combined. Only 19 occupations were identified in the Artistic area. Overall, occupations in the Enterprising area increased from 27 in 1960 to 88 in 2010. Finally, 282 occupations were included in the 1960 analysis, which increased to 465 in 1970, 502 in 1980 and 500 in 1990, dropped to 434 in 2000, and increased again to 494 in 2010.

Table 1 also shows that occupational titles were not equally distributed across the six areas of work over the past six decades and have changed very little during this period. For example, the Realistic area consistently has the most occupational titles and the Artistic area the fewest. Figure 1 shows the average percentages of occupational titles in the census across six decades, as follows: Realistic 46%, Investigative 12%, Artistic 3%, Social 12%, Enterprising 18% and Conventional 9%. This distribution is similar to what was found in 2010: Realistic 43%, Investigative 11%, Artistic 4%, Social 13%, Enterprising 18% and Conventional 11%. Figure 1 shows in graphic form that the schemas used to describe work activities in the U.S. economy have remained relatively stable over 6 decades.

Employment in Six Kinds of Work, 1960–2010

In analyzing U.S. employment data over 6 decades, we focused on the detailed occupations as in previous census studies (Reardon et al., 2007). Table 2 indicates that the total estimated employment increased over the 6 decades from 64.1 million in 1960 to 119.8 million in 2010. Table 2 and Figure 2 reveal that the percentage of Realistic employment declined 28% from 1960–2010, an average of about 4.7% for each decade. However, and in spite of this decline, the Realistic area showed that 31.9 million persons were employed in 2010, and the Realistic area had the highest level of employment across the six RIASEC areas in each census period. The Artistic area had the fewest number employed in 2010 with 2.0 million. Table 2 and Figure 2 show that the percentage of employment in the Social area increased from 9% in 1960 to 24% in 2010, or 5.6 million to 29.6 million persons. During the same period, employment in the Investigative area increased from 3% in 1960 to 10% in 2010, or 2.0 million to 11.5 million persons. Employment in the other four areas remained more stable. Figure 2 graphically shows that the RIASEC employment profile for highest to lowest areas was RSECIA in 1960 compared to RECSIA in 2010. The Realistic, Investigative, and Artistic areas maintained their positions over this time period.

Table 2

Number and Percentage of Persons Employed in Six Kinds of Work, 1960–2010

Note. Employment numbers are in millions rounded to the nearest thousand.

^aUsed by permission of Academic Press Inc., Journal of Vocational Behavior, 10, p. 131. Copyright 1977 Academic Press Inc.

Income and Kinds of Work, 1990–2010

For the third question, we focused on mean income levels for persons employed in six kinds of work in 1990, 2000 and 2010. Inspection of Figure 3 shows the results of this analysis. These data reveal the continued discrepancy with regard to income among the Holland types across the three most recent census periods. The RIASEC profiles for highest to lowest income were IESARC, ISEARC and IEASRC in 1990, 2000 and 2010, respectively. The Investigative area consistently showed the highest income levels over the 3 decades, while the Conventional and Realistic areas tended to show the lowest. The average income over the 3 decades for the Investigative area was $54,587, compared to Conventional, $28,047 and Realistic, $27,981. Data in Figure 3 continue to show wide variations in income levels among the six RIASEC groups. For example, in 2010 the income in the Investigative area was almost double that of the Conventional area.

Discussion

The principal findings of this study are examined in terms of the three questions that guided the research, followed by a discussion of the study’s limitations and implications for counseling practice.

Occupational Titles by RIASEC Code

Information about jobs and employment used in counseling may be affected by the uneven distribution of occupational titles describing work across RIASEC areas. Over 60% of the titles used in 2010 were in the Realistic and Enterprising areas, and this distribution has been consistent over the past 6 decades. It is noteworthy that the percentages of occupational titles in the Investigative and Conventional areas in 2010 were the same, but the income reported across the six areas was the most discrepant between these two areas. Labor market information across the six areas is not always equivalent.

G. D. Gottfredson and Holland (1989) reported that the Dictionary of Occupational Titles also showed variations in the distribution of RIASEC codes for occupational titles. They were interested in the number of times each RIASEC letter appeared somewhere in the three-letter code for each occupation in the Dictionary of Occupational Titles and reported the following: Realistic, 10,708 times; Investigative, 2,551; Artistic, 570; Social, 6,606; Enterprising, 10,405; and Conventional, 5,999. These data reveal that upon examining the world of work from a RIASEC perspective, counselors can obtain a theory-based view of work environments that is not equitable across the six areas. Counselors can use RIASEC codes to inform clients about work and to increase their understanding of occupations and employment.

Information about jobs and employing organizations changes more frequently than information about occupations (e.g., typical work duties, training requirements, working conditions; Reardon, Lenz, Sampson, & Peterson, 2012). Perhaps lessons from the field of general semantics (Johnson, 1946) can be useful here. For example, the word chair can communicate information about the arrangement of furniture in a room, but this word does not communicate everything known about chairs, which in reality may take many different forms and be built of varied kinds of materials. The same is true for occupational terms. There are many different carpenters working in varied job positions and for varied employers, but the term carpenter still has meaning in communications because it is generally understood that not all carpenters are the same.

Over the 6 decades of this analysis, the number of census occupations in each RIASEC area has been relatively static. This stability indicates that there is considerable permanence in the array of the named occupations in the census reports about the workforce. This finding is contrary to the observations by Savickas (2012) and Savickas et al. (2009) regarding instability of the concept of an occupation in the contemporary global economy.

Employment in Six Areas of Work

The findings of this study report both the numbers of persons employed and the percentages of employment across RIASEC areas for six census periods. We believe that information about employment in the past can be instructive for future career planning. Table 2 reports the numeric and percentage changes in employment over 6 decades. The table shows the actual number of persons employed according to the decennial censuses over the 60-year period. This table and Figure 2 show the percentage change­s—the distribution of the workforce within the RIASEC categories. Occupations that employ the largest numbers of people are in the Realistic, Social and Enterprising areas, with less employment in the Investigative and Artistic areas. The latter two areas report both the fewest numbers employed and the smallest percentages of employment across the six RIASEC areas.

The current emphasis on preparation for careers in science, technology, engineering and mathematics (STEM) fields involves occupations that do not employ large numbers of people. These occupations, often found in the Investigative and Artistic areas, employed relatively small numbers of people in 2010 compared to the other four areas: 12% versus 88%. The STEM fields are not big-growth occupational areas that employ many hundreds of thousands or millions of people (e.g., nurses, retail salespersons, office clerks, teachers). However, the STEM fields are generally characterized by fast growth that involves a few thousand or more persons (e.g., biomedical engineers, veterinary technicians, glaziers, physical therapists). Persons in these occupations typically have higher salaries and better employment opportunities (Horrigan, 2003–2004).

The findings of the present study indicate that most people are employed in Realistic, Enterprising and Conventional (REC) occupations. Public attention to employment and career preparation often is directed at occupations with code combinations in the Investigative, Artistic and Social (IAS) areas because the percentage rate of employment growth is often greater there than in the REC areas (Reardon et al., 2012). The IAS areas provide higher levels of prestige and income, but employ fewer people (Reardon et al., 2004). One must remember that these are projected new jobs, which seem to capture more public attention and interest than the census data regarding actual employment.

A large number of jobs actually involve replacement of older workers, perhaps as much as one-third of employment (Mittelhauser, 1998). The U.S. Bureau of Labor Statistics (BLS; 2012) used data from the Current Population Survey and found that the replacements provide many more job openings in most occupations than straight employment growth does.

While the census data provide information about past employment that can inform career planning, the U.S. BLS provides additional labor market forecast information based on occupational projections. Lockard and Wolf (2012) identified the 20 occupations expected to have the most job openings each year through 2020 (big-growth occupations). Four of these occupations—registered nurses, retail salespeople, home health aides and personal care aides—will add more than half a million jobs each through 2020. These occupations are not new, different or unique, and they are unrelated to STEM fields. Reardon et al. (2012) noted that the Holland summary code order for these 20 big-growth occupations was SREICA. In the current study, the profile for employment in the 2010 census was RECSIA. It is not surprising that the Realistic area is prominent in both of these projections, because according to the census, it is the area of largest employment in the economy.

Given the overall increase in actual employment from 64.1 million in 1960 to 119.8 million in 2010, there has been a corresponding increase in employment across the six areas of work. For example, the number of persons employed in the Investigative area has grown from over 1.9 million in 1960 to 11.5 million in 2010, and in the Social area from 5.6 million in 1960 to 29.6 million in 2010. Employment growth has been less dramatic in the Artistic, Enterprising and Conventional areas, and growth has declined slightly in the Realistic area from 35.0 million in 1960 to 31.9 million in 2010.

In addition, the percentages of the U.S. population employed in the six areas of work also have changed from 1960–2010, but in a less dramatic way (see Figure 2). For example, the employment percent profile from highest to lowest employment in 1960 was RECSIA, and in 2010 it was RSECIA, with only the ECS areas alternating in order. However, the percentage difference between the Realistic and Artistic areas was greater in 1960 (54%) than in 2010 (25%). This may be evidence of a decline in manufacturing.

Income Across Six Areas of Work

Our findings indicate that income is not equitable across the six RIASEC areas, with the Investigative area consistently having the highest income and the Realistic and Conventional areas the lowest. Research using census data by Huang and Pearce (2013), Reardon et al. (2004), and Reardon et al. (2007) revealed similar findings. We find that although the RIASEC schema is familiar to counselors using the Self-Directed Search, the Strong Interest Inventory and many other career assessments, the idea of using this schema to analyze occupational information is more novel. For example, thinking of income levels in terms of the RIASEC schema means using an order of IEASRC per the 2010 census data when discussing occupational information with clients.

Reardon et al. (2007) reported that examining levels of cognitive complexity associated with occupations may provide an explanation for the income disparity among the six RIASEC areas. G. D. Gottfredson and Holland (1996) created the Complexity Rating (Cx) to estimate the cognitive skill and ability associated with an occupation. In developing the Cx, the authors wanted to make greater use of job analysis ratings obtained by the U.S. BLS and also create a single measure of cognitive or substantive complexity associated with an occupation. They noted that cognitive complexity of work demands (G. D. Gottfredson & Holland, 1996) might be an appropriate term for the Cx. A Cx rating of 65 or higher could be associated with an occupation requiring a college degree and possibly postgraduate work and on-the-job training of 4–10 years, while a Cx level of 50 might characterize an occupation requiring a high school diploma and a year or more of on-the-job training. Reardon et al. (2007) found that Cx levels were highest in the Investigative and Artistic areas and that the Conventional area was associated with the lowest ratings. They found that employment in the Investigative area occurred only at the highest two levels of Cx (i.e., baccalaureate or higher) while the other four areas—Realistic, Social, Enterprising and Conventional—showed employment at all six levels of Cx.

Huang and Pearce (2013) reported that higher annual incomes in 2010 were found in occupations associated with greater Investigative and Enterprising traits. In addition, they found that the differentiation of an occupational interest profile positively predicted median annual income and moderated the effect across each of the six RIASEC areas. In other words, the more the occupation was characterized by a single, robust RIASEC code letter, the greater the income level for the occupation.

Reardon et al. (2007) examined income by kinds of work and found that the average income profile for six kinds of work ranging from highest to lowest was IESARC. In the current study, the income profile was almost identical—IEASRC. These findings are very similar to those reported by Huang and Pearce (2013). Given that the Investigative area of work requires more education and training than the other five areas, these findings from census data provide evidence that education pays. Reardon et al. (2012) reported that the unemployment rate is clearly related to educational attainment. Those with more education are less frequently unemployed and have higher weekly earnings—more education is connected to more income.

Limitations

As with earlier studies (Reardon et al., 2007; Reardon et al., 2004), several limitations in the present study should be noted. First, the occupational titles included in the census have changed only slightly over the years. The U.S. BLS conducts extensive research to determine whether a new occupation should be added to its list of detailed occupations. A new occupation is one that includes duties not previously identified, one that has been recognized in small numbers and continues to grow (e.g., now has its own professional association or trade group), or one that is evolving and whose tasks have changed significantly. These new occupations arise from technological advances, new laws or regulations, or changing demographics. However, we believe that this issue has minimal impact on the findings of the present study because changes in occupational codes are unlikely to affect the first letter of a code. First-letter codes of occupations are much more stable over time and across industries than second or third letters.

A second limitation of this study is related to the classification of hundreds of thousands of jobs into 350–500 occupational categories, which requires considerable judgment and skill by occupational analysts. These specialists base their judgments on the application of classification criteria, and there is the possibility of error in the use of this system of analysis. Third, we used the first letter in each Holland code in our analysis in order to simplify reporting. While this decision reduced some of the precision inherent in the Holland classification when three-letter codes are used, it increased the accuracy of occupational classification.

Fourth, our analysis was based on a sampling procedure used by the U.S. Census Bureau over 6 decades, and we generalized from this sample to the entire U.S. population. We assumed that the sampling procedure used by the U.S. Census Bureau was appropriate for this study. Fifth, the method for calculating the income levels reported in this study differed across the 3 decades, and comparisons should be made with caution. We used mean levels rather than median levels, and information about the skew of the distribution is not provided in this study. For example, Lowe (2010) noted that while the American Community Survey data are more current, they are not as precise (margins of error are generally higher) as data obtained in the long form used by the U.S. Census Bureau previously. Finally, it is possible that occupations may be shifting within or among industry groups, which would mask some of the findings regarding income reported in the present analysis.

Implications for Counseling Practice

Limitations notwithstanding, the results of this analysis of six kinds of work and employment over 6 decades have implications for counselors. Holland (1997) noted several rules to use in interpreting the Self-Directed Search interest inventory, such as the Rule of Asymmetrical Distribution of Types and Subtypes. This rule reminds both counselor and client that the distribution of types across the six RIASEC areas is very uneven and unequal; moreover, the distribution of jobs across the six types is not symmetrical or equal. Codes associated with small employment numbers may have fewer positions and fewer openings. The research in the present article underscores the validity of this rule. In each census period, the Artistic area was the smallest area of employment at 1% or 2%. At the other extreme, the Realistic area was the largest area of employment, ranging from 55% in 1960 to 27% in 2010. Career counselors should be cautious in advising workers to look for employment outside the Realistic area, because it has been the largest area of employment for the past 6 decades, with ongoing replacement needs (Reardon et al., 2007).

We can add that even the numbers of named census occupations are extremely uneven across six kinds of work. For example, the schema based on RIASEC types used in 2010 to examine occupations was heavily skewed in the direction of the Realistic area (N = 211), with very few occupational titles associated with the Artistic area (N = 19). We surmise that these findings reveal little evidence of instability and change in the use of the occupational schema by the U.S. Census Bureau, at least from a RIASEC perspective.

Some of these findings may be interpreted in different ways. For example, the Realistic area employed the most persons in 2010, but employment in that area has dropped 28% over the 6 decades. The loss of jobs in the Realistic area is greater than the changes in any other area, decreasing from 42.7 million in 1990 to 31.8 million in 2010. The Investigative area almost tripled in employment from 1960–2010, from 3% to 10%, but fewer than 10% of total U.S. jobs are in the Investigative area (11.5 million). These findings seem related to the issue of big-growth and fast-growth jobs described by Horrigan (2003–2004), in which very few occupations appear at the top of both lists. For example, only home health aides and personal care aides are included in both the top 20 big-growth and fast-growth employment areas. This information underscores the importance of understanding demography and an aging population in using labor market information. The information used in career guidance programs often touts the rapid growth in information and technology jobs; however, this information must be balanced with the understanding that only 8% of U.S. employment is in the Investigative area.

The findings of the current study can update and enhance a counselor’s view of labor market information based on Holland’s career theory. We suggest that a RIASEC perspective on jobs in the labor market indicates that things are not really changing as much as others sometimes discuss. U.S. census data compiled over 6 decades (1960–2010) can inform counseling practice and career interventions for students and others exploring occupational changes. These findings can assist counselors and their clients in better matching personal characteristics with occupational and work environments.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of

interest or funding contributions for

the development of this manuscript.

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Mary-Catherine McClain is a postdoctoral fellow at the University of Georgia. Robert C. Reardon is a professor emeritus at Florida State University. Correspondence can be addressed to Robert C. Reardon, FSU Career Center, PO Box 3064162, 100 South Woodward Avenue, Tallahassee, FL 32306-4162, rreardon@admin.fsu.edu.

Using Mindfulness-Based Cognitive Therapy in Individual Counseling to Reduce Stress and Increase Mindfulness: An Exploratory Study With Nursing Students

Feb 9, 2015 | Article, Volume 5 - Issue 1

Mark J. Schwarze, Edwin R. Gerler, Jr.

The purpose of this exploratory study was to investigate the effectiveness of a modified mindfulness-based cognitive therapy intervention using individual counseling sessions to reduce stress and increase levels of mindfulness among nursing students. An AB single-subject experimental design replicated three times was implemented. Results indicated reduced stress in two out of three participants and increased mindfulness levels in all participants. Implications for college counselors and counselors working with clients in high-stress occupations are provided. Additionally, the results show promise for the use of mindfulness-based cognitive therapy in individual counseling.

Keywords: mindfulness, mindfulness-based cognitive therapy, stress, college counselors, nursing, single-subject experimental design 

Mindfulness-based cognitive therapy (MBCT) has been described as part of a third generation of cognitive therapies (Harrington & Pickles, 2009). Along with dialectical behavioral therapy and others like it, MBCT has integrated the construct of mindfulness with standard cognitive-behavioral paradigms. MBCT found its origins in the work of Kabat-Zinn’s (1990) mindfulness-based stress reduction program. This 8-week group-based program consisted of Buddhist mindfulness mediation practices to help chronic pain sufferers reduce their stress associated with illness. MBCT has incorporated elements of mindfulness-based stress reduction and cognitive-behavioral therapy to help individuals become more aware of thoughts and feelings and put them into context as mental events rather than self-defining constructs (Teasdale et al., 2000).

Seeing a need for an intervention to help patients who had repeatedly relapsed into depression, Segal, Williams, and Teasdale (2002) formalized MBCT as a standardized program of therapy. Designed as an 8-week program with specific guidelines for each session, MBCT was originally conceived as a group modality. Clients are placed in classes to learn the mindfulness and cognitive-behavioral (Beck, Rush, Shaw, & Emery, 1979) skills needed to regulate emotions and thoughts. MBCT involves training the mind to avoid judgmental reactions to events, thoughts, feelings and body sensations and to practice nonjudgmental awareness and acceptance (Ma & Teasdale, 2004). The key component of MBCT is mindfulness.

Mindfulness, once an abstract concept in the counseling field, is reaching mainstream awareness and gaining more attention in the literature (Brown, Marquis, & Guiffrida, 2013). Derived from Zen Buddhism, mindfulness has been described as a commitment to bringing awareness back to the present moment (Harrington & Pickles, 2009). Brown and Ryan (2003) defined mindfulness as “the state of being attentive to and aware of what is taking place in the present” (p. 822). Despite a growing research base, mindfulness as a testable and operationally defined variable is still being shaped.

Bishop et al. (2004) proposed an operational definition of mindfulness as a two-component skill-building approach for responding to emotional and cognitive distress. The first component involves the self-regulation of attention. Measurable skills must be obtained to reach a successful level of self-regulation of attention, including sustained attention; switching, or bringing the attention back to a focal point; and inhibition of elaborative processing, which involves the ability to maintain a state of flexible and nonjudgmental focus and awareness over a period of time. The second component includes developing an orientation to experience. In this, all thoughts, feelings and sensations are acknowledged.

Mindfulness training works well in counseling in that it is a simple idea: staying focused on momentary experience (Grabovac, Lau, & Willett, 2011). The core strategy to teach clients is mindfulness meditation. Meditation has many forms but is ultimately the practiced skill of quieting the mind (Wright, 2007). Counselors trained in meditation can teach clients to sit quietly and observe thoughts and feelings without reaction or judgment (Brown et al., 2013). A version of this meditation is the 3-minute breathing space (Segal et al., 2002). This meditation approach is a core skill learned in MBCT. It utilizes the breathing techniques of meditation while attempting to bring awareness to present experience, focusing on breath as a mediator and expanding to other bodily sensations.

     Because mindfulness is rooted in Buddhist philosophy and belief, its inclusion in Western counseling paradigms has been slow. Most interventions and models consisting of mindfulness-based ideas have been stripped of the Eastern religious and philosophical foundations and presented as skill-based acquisitions (Baer, 2003). This change has increased acceptance of mindfulness-based approaches in mainstream treatment and educational venues. Specifically, using mindfulness to mitigate stress has been a benefit of this practice. One particular population that has historically reported high levels of stress is nursing students (Beddoe & Murphy, 2004).

Nursing Students and Stress

The Spring 2013 American College Health Association’s National College Health Assessment (American College Health Association, 2013) listed stress as the number-one impediment to academic success for college students. Specifically, college students training to be nurses at the university level are subjected to high levels of stress (Gibbons, Dempster, & Moutray, 2011). Pulido-Martos, Augusto-Landa, and Lopez-Zafra’s (2012) review of the literature on the nursing student experience found several factors leading to stress, including balancing home and academic demands, experiencing time management pressures and financial problems, lacking meaningful connections with the nursing faculty, and feeling unprepared and incompetent in clinical practice.

In addition, stress, combined with other issues, has led to significant attrition rates in nursing programs (Harris, Rosenberg, & O’Rourke, 2014). Stickney (2008) found that the number of new students in nursing programs is too low to ensure an adequate number of nurses to meet the future needs of health care agencies. Students in nursing programs experience significant amounts of stress from trying to balance their lives at home with academic responsibilities. It is imperative that counselors, especially those in college settings, are aware of effective and innovative interventions to help nursing students, as well as other students, reduce stress and be successful. MBCT has shown promise in helping people reduce negative emotions such as stress (Collard, Avny, & Boniwell, 2008; Teasdale et al., 2000).

This study utilized a modified version of MBCT in individual counseling sessions to teach and process MBCT core skills of mindfulness meditation and cognitive decentering. While MBCT has mostly been utilized in group formats, there is some argument that group counseling is not always the best approach. Kuyken et al. (2008) found that 5% of an eligible sample for their MBCT study declined participation because they did not like the group aspect of the intervention. Lau and Yu (2009) suggested that offering mindfulness-based treatments in an individual format might increase participation for those who are reluctant to be involved in group settings.

The purpose of this exploratory single-subject experimental study was to evaluate the effectiveness of using MBCT to help reduce stress among university nursing students. Nursing students were used because of their documented high levels of stress. The questions explored included whether using MBCT in individual sessions increases self-reported levels of mindfulness and decreases self-reported levels of stress.

Method

Research Design

Single-subject design has a long history in psychological and counseling research (Heppner, Wampold, & Kivlighan, 2008). Barlow and Hersen’s (1984) exposition on the chronology of single-subject design reveals that psychology’s early research development was steeped in the use of this type of experiment. Lundervold and Belwood (2000) called single-subject experimental design “the best kept secret in counseling” (p. 92). This design can provide counselors with scientific methods of research that produce practical and useful clinical information that can be applied to practice settings.

There are several advantages of using a single-subject experimental design. It allows the researcher to narrow causes of behavior change and determine which treatment approaches are most effective. Group designs often can obscure change in individuals, thereby not allowing flexibility in modifying treatment protocols to isolate examples of cause and effect (Barlow & Hersen, 1984). Morgan and Morgan (2003) posited single-subject design as the best option when trying to explain individual differences. Another advantage of single-subject experimental design is that because the researcher collects data using a baseline and intervention phase, the subject acts as his or her own control group, thereby increasing internal validity (Sharpley, 2007). Additionally, single-subject design can allow for scrutiny of new and innovative approaches (Chapman, Baker, Nassar-McMillan, & Gerler, 2011). Specifically, this study utilized a basic single-subject experimental AB design that allows for a maximum clinical utility.

Participants

     Participants in this study were all senior-level students enrolled in a Bachelor of Science in Nursing (BSN) program at a small rural Southeastern university. Four of the participants were female and one was male. Participant ages ranged from 21–30 years old (mean age 25.6 years). Three of the participants were Caucasian, one was Hispanic American and one was Native American.

The sample was recruited from students enrolled in the upper division pre-licensure BSN program and fully engaged in all activities and requirements of the program, including clinical work at local hospitals in order to develop basic and advanced nursing skills. Recruitment involved presenting the study and requirements for participation to a class for senior nursing students and sending an e-mail to all junior and senior students, yielding five volunteers. Two of the participants dropped out of the study after intervention sessions two and three, respectively. (More discussion and analysis about participant attrition is presented in the results section.)

Measures

The Perceived Stress Scale (PSS) was developed by Cohen, Kamarck, and Mermelstein (1983) to measure the degree to which one evaluates situations and events in his or her life as stressful. Specifically, the 10-item version of the PSS (PSS-10; Cohen & Williamson, 1988) measures the degree to which one perceives life as uncontrollable, unpredictable and overloading. The PSS-10 typically requires participants to answer questions based on their experiences in the past 30 days. A modification for this study was asking participants to answer the questions based on their experiences and thoughts in the past 7 days, as the study was focused on weekly variability. The PSS-10 has a Likert-type rating scale and is widely used as a measure of perceived stress. It is shown to have internal reliability (coefficient alpha of .78) with established construct validity, as the PSS-10 scores have shown moderate relation to other measures of appraised stress. Scores can range from 0–40, with higher scores indicating greater stress. Roberti, Harrington, and Storch (2006) found the PSS-10 reliable and valid with a non-clinical sample of college students. Mean scores for males were 17.4 (SD = 6.1); mean female scores were 18.4 (SD = 6.5).

Also used in the study was the Mindful Attention Awareness Scale (MAAS), a 15-item scale designed to measure characteristics of openness or receptiveness to what is taking place in the present (Brown & Ryan, 2003). The MAAS aims to assess the level at which one is able to observe what is happening without judgment. The MAAS assesses the absence or presence of mindful mental states over time. For this study, participants were asked to form their answers based on experiences and thoughts over the past 7 days. Normative information is available for college populations (14 independent samples: N = 2,277; M = 3.83, SD = .70). Cronbach’s alphas range from .80–.90. The MAAS also has shown high test-retest reliability, discriminant and convergent validity, and criterion validity (Brown & Ryan, 2003). MacKillop and Anderson (2007) confirmed validity and reliability of the MAAS with internal reliability scores of .89.

Procedure

     Participants volunteering for the study were scheduled individually for an appointment to meet with the researcher to complete a study orientation and start baseline measurements. This 30-minute meeting consisted of an introduction to the study and the researcher, obtaining a brief background of the participant, a definition of mindfulness, completion of the informed consent paperwork, and completion of a short participant demographic form. Additionally, the first baseline measurements with the PSS-10 and MAAS were collected at the end of this meeting, and the final three baseline measurements were scheduled. Each baseline meeting consisted of an informal discussion about academic and personal stress levels and completion of the dependent measures. In total, four baseline measurements were collected over 5 weeks.

The intervention phase (B) consisted of six 1-hour sessions conducted over 5.5 weeks starting at the conclusion of the baseline phase (A). Session one began on the first week of classes in a spring semester, and sessions two through five occurred during each subsequent week. The final session, a wrap-up and review, was scheduled for the beginning of week six of the intervention. Each session consisted of 50 minutes focusing on MBCT skills, concepts and homework assignments, and 10 minutes at the end of the session to administer the dependent measures. At the final session, the researcher explained procedures and options for counseling if the participant desired to continue exploring MBCT or other issues that may have come up during the study period. Additionally, all participants received mindfulness resources such as book and Web site lists in order to continue learning and practicing mindfulness exercises.

MBCT Intervention

MBCT is traditionally an 8-week intervention conducted in group or class settings (Teasdale et al., 2000). Because this study utilized individual counseling, the intervention was reduced to six sessions, and session length was reduced to 1 hour. The individual counseling modality allowed for more focused attention to participants, and exercises could be consolidated. Also, MBCT was originally used to treat clients with chronic relapsing depressive disorders, while stress was the target symptom in this study. Due to this shift in focus, some of the exercises and homework assignments relevant to those who might have depression were not included in the intervention. Another modification included the use of prerecorded guided meditations for body scans and breathing instead of researcher-led meditations. During sections of the intervention when a meditation was introduced and practiced, the researcher started a prerecorded meditation and left the room while the participant experienced the meditation. The prerecorded breathing and body scan meditations used for this study were from the Maddux and Maddux podcast (2006).

The modified intervention still utilized the core MBCT exercises and philosophy. The next section includes a description of the major techniques used and what modifications were made to accommodate the study goals. Also included is an outline of the six session themes. Theme 1: Using Mindfulness to Break Out of Automatic Pilot focuses on an orientation to mindfulness and techniques to develop a heightened awareness of the present moment. Theme 2: Focus on the Body Enhances Clarity of the Mind, and Theme 3: Mindfulness of the Breath introduce the exercises of the body scan and breathing mediation. Theme 4: Acceptance promotes nonjudgmental acceptance of events, cognitions and emotions. Theme 5: Thoughts Are Not Facts is an educational session about cognitive-behavioral philosophies and their impact on moderating emotions. Theme 6: Putting It All Together provides a summary of the ideas and techniques of MBCT, with suggestions on how to integrate the concepts daily. The modified structure and content used is unique to this study; however, the specific components, homework and exercises are taken from Segal et al. (2002). Table 1 describes the schedule and order of session content, including session themes and agendas.

MBCT Techniques and Exercises Used in the Study

     Raisin exercise. Used as an introduction to mindfulness, this exercise asks participants to take a raisin offered by the researcher and examine all aspects of its shape, texture and external characteristics. Open-ended questions are asked to help participants explore their experience.

Body scan meditation. The exercise brings a detailed awareness and focus to specific areas of the body. A modification in this study included using a shorter meditation (8 minutes; Maddux & Maddux, 2006).

Be mindful during a routine activity. Participants are asked to choose a routine activity (e.g., brushing teeth, vacuuming, washing dishes) and to complete it mindfully per the study’s training.

     Homework record forms. Used in all sessions, these forms allow participants to document the frequency of practice of mindfulness activities.

     Thoughts and feelings exercise (professor sends an e-mail). In this exercise, the researcher presents a scenario to elicit participant reaction.

Pleasant and unpleasant events calendars. Participants receive forms (Segal et al., 2002) that help them identify one pleasant event per day in week two and one unpleasant event per day in week three.

Five-minute hearing exercise. Participants are asked to sit for 5 minutes with eyes closed and center all of their focus on hearing. When intrusive thoughts enter, participants are instructed to acknowledge them, but then return their focus to only hearing.

     Three-minute breathing space. A core skill in MBCT, this exercise acts as a mindfulness timeout.

Twenty-minute sitting meditation. This meditation is a combination of all the skills participants have learned, including the body scan, breathing meditations and the hearing exercise.

Moods, thoughts and alternative viewpoints discussion. This exercise involves a short overview of how thoughts can influence mood, and techniques and suggestions for viewing intrusive thoughts in a different way. Handouts titled Ways You Can See Your Thoughts Differently and When You Become Aware of Negative Thoughts are provided (see Segal et al., 2002).

Breathing meditation. This exercise brings a detailed awareness and focus to the breath. A modification in this study included using a shorter (9 minutes) recorded breathing meditation (Maddux & Maddux, 2006).

Mindfulness resources handout. Researchers generate a list of books, Web sites and podcasts that describe mindfulness and the techniques associated with the study. The participants receive this at the last session with encouragement to continue to seeking information on mindfulness practice if they have found it helpful.

Table 1

Schedule and Order of Session Content

Results

All five participants completed the baseline phase (A) of four weekly meetings to complete the dependent measures.  A₁–A₄ represent weeks one through four of the baseline phase_. Out of five participants, three participants completed the full intervention phase (B) of six weekly 1-hour sessions. B₁–B₆ represent weeks one through six of the intervention phase. The phases ran consecutively. One participant completed only two of the intervention sessions before withdrawing from the study. This participant cited a variety of issues including unexpected sickness and time constraints as deciding factors for withdrawal. Another participant completed three intervention sessions before withdrawing, citing time constraints and academic demands as reasons for withdrawal. The researchers have included the results for only the three participants who completed the full intervention, due to the importance in single-subject designs of multiple measurements of the dependent variable occurring over the complete span of the study in order to determine changes in self-reports of mindfulness and perceived stress. It is the comparison of the two full phases that allows interpretation of whether the intervention was the cause of the change.

Participant 1

Participant 1 reported no previous experience with mindfulness activities. The baseline mean score on the PSS-10 for Participant 1 was 21.75. This baseline mean score is higher than that of the normative sample and indicates some experience of perceived stress. The baseline mean score on the MAAS was 3.07. This baseline mean score is lower than that of the normative sample and indicates less self-report of mindfulness as measured by the MAAS. The individual scores over the baseline period for the PSS-10 fluctuated, which may be related to the time of administration. The A₁ and A₄ scores were both 22, and both measurements were taken at high-stress academic times. However, Participant 1 earned the highest score in the baseline phase (25) at A_3, on Christmas Eve. These scores support the literature that found several factors leading to nursing student stress, including home and academic demands (Magnussen & Amundson, 2003). Alternately, the individual scores over the baseline period for the MAAS were mostly stable with a significant drop in A_4, which was collected on the last Friday before the spring semester started. With the focus that mindfulness places on staying in the present moment, as measured by the MAAS, the anticipation of the new semester may have taken precedence. Essentially, baseline scores on the PSS-10 were variable, as were the academic and home stressors, and MAAS scores were relatively stable, but below normative scores for college populations.

The intervention mean score on the PSS-10 was 23. This score was 1.25 points higher than the baseline mean of overall self-reported stress as measured by the PSS-10. The individual scores of the intervention phase showed decreasing PSS-10 scores from B₁–B₃ while showing increasing MAAS scores at the same time for Participant 1 (see Figure 1). B₄ showed a 1-point increase from B₃ in PSS-10 scores, which coincided with Participant 1 experiencing a medical emergency. Despite this crisis, stress scores increased only minimally while mindfulness scores increased by .47 from B₃–B₄.

MAAS scores continued to increase throughout the intervention phase, with the highest score of 4.93 reported at the final session. This finding indicated that increased exposure to and practice of mindfulness activities correlated with higher self-report of mindfulness scores. This result was confirmed by an increase of 0.88 in the mean scores on the MAAS from baseline to intervention and a gain of 2.73 from B₁–B₆ (see Figure 1). Table 2 provides the dependent measure scores for Participant 1.

Participant 2

Participant 2 reported no previous experience with mindfulness activities. The baseline mean score on the PSS-10 for Participant 2 was 22.25. This baseline mean score is higher than that of the normative sample and indicates some experience of perceived stress as measured by the PSS-10. The baseline mean score on the

Table 2

Dependent Measure Scores for Participant 1

Note. Sessions A₁–A₄ were baseline sessions; sessions B₁–B₆ were intervention sessions.

MAAS was 2.9. This baseline mean score is lower than that of the normative sample and indicates less self-report of mindfulness as measured by the MAAS. The individual scores over the baseline period for the PSS-10

showed an increase in stress scores, with the highest baseline score (25) coming at A_4. The individual scores over the baseline period for the MAAS were stable with the lowest score (2.33) reported at A₁. Baseline scores on the PSS-10 increased and MAAS scores were stable once past the initial baseline meeting, but still remained below the normative scores (see Figure 2).

The intervention mean score on the PSS-10 was 25.17. This score represents a 2.92-point gain from the baseline mean in overall self-reported stress as measured by the PSS-10. However, there was a drop of five points on the PSS-10 from B₁–B₂. MAAS scores continued to increase throughout the intervention phase with highest score of 3.73 coming at the final session. There was an increase of 0.54 in the mean scores on the MAAS from baseline to intervention and a gain of 0.46 from B₁–B₆. Table 3 lists the dependent measure scores for Participant 2.

Table 3

Dependent Measure Scores for Participant 2

Note. Sessions A₁–A₄ were baseline sessions; sessions B₁–B₆ were intervention sessions.

Participant 3

Participant 3 reported no previous experience with mindfulness activities. The baseline mean score on the PSS-10 for Participant 3 was 20.75. This baseline mean score is higher than that of the normative sample. The baseline mean score on the MAAS was 2.7. This baseline mean score is lower than that of the normative sample. The individual scores over the baseline period for the PSS-10 showed a baseline high of 27 at A_1. PSS-10 scores dropped eight points from A₁–A₂.

The individual scores over the baseline period for the MAAS were stable from A₂–A ₄ with the lowest score (1.93) coming at A_1. Participant 3 posted stable baseline scores on both the PSS-10 and the MAAS except for at the first baseline meeting. At this meeting, the stress score was high and the mindfulness score was low.

The intervention mean score on the PSS-10 was 22.17. This score is a 1.42-point gain from the baseline mean in overall self-reported stress as measured by the PSS-10. There was a significant drop of seven points on the PSS-10 from B₃–B_4, and drops in stress score continued throughout the rest of the intervention phase.

MAAS scores continued to increase throughout the intervention phase with highest score of 5.13 coming at the final session. This is an increase of 1.1 in the mean scores on the MAAS from baseline to intervention and a gain of 3.46 from B₁–B₆ (see Figure 3). Table 4 lists the dependent measure scores for Participant 3.

Table 4

Dependent Measure Scores for Participant 3

Note. Sessions A₁–A₄ were baseline sessions; sessions B₁–B₆ were intervention sessions.

Discussion

The results indicate that exposure to an MBCT intervention can positively impact self-reported stress scores as measured by the PSS-10 and increase self-reported mindfulness scores as measured by the MAAS. All participants in this exploratory study showed gains in mindfulness levels as measured by the MAAS. These results likely occurred due to the fact that most participants began the study with no knowledge of or experience with mindfulness. Once exposed to the practice of meditation and other mindfulness exercises, the participants’ use of mindfulness processes increased steadily.

PSS-10 scores fluctuated during the baseline phase, and that variability could be explained by academic events, evaluative and anticipatory, occurring at the time of administration of the dependent measures. Specifically, the A₁ and A₄ scores for Participant 1 were both 22. Both of these measurements were taken at high-stress academic times. However, Participant 1’s highest score in the baseline phase (25) came at A_3, on Christmas Eve. These scores support the literature that found several factors leading to nursing student stress, including home and academic demands (Magnussen & Amundson, 2003; Pulido-Martos et al., 2012). Consequently, all three participants posted their lowest mindfulness scores on days that coincided with a high-stress academic event such as final exam week, or the day before the first week of classes. However, once the MBCT intervention phase began, two of the participants (1 and 3) showed steady drops in PSS-10 scores, and the third (2) showed conservative reductions, suggesting that the intervention was a likely factor in decreasing perceived stress among the three participants. However, Participant 3’s PSS-10 scores revealed a 1.42-point gain from the baseline to intervention mean. The overall gain in the intervention mean score can be attributed to an unusually high score of 28 on B₃. Participant 3 did report increased personal and academic distress at this time in the study. This result also could indicate that the benefits of mindfulness practice can be preempted by reactive stress to specific events. More long-term practice of mindful activities might have mitigated the stress of these events.

Participant 2 posted an overall 2.92-point gain from the PSS-10 baseline mean score to the intervention mean score. This increase may be attributed to an unusually high score of 28 on B₁, perhaps because the first session of the intervention fell on the first week of classes for the spring semester. Additionally, Participant 2 had only 50 minutes of exposure to mindfulness activities at this time. However, there was a drop of five points on the PSS-10 from B₁–B₂. This decrease might be attributed to some type of novelty effect from being introduced to a new treatment (MBCT). This argument is strengthened by the fact that Participant 2 posted stable stress scores throughout the rest of the intervention. It should be noted that Participant 2 informed the researchers after B₄ of the need to withdraw from the study due to time constraints and academic demands, but eventually decided to finish the study. This participant acknowledged feeling some personal distress throughout the next two sessions but did not report the need for any additional intervention. For Participant 2, it appears that MBCT was successful overall in increasing self-reported mindfulness levels as measured by the MAAS. Based on the small decreases in PSS-10 scores over time, and the continued self-report of personal distress through the final session, the MBCT intervention was only minimally successful in stress reduction for Participant 2.

Homework assignments and additional meditation practice were highly encouraged in this study, but not required. All participants chose to complete some homework and practice meditations outside the study sessions; however, the reported time spent on meditation practice varied significantly. Ancillary findings from Collard et al. (2008) found a correlation between longer practice times of mindfulness and higher levels of mindfulness. An analysis of the data surrounding participant practice time and levels of obtainment of mindfulness would have been an interesting component to include in this study.

Limitations

One disadvantage of this study is the inherent limitation of single-subject design—specifically, external validity. Because this study included separate experiments with data on only three cases, it is difficult to make generalizations. However, external validity can be enhanced by replicating the design multiple times, thus strengthening possibilities for generalizations (Heppner et al., 2008; Hinkle, 1992). Additionally, Hinkle (1992) discussed issues related to the AB design, specifically stating that “cause and effect cannot be explicitly determined” (p. 392).

The sample was motivated enough to volunteer for the study, which may mean that participants were more stressed than other nursing students and more motivated to seek solutions for stress. Alternatively, the willingness to volunteer for the study may have meant that these participants were less stressed and had more free time to participate in a weekly intervention. Another limitation was the modifications made to the MBCT intervention to accommodate the individual counseling modality. However, because of the exploratory nature of the study, modifications in future studies are warranted.

The use of only one dependent measure for each dependent variable also presents limitations. The PSS-10 and the MAAS measure self-reported stress and mindfulness. The use of other instruments or data to measure the two dependent variables might have strengthened the study. Specifically, a mixed-methods design could have focused on the provided quantitative measures as well as qualitatively analyzing the homework forms and the participant’s verbal session content. Potentially, because of the timing of the study, and because stress is often linked with college student academic failure (American College Health Association, 2013), final exam grades and midterm grades could have been compared to assess for academic changes based on the introduction of the intervention.

Implications for Counseling

This study strengthens the growing research base on the use of MBCT. Specifically, it adds to the literature supporting the efficacy of MBCT in reducing stress. A unique contribution is the use of MBCT in a single-subject experimental design. Typically, MBCT is used in a class format; this study supports the potential efficacy of MBCT in individual sessions.

Based on the above implications, this study also has implications for college counselors and counselors who work with those in stressful professions. Due to high attrition, academic distress and high-stakes testing, colleges are struggling to help their students succeed. College counselors provide services that address the academic and personal well-being of students. This study indicates that the use of MBCT in individual counseling to mitigate stress can yield promising results.

College counselors and faculty members can work together to provide mindfulness-based workshops or other activities that promote a meditative approach. Developing effective partnerships should begin with an assessment of available mindfulness resources. Program administrators want their students to be successful and may be willing to integrate resources from outside the program. Many colleges and universities might have trained counselors on staff who can provide these services. If not, negotiations with certified off-campus meditation or yoga centers could provide reduced rates for students. More importantly, counselors can train faculty members in mindfulness exercises that they can integrate into the fabric of their programs. Embedding mindfulness into academic programs could be more effective and efficient if students perceive it as part of their education instead of an add-on.

College counselors will do well to learn the language and culture of the academic programs on their campus. Crafting mindfulness interventions to correspond directly with the stressors of a certain program might help develop rapport with students. This process could be collaborative, involving college counselors, educators and students. Also, sharing data gathered from program evaluations or studies with administrators can strengthen relationships by assisting with long-term programmatic goals.

Future Areas of Research

Future areas of research should include the use of MBCT and other mindfulness-based interventions in order to replicate these results. More research is needed on delivering MBCT in an individual format. Another component of this study that needs more exploration is whether a longer amount of time spent practicing mindfulness activities provides greater relief from self-reported stress symptoms. The next logical step is to complete a study using a larger sample. Experimental studies comparing the effects of MBCT with more traditional counseling approaches aimed at reducing stress and improving performance could prove useful. How, for example, might MBCT compare to rational-emotive or rational-behavior approaches to reducing stress? Additionally, given the evolving nature of technology-based counseling interventions, future research on MBCT also might explore the value and usefulness of online MBCT interventions. Examining and comparing the effectiveness of synchronous versus asynchronous MBCT interventions would be especially valuable.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of

interest or funding contributions for

the development of this manuscript.

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Mark J. Schwarze, NCC, is an Assistant Professor at Appalachian State University. Edwin R. Gerler, Jr. is a Professor at North Carolina State University. Correspondence can be addressed to Mark J. Schwarze, Reich College of Education, Department of Human Development and Psychological Counseling, 151 College Street, Appalachian State University, Box 32075, Boone, NC 28608-2075, schwarzem@appstate.edu.