The Impact of Organizational Factors and Professional Identity on Turn Over Intent in State Vocational Rehabilitation Agencies

Abstract

Turnover of staff is an ongoing concern in the state-federal vocational rehabilitation (VR) system. To better understand employee turnover intent, a multiple regression analysis was conducted using professional identity and organizational factors (e.g., pay, work tenure) as predictor variables. An internet-based survey solicited counselor perceptions regarding professional identity, turnover intent, and job satisfaction. Respondents (n = 351) described their level of satisfaction relevant to several workplace conditions (co-workers, job in general, work assignments, pay, promotion, and supervision), and their current level of professional identity. The outcome variable representing current or future turnover intent was whether participants were actively looking for a new position or planning to look for a new position in the next 5 years. Results indicated that a little over one third of current counselors would look for a new position in the coming year, with half of current counselors seeking a new position within 5 years. Findings suggest that turnover in state-federal VR settings exceeds the national average of 19%. Purposeful pre-hire considerations, goodness of fit, motivation factors (i.e., opportunities for promotion), and hygiene factors (i.e., caseload size, supervision, and pay) identified in this study should be elements of a state-federal VR agency’s plan to mitigate turnover intent.

Keywords

turnover intent vocational rehabilitation rehabilitation counseling professional identity

Rehabilitation counseling is a specialization focusing on service delivery to people with disabilities who are striving to realize goals related to education and career training, occupational attainment, independent living, and social inclusion (Commission on Rehabilitation Counselor Certification [CRCC], 2021). Rehabilitation counselors (RCs) work in various settings, including mental health agencies, college and university settings, private insurance, and various nonprofit entities (Saunders et al., 2009). The state-federal vocational rehabilitation (VR) system was once the only rehabilitation counseling practice setting and remains a primary one today. That said, the state-federal VR system has been dealing with staffing issues for some time (Tansey et al., 2004). These concerns with staffing may be due to the transferability of skills possessed by highly qualified RCs (i.e., having a master’s degree, national certification, and professional licensure; Leahy, 2012). With counselor retirement rates increasing, recurrent concerns related to staff turnover, the financial cost of replacing staff, and fewer RCs pursuing employment in the state-federal VR system, the issue of retention remains salient (Chan, 2003; Chan & Ruedel, 2005).

The Financial Cost of Turnover

Yang and colleagues (2011) suggested the cost of employee turnover to be 17% of the worker’s salary. The Center for American Progress estimated the cost of employee replacement to be equal to 20% of the employee’s annual salary (e.g., the cost of replacing an employee making US$60,000 would be US$12,000; Boushey & Glynn, 2012). Cascio (2000) found that human resource professionals suggested a range of 93% to 200% of the position’s annual salary to be the expected replacement cost. A more recent estimate suggests the turnover cost of an employee may be five times the salary of the position (Fortin, 2022). With conservative estimates of annual employee turnover in state-federal VR agencies at 16% (Pitt et al., 2013), the financial impact of turnover on the organization is substantial. When workers leave their position of employment, it creates a hardship for the organization because of the loss of institutional memory the worker has acquired, as well as the time required to search for candidates, fill vacancies, train workers, and build the capacity of the organization to serve its clientele.

In addition to the costs associated with recruiting and training new hires, a second commonly suggested consequence of staff turnover is the reduced quality of services to the agency’s clientele (Garner et al., 2012). Studies on staff turnover in parallel fields of mental health and substance abuse treatment have shown staff turnover to decrease fidelity to treatment plans (Woltmann et al., 2008), lower client satisfaction rates (Plomondon et al., 2007), and lead to poorer client outcomes (Williams & Plotts, 2010). Johnson-Kwochka and colleagues (2020) found that counselor turnover was associated with clinical decline in clients, specifically increasing client anxiety levels and decreasing their physical health. In addition to the potential impact on client anxiety and physical health, this clinical decline in a rehabilitation setting might include a lack of follow through, apathy toward counselor engagement, and a general sense that support from the agency is minimal due to ongoing counselor turnover.

Researching Turnover in State VR Agencies

Turnover intent, or an employee’s intent to quit their current position and seek out a new position of employment, has been described as “what occurs ‘behind the scenes’ before the physical act of leaving an organization” occurs (Pitt et al., 2013, p. 5). Thus, leaving a job is an observable outcome and final step in the cognitive decision-making process related to voluntary turnover (Lambert et al., 2001; Steel & Ovalle, 1984). Turnover intent is an important predictor of voluntary turnover (Mathieu et al., 2015), which is known to be more costly, recurrent, and unexpected than involuntary turnover (i.e., terminating an employee; (Armstrong et al., 2008; Lambert & Paoline, 2010).

Job satisfaction, organizational commitment, age, and work tenure are some of the most researched factors in the study of turnover intent across occupations and professions (Van Breukelen et al., 2004). These and other variables have been used in research to examine the turnover intent of RCs.

Frederick Herzberg’s two-factor model (Herzberg et al., 1959) postulated that an individual’s job satisfaction (a variable influencing employee turnover intent) is shaped primarily by two constructs: hygiene factors and motivation factors. Hygiene factors are extrinsic to the actual work performed (e.g., salary, working conditions, physical workspace, relationships with colleagues and supervisors, etc.) and influence satisfaction with employment. Motivation factors are intrinsic to the job and satisfy personal needs for growth and achievement (e.g., recognition for performance and achievement, perceived job status, opportunities for advancement and personal growth, etc.). In this model, work satisfaction is considered a motivating factor for maintaining current employment and is associated with higher rates of occupational commitment (Herzberg et al., 1959).

Armstrong and colleagues (2008) examined turnover intent and actual turnover among certified rehabilitation counselors (CRC) in various practice settings using Herzberg’s framework. Their findings indicated that

Of those reporting an intention to quit in the next two years, an alarming 54.2% (n = 274) [had] been in their jobs for less than five years, and 30% of those (n = 154) were less than 36 years old. (Armstrong et al., 2008, p. 29)

Turnover intent was influenced by limited options for career advancement, limited work tenure (i.e., new professionals), and ethnicity (i.e., African Americans). In contrast, high salary, advanced work tenure (i.e., the advanced age of the worker), and a master’s level education were more likely to indicative of the workers intent to stay.

Considering Holland’s model of person-environment fit (1997), one can speculate that a better fit between the individual’s personality and work environment might predict occupational satisfaction (Holland’s model of social work environments, 1997). Zanskas and Strohmer (2011) found the following factors to positively correlate with the intent to maintain employment with a state VR agency: office environment, autonomy as a professional, an interest in helping individuals with disabilities become independent, and opportunities for personal and professional growth. The authors noted a continual evolution of the state-federal VR work setting toward a work environment that is only moderately congruent with the “prototypical rehabilitation counselor personality” (Zanskas and Strohmer, 2011, p. 8), suggesting rates of occupational incongruency and resultant turnover intent will continue to rise.

Pitt and colleagues (2013) explored turnover intent from the person-organization fit theoretical framework. Their work explored person-organization fit, job satisfaction, and organizational commitment as predictors of turnover intent. The authors found high levels of affective commitment to the employment setting (i.e., state VR), particularly when the work was considered meaningful and the work environment was viewed positively. Higher caseload sizes and lower pay correlated with increased counselor turnover intent. In contrast to the study by Layne and colleagues (2004), where age and years of experience were not found to be a significant contributor to turnover intent, Pitt et al. (2013) found that those with 5 years of experience or less were more likely to leave their position with a state-federal VR agency. Results indicated that when rehabilitation professionals working in state VR settings were new to their position and experienced a high degree of occupational “incongruence relative to values, expectations, and needs,” there was an increase in turnover intent and, subsequently higher rates of actual turnover (Pitt et al., 2013, p. 12).

More recently, Zhai and colleagues (2021) examined turnover intent of RCs who were alumni of the same graduate program. Like prior research (e.g., Pitt et al., 2013), job satisfaction and person-organization fit were examined as predictors of turnover intent. Results indicated that lower job satisfaction was the primary predictor of turnover intent, and when compared with RCs working in other practice settings, RCs working in state VR settings were more likely to leave their current position of employment. Results also indicated that one third of the participants were likely to leave their present organization at some point in the coming year, and two thirds of the participants were unlikely to recommend the state VR setting to other qualified job applicants. Although these studies have helped to outline some potential reasons for the occupational turnover manifested in state VR settings, none examined the possible influence of professional identity salience on turnover intent.

Professional Identity

Professional identity encapsulates the growth and development of a professional from the time they enter their graduate training program to the time they retire (Moss et al., 2014). A strong professional identity is achieved when the individual view of self as a person and competence as a professional are in a state of congruence, suggesting a balance between the personal and professional views of self (Auxier et al., 2003; Gibson et al., 2010). This lifelong process culminates with counselors developing attitudes regarding professional responsibilities, adherence to ethical standards, and active membership in professional associations (Auxier et al., 2003). Although a unified professional counseling identity remains elusive (Mellin et al., 2011), the philosophy of rehabilitation counseling helps build a professional identity grounded in the professional values of: empathy and acceptance, valuing the person with the disability, and recognizing the dignity and worth of all humans, valuing employment, instilling hope and opportunity, and approaching service delivery from a holistic and flexible point of view (CRCC, 2017; Landon et al., 2021).

Three themes have been the foci of definitions of professional identity: (a) self-labeling as a professional, (b) integrating skills and attitudes of the profession, and (c) an internalized perception of one’s professional self as belonging to a corresponding professional community (Gibson et al., 2010). Self-labeling as a professional begins when the individual enrolls in a counselor education program (Gibson et al., 2010). During this initial phase of identity development, counselors rely on external authority figures and experts (typically faculty members) as they engage in conceptual learning (Auxier et al., 2003). Conceptual learning represents the didactic instruction and study typical of a graduate training program and has a high degree of value early in the counselor’s journey of professional identity development (Gibson et al., 2010). Counselors are expected to demonstrate adequate skill and corresponding professional dispositions by integrating the didactic instruction received in conceptual learning and applying these skills and attitudes in their selected practice setting. This experiential learning phase involves feedback from supervisors and colleagues, geared toward shifting the locus of evaluation from external sources (e.g., supervisors, colleagues) to internal self-reflection and evaluation indicative of higher levels of cognitive complexity (Granello, 2000). The internalized perception of oneself as a counselor is demonstrated through the acquisition of professional certification and licensure and continues over time with active involvement in the profession through service delivery (employment in the field) and continuing education (activity in a professional association). This cyclical development of counselors is represented in models of counselor supervision and development (Granello, 2010; Rønnestad & Skovholt, 2003; Skovholt & Rønnestad, 1995).

Although previous work has explored turnover intent’s relationship to occupational stress (Layne et al., 2004), job satisfaction (Armstrong et al., 2008), and organizational commitment (Pitt et al., 2013), prior research has not explored turnover intent using professional identity as a predictor variable. The purpose of this project was to better understand how professional identity and motivation factors (e.g., pay, work caseload size, years of experience, etc.) impacted turnover intent of rehabilitation professionals working in the state VR setting. The following research questions were explored:

Research Question 1 (RQ1): What proportion of state-federal RCs are actively looking for another job or intend to look for another job within 5 years?

Research Question 2 (RQ2): Do aspects of job satisfaction (i.e., hygiene and motivation factors), and professional identity predict actively looking for another job among state-federal RCs?

Research Question 3 (RQ3): Do aspects of job satisfaction (i.e., hygiene and motivation factors) and professional identity predict intentions to look for another job within 5 years among state-federal RCs?

Method

Participants and Recruitment

Prior to data collection and any research activities, the research team first obtained human subject approval from the Institutional Review Board at the lead author’s university. Although the title of the position may vary based on the participants’ educational level, years of experience, and title used by their respective agency (e.g., rehabilitation specialist, RC I, RC II, etc.), the targeted participants for this research project were individuals performing the expected roles and duties of an RC in a state VR agency. Respondents not presently employed by a state VR agency or working in a state VR agency but not acting in the primary role of RC (e.g., agency administrator, support staff, manager/supervisor) were excluded.

The research team obtained a statement of support from the Council of State Administrators of Vocational Rehabilitation (CSAVR) that was designed to encourage participation by targeted practitioners. Recruitment materials were provided to CSAVR, who assisted with the distributing the informational email/invitation to participate through their listserv of agency directors and provided their statement of support. Data collection took place over 1 month in September and October of 2021. After the initial distribution of the first invitation email, two follow-up reminders were sent at 10- and 20-day increments. Participants could choose to participate in a random drawing for one of US$10 to US$50 Amazon gift cards as compensation for their participation.

Although it can be reasonably assumed that all state agencies received the study invitation via the CSAVR listserv, the research team could not confirm all RCs in the state agencies received the invite email, nor was there a way to calculate the number of RCs presently working in the state-federal VR system. As such, a response rate was not calculated. A total of 544 participants began the survey, but only fully completed responses (all quantitative questions answered) were used in final data analysis. The final sample consisted of 351 state-federal vocational RCs representing 24 states (64.3% completion rate).

Instrument

The instrument for this cross-sectional survey comprised 77 total items. Twenty-three of the questions were specific to participant demographics. These demographics included the following: gender, race-ethnicity, education, and whether the person is the primary earner in the home. Work-related demographics included CRC status, years in the field, and current and desired caseload size. The Professional Identification Scale (6 items; Phillips & Leahy, 2012) was used to measure professional identity. The Abridged Job Descriptive Index was used to measure job satisfaction and has 38 items (Stanton et al., 2002). Ten items were specific to turnover intention and were designed using the Staying/Leaving Index developed by Bluedorn (1982).

Counselor Professional Identity Development

The Professional Identification Scale (PID) was used by Phillips and Leahy (2012) in their research on professional associations. Participants in the Phillips and Leahy (2012) study held the CRC professional credential and/or were a member of one of three rehabilitation counseling profession associations (i.e., the American Rehabilitation Counseling Association, the National Rehabilitation Counseling Association, and the Rehabilitation Counselors and Educators Association). The five items for this scale were modified from the organizational identification scale originally built by Mael (1988). Participants responded to these questions using a 5-point Likert-type style rating ranging from 1 (strongly disagree) to 5 (strongly agree). These questions are grounded in social identity theory, and reliability coefficients were found to be .77 (Phillips & Leahy, 2012), with a typical range from .81 to .91 (Mael et al., 2001). The reliability of the PID in this survey was .72, slightly below previous reliability measures on the PID, but still considered acceptable (Cortina, 1993).

Abridged Job Descriptive Index

The Job Descriptive Index (JDI) was originally developed by Smith and colleagues (1969) to measure general job satisfaction. Some concerns regarding the JDIs length and time required for completion led to Stanton and colleagues’ (2002) development of the Abridged Job Descriptive Index (aJDI). This 38-item instrument requires the participant to consider one of six scenarios (e.g., co-workers, the job in general, work assignments, pay, promotion, and supervision) and respond to a specific word relating to the scenario. Participants select either Yes, No, or Unsure. Positive perceptions of the work environment are scored as a 3, negative perceptions of the work environment as a 0, and unsure perceptions are scored as a 1. The total score is calculated by summing all items. The reliability of the aJDI has been found to be strong, with Cronbach’s alpha scores of .86 (Work), .76 (Pay), .81 (Promotion), .79 (Supervision), and .82 (Coworkers). Both the JDI and aJDI are copyrighted (Bowling Green University, 2009) but available for use with permission through Bowling Green University. Only the subscales focused on co-workers (referred to as people for this study), pay, promotion, and supervision were used for this study.

Outcome Variable

To represent the current turnover intent of participants, authors measured whether participants were actively looking for a new position or planning to look for a new position in the next 5 years. These two outcome variables were modified from the Staying Leaving Index (Bluedorn, 1982) to represent current and future turnover intent. All items were scored using a 5-point Likert-type style rating ranging from 1 (strongly disagree) to 5 (strongly agree).

Data Analysis

Analyses were completed in a combination of SPSS Version 26 (IBM Corp., 2019) and R 4.0.3 (R Core Team, 2020). The SPSS was used for the initial exploration of the data and for running descriptive data. R was used to conduct the regression analysis and bootstrapping with the help of the Hmisc (Harrel, 2021), boot (Canty & Ripley, 2019), MASS (Venables & Ripley, 2002), and QuantPsyc (Fletcher, 2022) R software packages. Data were only included when values were reported for all items included in the regression analysis.

Demographics

The demographic characteristics are described in Table 1. Participants had an average of 8.29 years of experience (SD = 8.43, range = 1–53) as RCs in the state-federal VR agency. The majority of respondents were Caucasian (74.9%), and inadequate numbers of other racial-ethnic groups required the use of a dichotomous Caucasian or Not Caucasian rating for the statistical modeling. To better understand the difference between current and preferred caseload sizes (a hygiene factor influencing work dissatisfaction), participants were asked to provide a current caseload size using increments of 25 (e.g., 0–25, 26–50, 51–75, etc.). Participants were also asked to provide an ideal caseload size or their preferred caseload size using the same incremental system. The mode for current caseload size was 76 to 100 cases, while the mode for ideal caseload size was 51 to 75 cases.

Table 1.

Demographic Characteristics of Participants by Group (N = 351).

Characteristic	n	%
Gender
Male (0 or reference)	58	16.5
Female	293	83.5
Race
White (0 or reference)	269	76.6
Not White	82	23.4
Education
Bachelor’s or less (0 or reference)	48	13.7
Master’s degree or higher	303	86.3
Primary earner in the home
No (0 or reference)	167	47.6
Yes	184	52.4
CRC status
Not certified (0 or reference)	201	57.3
Certified	150	42.7
	M	SD
Years in the field	8.00	8.10

Note. CRC = certified rehabilitation counselors.

Hierarchical Regression Analysis

Hierarchical regression analysis (HRA) was used to measure the incremental variance accounted for by each set of predictors and to determine the unique contribution and predictive ability of each predictor variable to the variance of the selected measures of turnover intent. The change in R² (ΔR²) was examined as a measure of each predictor set’s unique contribution to the model. Three blocks were entered, namely: (a) demographics (i.e., gender, race, education, and whether the respondent was the primary earner in the home); (b) commitment to the discipline (i.e., CRC status, Years in the field, and professional identity); and (c) characteristics of the workplace (i.e., ideal vs. real caseload size, and perceptions of co-workers, pay, promotion, and supervision). This order of blocks was used to facilitate a more accurate understanding of the effect of the modifiable factors in the workplace most relevant to predicting turnover intent while controlling for individual demographics and general markers of commitment to the discipline.

Significance tests of the regression coefficients for each predictor variable were assessed both at entry to the model and for the final model. Prior to the analysis, assumptions of normality, independence of errors, linearity, homoscedasticity, and multicollinearity were reviewed. Although most assumptions were met, visual markers of normality and heteroscedasticity suggested the use of bootstrapping for a more accurate interpretation of the data.

Results

First, the goal was to understand the current trends in turnover by counselors in the state-federal VR systems. Table 2 shows the participant responses to the modified Staying/Leaving Index (Bluedorn, 1982). Authors sought to understand the proportion of sample participants practicing in state-federal rehabilitation counseling who were either actively looking for a new job or intended to do so within 5 years (See Figure 1). To the question of whether respondents were actively looking, 176 (50.1%) strongly disagreed, 37 (10.5%) somewhat disagreed, 47 (13.4%) neither agreed nor disagreed, 47 (13.4%) somewhat agreed, and 44 (12.5%) strongly agreed. To the question of whether respondents intended to look for a new job within 5 years, 77 (22%) strongly disagreed, 32 (9.1%) somewhat agreed, 61 (17.4%) neither agreed nor disagreed, 68 (19.4%) somewhat agreed, and 112 (32.0%) strongly agreed.

Table 2.

Current Job Seeking Trends of State-Federal VR Workers.

Time frame	Response
Time frame	Strongly disagree	Somewhat disagree	Neither agree/disagree	Somewhat agree	Strongly agree
Working for current agency in 6 months	40 (11.4%)	25 (7.1%)	27 (7.7%)	74 (21.1%)	184 (52.6%)
Working for current agency in 1 year	46 (13.1%)	28 (8.0%)	31 (8.9%)	82 (23.4%)	163 (46.6%)
Working for current agency in 5 years^a	86 (24.6%)	55 (15.7%)	47 (13.4%)	56 (16.0%)	105 (30.0%)
See myself retiring from current position and agency^a	88 (25.1%)	41 (11.7%)	54 (15.4%)	66 (18.9%)	100 (28.6%)
I frequently think of quitting my job	87 (24,9%)	59 (16.9%)	41 (11.7%)	101 (28.9%)	62 (17.7%)
Planning to seek a new job in the next 6 months	144 (41.1%)	43 (12.3%)	57 (16.3%)	53 (15.1%)	53 (15.1%)
Planning to seek a new job in the next year	133 (38.0%)	41 (11.7%)	46 (13.1%)	69 (19.7%)	61 (17.4%)
Planning to seek a new job in the next 5 years	77 (22.0%)	32 (9.1%)	61 (17.4%)	68 (19.4%)	112 (32.0%)
Actively looking for a new position^a	175 (50%)	37 (10.6%)	47 (34.4%)	47 (13.4)	43 (12.3%)

Note. VR = vocational rehabilitation.

n = 349, 1 Missing. N = 350.

Figure 1.

Descriptive Scores for Participants Reporting Actively Looking for Another Position and Intent to Leave Within 5 Years.

HRA was used to address the research questions exploring what factors predict turnover intent among state-federal RCs. Table 3 reflects the descriptive statistics for the scales used in the study and their respective Cronbach’s alpha scores. Table 4 provides a correlation table for all variables included in both models, and Tables 5 and 6 provide the results of the separate regression analyses. The three variable sets in order of entry were (a) demographics, (b) commitment to the discipline, and (c) characteristics of the workplace. In predicting whether participants were actively looking for work, the change in R² (ΔR²) suggested that commitment to the discipline and characteristics of the workplace predicted turnover intent while demographic variables did not at entry in the model. Although significant, the commitment to the discipline factors entered in Step 2 accounted for just under 5% of the variance in immediate turnover intent, Adj. R² = .051, F(7, 343) = 2.65, p = .001. Professional identity was the only variable in the set that was statistically significant, with increases in professional identity correlating with a reduction in turnover intent (β = −.21, p < .001). The final model predicted 28.1% (25.5% using Adjusted R²) of the total variance in actively looking for a new job. The inclusion of workplace characteristics in this third and final step accounted for an additional 23% of the variance. All five workplace characteristics were significant in predicting whether participants were actively looking for a new job. Supervision was the most powerful predictor of actively looking (β = −.25, p < .001), with more negative perceptions of supervisors being correlated with an increased likelihood of actively looking for a new job. Professional identity, the only other significant predictor at entry in the model from Steps 1 and 2 remained significant in this final step (β = −.15, p = .003; see Table 5 for more information).

Table 3.

Descriptive Statistics for Scales Used in Study (N = 351).

Constructs	Instruments	Number of item	M (SD)	Respective range	α
Predictor variables
Professional identity	Professional Identification Scale	6	22.33 (4.19)	6–30	.72
People	Job Descriptive Index: People	6	14.19 (4.78)	0–18	.80
Pay	Job Descriptive Index: Pay	6	6.02 (6.02)	0–18	.86
Promotion	Job Descriptive Index: Promotion	6	4.71 (5.21)	0–18	.84
Supervision	Job Descriptive Index: Supervision	6	13.16 (5.72)	0–18	.85

Table 4.

Intercorrelations for Variables Used in Hierarchical Regression Analysis (N = 349).

Variable	1	2	3	4	5	6	7	8	9	10	11	12	13
1. Gender	1	—	—	—	—	—	—	—	—	—	—	—	—
2. Race	.138†	1	—	—	—	—	—	—	—	—	—	—	—
3. Education	−.067	−.014	1	—	—	—	—	—	—	—	—	—	—
4. Primary earner	.024	.092	−.095	1	—	—	—	—	—	—	—	—	—
5. CRC status	−.035	−.137*	.328‡	−.122*	1	—	—	—	—	—	—	—	—
6. Years in the field	−.062	−.055	.215‡	.058	.177‡	1	—	—	—	—	—	—	—
7. Professional identity	−.006	.026	−.001	−.164^†	.126*	.006	1	—	—	—	—	—	—
8. Caseload size	−.095	.022	.081	.046	.010	.039	.006	1	—	—	—	—	—
9. People	.026	−.032	−.015	.053	.075	.025	.039	.082	1	—	—	—	—
10. Pay	−.189‡	−.049	.089	−.081	.129*	.143†	.025	.290‡	.197‡	1	—	—	—
11. Promotion	−.032	−.022	−.086	−.032	−.083	−.070	.076	.117*	.198‡	.249‡	1	—	—
12. Supervision	.052	−.035	−.044	−.000	−.059	−.083	.166†	.025	.293‡	.142†	.280‡	1	—
13. Actively looking	−.032	.008	−.039	.058	−.029	−.074	−.213 ‡	−.223‡	−.277‡	−.273‡	−.308‡	−.375‡	1
14. Looking in 5 years	−.038	−.021	−.030	.006	.004	−.188‡	−.085	−.220‡	−.260‡	−.280‡	−.303‡	−.251‡	.569‡

Note. Gray cells were non-significant. CRC = certified rehabilitation counselors.

p ≤ .05. †p ≤ .01. ‡p ≤. 001.

Table 5.

Hierarchical Regression Analysis for Prediction of Actively Looking (N = 351).

Variable	Adjusted R²	ΔR²	At entry into model			Final model
Variable	Adjusted R²	ΔR²	B	SE B	β	B	SE B	β	Bootstrapped CI (95%)
Step 1	−.006	.005
Gender (Men)			−0.14	0.22	−.04	−0.26	0.19	−.06	[−0.60, 0.11]
Race (White)			0.02	0.19	.01	−0.02	0.17	−.01	[−0.35, 0.30]
Education (bachelor’s or less)			−0.15	0.23	−.04	−0.16	0.22	−.04	[−0.61, 0.35]
Primary earner (no)			0.15	0.16	−.05	−0.10	0.14	−.03	[−0.18, 0.38]
Step 2	.032	.046***
CRC status			0.10	0.17	.03	0.06	0.15	.02	[−0.26, 0.35]
Years in the field			−0.01	0.01	−.06	−0.01	0.01	−.06	[−0.03, 0.01]
Professional identity			−0.08	0.02	−.21***	−0.05	0.02	−.15**	[−0.09, −0.02]
Step 3	.255	.230***
Caseload correspondence			−0.14	0.05	−.15**	−0.14	0.05	−.15**	[−0.24, −0.04]
People			−0.04	0.02	−.12*	−0.04	0.02	−.12*	[−0.07, −0.01]
Pay			−0.03	0.01	−.13*	−0.03	0.01	−.13*	[−0.06, −0.01]
Promotion			−0.05	0.01	−.16***	−0.05	0.01	−.16***	[−0.07, −0.02]
Supervision			−0.07	0.01	−.25***	−0.07	0.01	−.25***	[−0.09, −0.04]

Note. F(12, 338) = 11.00, p < .001 for the full model; ΔF(4, 346) = 0.45, p = .773, for Step 1; ΔF(7, 343) = 2.65, p =.001 for Step 2. CI = confidence interval; CRC = certified rehabilitation counselors.

p ≤ .05. **p ≤ .01. ***p ≤ .001.

Table 6.

Hierarchical Regression Analysis for Prediction of Planning to Look Within 5 Years (N = 350).

Variable	Adjusted R²	ΔR²	At entry into model			Final model
Variable	Adjusted R²	ΔR²	B	SE B	β	B	SE B	β	Bootstrapped CI (95%)
Step 1	−.009	−.009
Gender (Men)			−0.16	0.23	−.04	−0.33	0.20	−.08	[−0.71, 0.03]
Race (White)			−0.05	0.20	−.01	−0.10	0.18	−.03	[−0.44, 0.22]
Education (bachelor’s or less)			−0.14	0.24	−.03	−0.04	0.23	−.01	[−0.49, 0.43]
Primary earner (no)			−0.01	0.17	−.00	−0.05	0.15	−.02	[−0.34, 0.24]
Step 2	.028	.045***
CRC status			0.16	0.18	.05	0.15	0.16	.05	[−0.17, 0.47]
Years in the field			−0.04	0.01	−.20***	−0.04	0.01	−.19***	[−0.05, −0.02]
Professional identity			−0.03	0.02	−.09	−0.01	0.02	−.04	[−0.05, 0.03]
Step 3	.214	.194***
Caseload congruence			−0.14	0.05	−.14**	−0.14	0.05	−.14**	[−0.25, −0.04]
People			−0.04	0.02	−.13**	−0.04	0.02	−.13**	[−0.07, −0.01]
Pay			−0.04	0.01	−.14**	−0.04	0.01	−.14**	[−0.07, −0.01]
Promotion			−0.06	0.02	−.20***	−0.06	0.02	−.20***	[−0.09, −0.03]
Supervision			−0.04	0.01	−.14**	−0.04	0.01	−.14**	[−0.06, −0.01]

Note. F(12, 337) = 8.94, p < .001 for the full model; ΔF(4, 345) = 0.23, p = .924, for Step 1; ΔF(7, 342) = 2.43, p =.001 for Step 2. CI = confidence interval; CRC = certified rehabilitation counselors.

p ≤.05. **p ≤ .01. ***p ≤ .001.

In predicting intent to find a new job within 5 years, the change in R² (ΔR²) again suggested that commitment to the discipline and characteristics of the workplace predicted turnover intent while demographic variables did not at entry in the model. Although significant, the commitment to the discipline factors entered in Step 2 accounted for < 3% of the variance in turnover intent, Adjusted R² = .028, F(7, 342) = 2.43, p = .001. Years in the field was the only variable in the set that was predictive of intent to look for a new job within 5 years, with increased years in the field correlating with a reduction in turnover intent (β = −.20, p < .001). The final model predicted 24.1% (21.4% using Adjusted R²) of the total variance in intent to look for a new job within 5 years. The inclusion of workplace characteristics in this third and final step accounted for an additional 19.4% of the variance. All five workplace characteristics were again significant in predicting whether participants were actively looking for a new job. Opportunities for promotion was the most powerful predictor of actively looking (β = −.20, p < .001), with more restricted perceptions of promotion opportunities being correlated with an increased likelihood of turnover intent. Years in the field, the only other significant predictor at entry in the model from Steps 1 and 2 remained significant in this final model (β = −.19, p < .001; see Table 6 for more information).

Discussion and Implications

Turnover has been and continues to be an issue in state-federal VR settings. Similar to previous research (Armstrong et al., 2008; Zhai et al., 2021), results in the present study indicate a little over one third of participants (n = 130, 37.1%) intend to seek a new position in the coming year and half of participants (n = 178, 51.4%) would seek a different work opportunity in the next 5 years. Opportunities for promotion, supervision, caseload congruence, pay, and co-workers were all found to be statistically significant in their impact on a counselor’s intent to quit (see Table 6).

Turnover in agency personnel is a natural part of the employment cycle, with most employers averaging a 19% turnover on an annual basis (Fortin, 2022). However, the turnover rates noted in this study (one third annually, and half of the labor force in the next 5 years) and in prior research suggest turnover in state-federal VR settings exceeds the national average (Armstrong et al., 2008). From a cost perspective, an agency with 90 counselors experiencing an annual turnover rate of one third (as indicated by participant responses) would have 30 counselor vacancies each year. Using a replacement rate of 20% of the counselor’s annual salary (Boushey & Glynn, 2012) and an annual salary of US$56,000 (CRCC, 2022), a state VR agency could anticipate an annual replacement and retraining/hiring cost of US$336,000 due to counselor turnover. Whether administrators in VR settings consider the more conservative projections suggesting replacement cost to be 17% to 20% of the position’s associated annual salary (Boushey & Glynn, 2012; Yang et al., 2011), or higher projections ranging from 93% to 200% of the salary (Cascio, 2000; Fortin, 2022), the financial cost associated with turnover is evident. All factors considered in the HRA were found to be statistically significant in their impact on turnover intent (see Table 6) and represent both hygiene factors related to the organizational environment (i.e., caseload congruence, co-workers, pay, and supervision) and motivation (i.e., promotion opportunities). The following section explores the topic of turnover in state-federal VR agencies further.

Pre-Hire Considerations and Professional Identity

Pre-Hire Considerations

Finding the right talent can improve client service delivery (Garner et al., 2012), and a highly qualified RC has been shown to have higher quality outcomes (Mackay et al., 2020). Findings in the present study indicated practitioners with a higher level of professional identity as a RC were less likely to seek employment elsewhere. As such, persons within the state-federal VR system responsible for hiring decisions should consider how they are assessing professional identity based on the practitioner’s professional dispositions and alignment with rehabilitation counseling philosophy as part of the hiring process.

Although there is risk with any hiring decision, evidence suggests state-federal VR settings should consider including an informal assessment of professional dispositions prior to hiring an individual to fulfill the role of RC (Landon et al., 2021; Sabella et al., 2020). Although accreditation standards require the assessment of disposition in preprofessional training (Council for Accreditation of Counseling and Related Programs [CACREP], 2016), few formal evaluations of counselor dispositions exist, and fewer yet have been validated for field use (Sabella et al., 2019). The lack of standardized instruments speaks to inherent challenges in assessing dispositions, such as the tendency to rely on observational rating systems with low reliability and potential for evaluator bias (Sabella et al., 2019).

Yet, the nature of dispositional attitudes, values, and interpersonal functioning may not be suited to assessment through standardized tests or self-report scales. Sabella et al. (2019) recommended that agencies identify a few critical professional dispositions that align with agency values and then build rating systems for hiring or ongoing counselor assessment. Key disposition examples offered within the literature include openness to learning, change, and feedback; respect for others; self-awareness, and professional responsibility and commitment (Sabella et al., 2019; Spurgeon et al., 2012). Others cite the primacy of assessing counselors’ interpersonal behaviors in forming working alliances with clients and attitudes in showing respect for culture and other diverse identities (Levine, 2020; Levine et al., 2021; Sabella et al., 2019). In interviewing candidates, agencies may consider designing case scenarios aligning with critical dispositions, where candidates are asked to reflect on their attitudes or offer examples of past behaviors. Another option is implementing probationary work periods prior to full hires, where observational ratings of dispositions could be used to make continuation decisions (Sabella et al., 2020). Finally, agency personnel may consider looking out for “red-flag” dispositions in prospective candidates, such as evidence of problematic close-mindedness, low self-awareness, and not being open to feedback (Sabella et al., 2020).

Professional Identity and Goodness of Fit

Recognizing that knowledge, skills, and abilities are important elements in the matching of professionals to careers, Saks and Ashforth (1997) emphasized the need to also match the values, beliefs, and personality traits of the individual with that of the organizational culture in which the professional provides goods or services. Their findings indicated that the person-organizational fit was related to actual turnover, not person-job fit. Thus, it is the matching of personal values (i.e., professional identity) to professional values of the organization that predicts turnover intent, not the practitioner’s knowledge, skills, and abilities. In the present study, this can be seen reflected by those actively looking for work.

Step two of the hierarchical regression (see Table 4) showed professional identity as a key predictor of turnover intent. If there is incongruence between perceived values (i.e., what I feel I was trained to do or what I value in my position) and actual work (i.e., what I am being asked to do), job dissatisfaction is likely, and turnover becomes the outcome. As a principle, this is the basis of the Minnesota Theory of Work Adjustment (Dawis & Lofquist, 1984; Kosciulek et al., 2015). There is a general need to balance the agency’s need for effectiveness and productivity with the individual employee’s need for a positive view of their (a) relationships with co-workers and supervisors, (b) working conditions in the work environment (including pay and work hours), and (c) sense of fulfillment from the work (Dawis & Lofquist, 1984).

Incongruence between the values, needs, and interests of the employee and the work environment can be mitigated by the type of supervision provided and relationships the employee has with colleagues, as well as future opportunities for pay/promotion (Step 3 in Table 4). Although the actual impact of the hiring changes of the Workforce Innovation and Opportunity Act (2014) has yet to be determined, hiring individuals who are unfamiliar with rehabilitation counseling philosophy will likely lead to an incongruence between personal values and professional values.

Application of Herzberg’s Factors of Job Satisfaction

Opportunities for growth (motivation factor) and caseload size, supervision, and pay (hygiene factors) were found to predict turnover intent of state-federal VR workers (see Table 6). Although these predictors of turn-over intent may take long-term planning and investment by state-federal VR agencies to sufficiently address the current turn-over dilemma, a short-term focus could outline options for improving access and commitment to supervision strategies focusing on the professional growth and development of counselors. Long-term strategies could focus on the reducing caseload size and adjustments to pay.

Motivation Factor – Opportunity for Promotion

Opportunities for promotion were found to impact turnover intent in both the present (i.e., currently looking for work) and future (see Tables 5 and 6). With administrators, supervisors/managers, and counselors, the very structure of the state-federal VR system is hierarchical in nature. Although the number of supervisory or administrative/executive positions may be limited due to the size of the agency, agencies would do well to find ways to increase promotion options for current RCs. This will help recognize employees for their meaningful and significant contributions and potentially facilitate leadership or mentoring opportunities. Examples could include providing mentorship or clinical supervision to new counselors, and being a trainer for the agency on a specific skill (e.g., case or caseload management, transition services, customized employment, employer engagement, etc.). Such opportunities provide a measure of recognition and a sense of contribution, further increasing an agency’s use of motivation factors to retain workers by increasing their overall satisfaction with employment.

Hygiene Factors

Caseload Size

Caseload congruence, defined in this study as the difference between a RC’s actual and preferred caseload size, showed actual caseload sizes tended to be larger than the preferred caseload size and was the third strongest predictor of turnover intent for those RCs actively looking for work (see Table 5). Caseload size has been a concern for many years, with some arguing that RCs would experience larger and more complex cases (Barros-Bailey et al., 2009) and that larger caseload sizes would adversely impact the working alliance between counselor and client (Kierpiec et al., 2010). With more and more individuals seeking VR services through the state-federal VR system, the perception that RCs in the state-federal VR setting must “[accomplish] more with fewer resources” may cause RCs to look for work in other settings (Kierpiec et al., 2010, p. 5), particularly when a comparison to their professional peers may find a work setting with a smaller caseload size and higher pay (Schultz & Millington, 2007). Although substantively reducing the caseload size of RCs is challenging, agency heads should engage in advocacy efforts at the state and federal level for additional resources. Agencies may also benefit from allied advocacy efforts with RC professional associations and the Council of State Administrators of Vocational Rehabilitation.

Pay

According to the United States Bureau of Labor Statistics (2021), possessing a master’s degree in the United States equates to a weekly median income of US$1,545, or an approximate annual salary of US$92,700. Conversely, the median wage for RCs, according to the CRCC (2022), is US$56,000. Although this comparison of master’s degrees and related wages does not paint a clear picture of salaries, what agencies need to consider the utility of the master’s degree in rehabilitation counseling. They have a skill set that is unique and in high demand across multiple settings. Counselors likely compare current perceived hygiene factors (e.g., wage, caseload size) to comparable fields or other practice areas within rehabilitation counseling and parallel professions. Discrepancies in wage and caseload size may lead to job dissatisfaction and subsequent career exploration in other areas. Chan and Ruedel (2005) found that a key component related to salary and retention was the perceived salary of peer professionals and other peers. Thus, as a highly qualified RC looks at other potential settings and pay, this may influence their decision to seek employment elsewhere (Schultz & Millington, 2007). Recent efforts by state VR agencies to increase wages are commendable but may not be sufficient to adjust for recent increases in inflation, housing costs, and general living expenses.

Beyond this, there seems to be a wage expansion across multiple work settings like never before. Wages are increasing across a variety of fields and settings in response to social movements and labor shortages resulting from the Great Resignation (Iacurci, 2022). As an example, the retail industry saw Target announce an increase in their minimum wage to US$24 hour, dependent on factors like the specific job, local labor market, and overall wage data (Torchinsky, 2022). This demonstrates the business worlds’ greater responsiveness to labor market demands compared with state-federal VR agencies, which often have limitations due to governmental and financial restrictions. Findings from the present study indicate that when wage opportunities in the general labor market and other rehabilitation counseling settings begin to outweigh potential income options available through traditional VR settings, RCs will seek employment elsewhere. Agencies will need to have a strong focus on building a work environment that demonstrates their comment to their employees and values their contributions, is growth-oriented, provides appropriate supervision to staff, and builds a culture of recognition and promotion to help offset potential employee turnover.

Supervision

Supervision has the potential to impact both motivation and hygiene factors, and so we frame our discussion here on the premise that supervision, with the application of clinical and administrative elements, can bridge between hygiene and motivation factors. The quality of supervision is very clearly a hygiene factor (Herzberg et al., 1959) and the quality of the supervisory relationship was among the most powerful indicators of counselors’ turnover intent, being the strongest predictor of a counselor actively looking for a job and a significant predictor of planning to look for a new position in the next 5 years (see Tables 5 and 6). Poor supervisory relationships were correlated with an increased likelihood that the counselor would actively be seeking a job elsewhere. The supervisory working alliance (SWA) is regarded as the “heart and soul” of supervision and is an extension of Bordin’s model of the client-counselor working alliance applied to the supervisor-supervisee relationship (Watkins, 2014). Evidence of connections between the SWA and numerous supervisory and counselor variables (e.g., supervision satisfaction, counselor-client alliance, adherence to treatment protocols; Bernard & Goodyear, 2019; Sabella et al., 2021) have led researchers to term it the essential common factor in supervision (Watkins, 2014).

From a motivation standpoint, the application of clinical supervision can influence motivation factors such as personal growth (developing appropriate professional goals), recognition (providing praise), performance and achievement (helping the counselor feel valued for the work), and work (the perceived value of the work counselors perform). This may require supervisors to revisit the philosophical underpinnings of rehabilitation to remind counselors of the “why,” meaning why their work is so important and the impact they can have on the individuals they serve. In terms of job intent, a supervisor’s supportive behaviors such as establishing a bond with the supervisee, collaboratively developing goals, helping the supervisee manage anxieties, addressing professional burnout, and contributing to counselor development, may act to instill professional agency and commitment (Sabella et al., 2022). Supportive clinical supervision may be critical in highly demanding work environments, such as state VR agencies, that experience persistently high caseloads, austere economic conditions, personnel retention issues, and continuously changing rules and environmental circumstances (McFarlane et al., 2011; Sabella et al., 2021). Past research indicates approaches to clinical supervision have been haphazard at best (Herbert & Trusty, 2006; McCarthy, 2013; Schultz et al., 2002) and do not meet the recommended threshold of one hour of clinical supervision per week (Association for Counselor Education and Supervision [ACES], 2011; Ellis et al., 2014). Supervisors need to dedicate time each week to clinical supervision, whereby they can focus on the needs of the practitioner. Agencies that prioritize meeting the practitioner’s needs can also expect to see improvements in client/consumer satisfaction, thereby meeting the needs of the agency (Capella & Andrew, 2004). In addition, a strong supervisory relationship may help moderate discontent feelings about a job enough to help the counselor bridge through challenging work events or conditions.

Limitations and Future Research

Although the findings make a meaningful contribution to our understanding of factors that predict turnover intent in the state-federal VR system, these results must be understood in context of the limitations. First, the data were procured by self-report and, as a result, are susceptible to being influenced by social desirability bias and unreliable disclosures of behaviors, thoughts, and feelings. This is particularly true given the potential difficulty of commenting on a current employer in a way that may be perceived as negative. Although participants were reassured of the confidentiality of their responses, the possibility of positively skewed responses remains.

The current study focused only on RC perceptions of turnover intent and did not explore any current measures to mitigate turnover within agencies. Working with state-federal VR agencies with higher retention rates may lead to identifying best retention practices. Future research may wish to qualitatively explore RC reasons for leaving. This may lead to a better understanding of personal factors beyond the currently identified motivation and hygiene factors at play in the overall decision to stay with a current employer or seek employment elsewhere. Qualitative and quantitative efforts to assess the impact of turnover on clients and their perception of service delivery are also warranted.

Another element possibly skewing the results centers on the concept of professional identity and what it means to be a RC. Concern has been expressed over how academic training programs are instilling professional identity in RCs since the 2017 CORE/CACREP merger (Strauser, 2017). Although the impact of master’s level curriculum on professional identity development was not specifically analyzed in the present study, future research may examine the curriculum of specific programs and their ability to adequately instill a professional identity grounded in the “commitment to help maximize the economic participation of those with disabilities” (Strauser, 2017, p. 5).

Finally, future research may also consider how supervision (with inherent clinical and administrative elements) might serve as a bridging element between hygiene and motivation factors, thereby improving overall work satisfaction. Although supervision is a hygiene factor, the clinical elements of supervision can influence motivation factors such as the professional development and workplace recognition of the counselor. Based on our findings, future RC job intent research should target the SWA as a principal variable of interest, perhaps including allied job intention variables such as job satisfaction. More particularly, future studies may investigate the relationship between specific supervision behaviors and intent-to-quit to inform the development of supervision approaches that can effectively reduce job dissatisfaction and enhance counselor retention.

Conclusion

The turnover issue in state-federal VR settings remains a multi-faceted issue and has been an ongoing topic of interest for many years (Armstrong et al., 2008; Chan & Ruedel, 2005; Layne et al., 2004; Pitt et al., 2013; Zanskas & Strohmer, 2011; Zhai et al., 2021). External factors (i.e., comparable work settings, shortage of applicants to graduate programs; Schultz & Millington, 2007), internal motivation factors (i.e., opportunities for promotion), and hygiene factors (i.e., caseload size, supervision, and pay) identified in this study should all be elements of a state-federal VR agency’s plan to mitigate turnover intent. One element (i.e., pay increases) should not be viewed as the panacea. State agencies should consider professional identity as one element in their pre-hire evaluation process. Careful planning, flexibility within the work environment, and support of employees will be key in minimizing the turnover intent of RCs in state-federal VR settings.

Footnotes

Acknowledgements

This research was conducted with support from the Council of State Administrators of Vocational Rehabilitation (CSAVR).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

T. J. Landon

B. N. Phillips

References

Armstrong

A. J.

Hawley

Blankenship

Lewis

A. N.

Hurley

(2008). Certified rehabilitation counseling personnel: Job satisfaction and intent to quit. Journal of Rehabilitation Administration, 32(1), 15–32.

Association for Counselor Education and Supervision Taskforce on Best Practices in Clinical Supervision. (2011). Best practices in clinical supervision. https://acesonline.net/wp-content/uploads/2018/11/ACES-Best-Practices-in-Clinical-Supervision-2011.pdf

Auxier

C. R.

Hughes

F. R.

Kline

W. B.

(2003). Identity development in counselors-in- training. Counselor Education & Supervision, 43(1), 25–38. https://doi.org/10.1002/j.1556-6978.2003.tb01827.x

Barros-Bailey

Benshoff

J. J.

Fischer

(2009). Rehabilitation counseling in the year 2011: Perceptions of certified rehabilitation counselors. Rehabilitation Counseling Bulletin, 52(2), 107–113. https://doi.org/10.1177/0034355208324262

Bernard

J. M.

Goodyear

R. K.

(2019). Fundamentals of clinical supervision (6th ed.). Pearson.

Bluedorn

A. C.

(1982). The theories of turnover: Causes, effects, and meaning. In Bacharach

S. B.

(Ed.), Research in the sociology of organizations (1st ed., pp. 75–128). JAI Press.

Boushey

Glynn

S. J.

(2012). There are significant business costs to replacing employees. Center for American Progress. https://www.americanprogress.org/issues/economy/reports/2012/11/16/44464/there-are-significant-business-costs-to-replacing-employees/

Bowling Green University. (2009). Job descriptive index. https://www.bgsu.edu/arts-and-sciences/psychology/services/job-descriptive-index.html

Canty

Ripley

B. D.

(2021). boot: Bootstrap R (S-Plus) functions (R package version 1.3-28).

10.

Capella

M. E.

Andrew

J. D.

(2004). The relationship between counselor job satisfaction and consumer satisfaction in vocational rehabilitation. Rehabilitation Counseling Bulletin, 47(4), 205–214. https://doi.org/10.1177%2F00343552040470040201

11.

Cascio

W. F.

(2000). Managing a virtual workplace. Academy of Management Executive, 14(3), 81–90. https://doi.org/10.5465/ame.2000.4468068

12.

Chan

(2003). Recruiting and retaining professional staff in state VR agencies: Some preliminary findings from the RSA evaluation study. American Institutes for Research.

13.

Chan

Ruedel

(2005). A national report: The demand for and the supply of qualified state rehabilitation counselors. American Institutes for Research.

14.

Commission on Rehabilitation Counselor Certification. (2017). Code of professional ethics for rehabilitation counselors. https://www.crccertification.com/code-of-ethics-4

15.

Commission on Rehabilitation Counselor Certification. (2021). Rehabilitation counselor scope of practice statement. https://crccertification.com/scope-of-practice/

16.

Commission on Rehabilitation Counselor Certification. (2022). 2021 Salary report: Occupations and wages for certified rehabilitation counselors. https://crccertification.com/wp-content/uploads/2022/06/2021-Salary-Report-FINAL.pdf

17.

Cortina

J. M.

(1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98

18.

Council for Accreditation of Counseling and Related Educational Programs. (2016). 2016 CACREP standards. https://www.cacrep.org/for-programs/2016-cacrep-standards/

19.

Dawis

R. V.

Lofquist

L. H.

(1984). A psychological theory of work adjustment. University of Minnesota Press.

20.

Ellis

M. V.

Berger

Hanus

A. E.

Ayala

E. E.

Swords

B. A.

Siembor

(2014). Inadequate and harmful clinical supervision: Testing a revised framework and assessing occurrence. The Counseling Psychologist, 42(4), 434–472. https://doi.org/10.1177/0011000013508656

21.

Fletcher

T. D.

(2022). Quantitative psychology tools. https://cran.r-project.org/web/packages/QuantPsyc/QuantPsyc.pdf

22.

Fortin

(2022). How to calculate employee turnover cost. The Predictive Index. https://www.predictiveindex.com/blog/how-to-calculate-employee-turnover-cost/

23.

Garner

B. R.

Hunter

B. D.

Modisette

K. C.

Ihnes

P. I.

Godley

S. H.

(2012). Treatment staff turnover in organizations implementing evidence-based practices: Turnover rates and their association with client outcomes. Journal of Substance Abuse Treatment, 42(2), 134–142. https://doi.org/10.1016/j.jsat.2011.10.015

24.

Gibson

D. M.

Dollarhide

C. T.

Moss

J. M.

(2010). Professional identity development: A grounded theory of transformational tasks of new counselors. Counselor Education & Supervision, 50(1), 21–38. https://doi.org/10.1002/j.1556-6978.2010.tb00106.x

25.

Granello

D. H.

(2000). Encouraging the cognitive development of supervisees: Using Bloom’s taxonomy in supervision. Counselor Education and Supervision, 40(1), 31–46. https://doi.org/10.1002/j.1556-6978.2000.tb01797.x

26.

Granello

D. H.

(2010). Cognitive complexity among practicing counselors: How thinking changes with experience. Journal of Counseling & Development, 88(1), 92–100. https://doi.org/10.1002/j.1556-6678.2010.tb00155.x

27.

Harrel

(2021). Hmisc: Harrel miscellaneous (R package version 4.5-0). https://CRAN.R-project.org/package=Hmisc

28.

Herbert

J. T.

Trusty

(2006). Clinical supervision practices and satisfaction within the state/federal vocational rehabilitation program. Rehabilitation Counseling Bulleting, 49(2), 66–80. https://doi.org/10.1177/00343552060490020801

29.

Herzberg

Mausner

Snyderman

B. B.

(1959). The motivation to work (2nd ed.). John Wiley.

30.

Holland

J. L.

(1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Psychological Assessment Resources.

31.

Iacurci

(2022, January 18). U.S. companies are expecting to pay an average 3.4% raise to workers in 2022. CNBC. https://www.cnbc.com/2022/01/18/us-companies-are-expecting-to-pay-a-3point4percent-raise-to-workers-in-2022.html

32.

IBM Corp. (2019). IBM SPSS statistics for Macintosh (Version 26.0).

33.

Johnson-Kwochka

Luther

Fischer

M. W.

Salyers

M. P.

Rollins

A. L.

(2020). The relationship between clinician turnover and client outcomes in community behavioral health settings. Psychiatric Services, 71(1), 28–34. https://doi.org/10.1176/appi.ps.201900169

34.

Kierpiec

K. M.

Phillips

B. N.

Kosciulek

J. F.

(2010). Vocational rehabilitation caseload size and the working alliance: Implications for Rehabilitation Administrators. Journal of Rehabilitation Administration, 34(1), 5–14.

35.

Kosciulek

J. F.

Phillips

B. N.

Lizotte

M. C.

(2015). Trait-factor theory and counseling process. In Chan

Berven

N. L.

Thomas

K. R.

(Eds.), Counseling theories and techniques for rehabilitation and mental health professionals (2nd ed., pp. 157–169). Springer.

36.

Lambert

E. G.

Hogan

N. H.

Barton

S. M.

(2001). The impact of job satisfaction on turnover intent: A test of a structural measurement model using a national sample of workers. The Social Science Journal, 38(2), 233–250. https://doi.org/10.1016/S0362-3319(01)00110-0

37.

Lambert

E. G.

Paoline

E. A.

(2010). Take this job and shove it: An exploratory study of turnover intent among jail staff. Journal of Criminal Justice, 38(2), 139–148. https://doi.org/10.1016/j.jcrimjus.2010.01.002

38.

Landon

T. J.

Sabella

S. A.

McKnight-Lizotte

Bernacchio

(2021). Rehabilitation counselor supervisors’ perceptions of counselor professional dispositions for rural service delivery. Rehabilitation Counseling Bulletin, 64(3), 145–157. https://doi.org/10.1177%2F0034355220940794

39.

Layne

C. M.

Hohenshil

T. H.

Singh

(2004). The relationship of occupation stress, psychological strain, and coping resources to the turnover intentions of rehabilitation counselors. Rehabilitation Counseling Bulletin, 48(1), 19–30. https://doi.org/10.1177/00343552040480010301

40.

Leahy

M. J.

(2012). Qualified providers of rehabilitation counseling services. In Maki

D. R.

Tarvydas

V. M.

(Eds.), The professional practice of rehabilitation counseling (1st ed., pp. 193–211). Springer.

41.

Levine

(2020). Preliminary establishment of the dispositional development scale: Incorporating the working alliance in professional disposition assessment. Rehabilitation Research, Policy, and Education, 34(2), 114–126. https://doi.org/10.1891/RE-19-21

42.

Levine

Henderson

Harley

Wilson

K. B.

(2021). Professional disposition evaluation for supervisors: A social justice approach. Journal of Rehabilitation Administration, 42(1), 7–20.

43.

Mackay

M. M.

Dunn

J. P.

Suedmeyer

Schiro-Geist

Strohmer

D. C.

West

S. L.

(2020). Rehabilitation counselor degree type as a predictor of client outcomes: A comparision of quantity versus quality in closure rates. Rehabilitation Counseling Bulletin, 63(2), 91–101. https://doi.org/10.1177/0034355218806378

44.

Mael

F. A.

(1988). Organizational identification: Construct redefinition and a filed application with organizational alumni [Unpublished doctoral dissertation, Wayne State University].

45.

Mael

F. A.

Waldman

D. A.

Mulqueen

(2001). From scientific work to organizational leadership: Predictors of management aspiration among technical personnel. Journal of Vocational Behavior, 59(1), 132–148. https://doi.org/10.1006/jvbe.2000.1783

46.

Mathieu

Fabi

Locoursière

Raymond

(2015). The role of supervisory behavior, job satisfaction and organizational commitment on employee turnover. Journal of Management & Organization, 22(1), 113–119. https://doi.org/10.1017/jmo.2015.25

47.

McCarthy

(2013). Relationship between supervisory working alliance and client outcomes in state vocational rehabilitation counseling. Rehabilitation Counseling Bulletin, 57(1), 23–30. https://doi.org/10.1177%2F0034355213484177

48.

McFarlane

F. R.

Enriquez

Schroeder

F. K.

Dew

(2011). How do we lead when change is constant? Journal of Rehabilitation, 77(4), 4–12.

49.

Mellin

E. A.

Hunt

Nichols

L. M.

(2011). Counselor professional identity: Findings and implications for counseling and interprofessional collaboration. Journal of Counseling & Development, 89(2), 140–147. https://doi.org/10.1002/j.1556-6678.2011.tb00071.x

50.

Moss

J. M.

Gibson

D. M.

Dollarhide

C. T.

(2014). Professional identity development: A grounded theory of transformational tasks of counselors. Journal of Counseling & Development, 92(1), 3–12. https://doi.org/10.1002/j.1556-6978.2010.tb00106.x

51.

Phillips

B. N.

Leahy

M. J.

(2012). Prediction of membership in rehabilitation counseling professional associations. Rehabilitation Counseling Bulletin, 55(4), 207–218. https://doi.org/10.1177/0034355212446533

52.

Pitt

J. S.

Leahy

M. J.

Lewis

A. N.

(2013). Turnover intent predictors among state vocational rehabilitation counselors. Journal of Rehabilitation Administration, 37(1), 5–18.

53.

Plomondon

M. E.

Magid

D. J.

Steiner

J. R.

MaWhinney

Gifford

B. D.

Shih

S. C.

(2007). Primary care provider turnover and quality in managed care organizations. The American Journal of Managed Care, 13(8), 465–472.

54.

R Core Team. (2020). R: A langauge and environment for statistical computer. R Foundation for Statistical Computing. https://www.R-project.org/

55.

Rønnestad

M. H.

Skovholt

T. M.

(2003). The journey of the counselor and therapist: Research findings and perspectives on professional development. Journal of Career Development, 30(1), 5–44. https://doi.org/10.1177/089484530303000102

56.

Sabella

S. A.

Bernacchio

C. P.

Boland

E. A.

Schultz

J. C.

(2019). The conceptualization and assessment of professional dispositions in rehabilitation counselor education. Rehabilitation Research, Policy, and Education, 33(3), 198–211. https://doi.org/10.1891/2168-6653.33.3.198

57.

Sabella

S. A.

Friedman

K. B.

McCarthy

Landon

T. J.

(2021). Supporting transformational leadership within public rehabilitation agencies. Journal of Rehabilitation Administration, 42(1), 37–52.

58.

Sabella

S. A.

Landon

T. J.

McKnight

Bernacchio

C. P.

(2020). How do supervisors assess and develop professional dispositions among counselors in vocational rehabilitation agencies? A qualitative inquiry. The Clinical Supervisor, 39(1), 106–127. https://doi.org/10.1080/07325223.2020.1729919

59.

Sabella

S. A.

Landon

T. J.

Pollack

S. R.

(2022). Measurement of clinical versus administrative supervision: Initial validation with a sample of rehabilitation counselors. The Clinical Supervisor, 41, 25–46. https://doi.org/10.1080/07325223.2021.1989641

60.

Saks

A. M.

Ashforth

B. E.

(1997). A longitudinal investigation of the relationships between job information sources, applicant perceptions of fit, and work outcomes. Personnel Psychology, 50, 395–426. https://doi.org/10.1111/j.1744-6570.1997.tb00913.x

61.

Saunders

J. L.

Barros-Bailey

Chapman

Nunez

(2009). Rehabilitation counselor certification: Moving forward. Rehabilitation Counseling Bulletin, 52(2), 77–84. https://doi.org/10.1177/0034355208325077

62.

Schultz

J. C.

Millington

M. J.

(2007). A microeconomic model of the personnel shortage in public rehabilitation agencies. Rehabilitation Education, 21(2), 133–142. https://doi.org/10.1891/088970107805059733

63.

Schultz

J. C.

Ososkie

J. N.

Fried

J. H.

Nelson

R. E.

Bardos

A. N.

(2002). Clinical supervision in state/federal rehabilitation counseling settings. Rehabilitation Counseling Bulletin, 45(4), 213–222. https://doi.org/10.1177/00343552020450040401

64.

Skovholt

T. M.

Rønnestad

M. H.

(1995). The evolving professional self: Stages and themes in therapist and counselor development. John Wiley.

65.

Smith

P. C.

Kendall

L. M.

Hulin

C. L.

(1969). The measurement of satisfaction in work and retirement. Rand McNally.

66.

Spurgeon

S. L.

Gibbons

M. M.

Cochran

J. L.

(2012). Creating personal dispositions for a professional counseling program. Counseling and Values, 57(1), 96–108. https://doi.org/10.1002/j.2161-007X.2012.00011.x

67.

Stanton

J. M.

Sinar

E. F.

Balzer

W. K.

Julian

A. L.

Thoresen

Aziz

Fisher

G. G.

Smith

P. C.

(2002). Development of a compact measure of job satisfaction: The abridged job descriptive index. Educational and Psychological Measurement, 62(1), 173–191. https://doi.org/10.1177/001316440206200112

68.

Steel

R. P.

Ovalle

N. K.

(1984). A review and meta-analysis of research on the relationship between behavioral interventions and employee turnover. Journal of Applied Psychology, 69(4), 673–686. https://doi.org/10.1037/0021-9010.69.4.673

69.

Strauser

D. R.

(2017). Expanding opportunities and new directions in rehabilitation cousneling: Beyond CACREP. Rehabilitation Research, Policy, and Education, 31(1), 2–6. https://doi.org/10/1891/2168-6653.31.1.2

70.

Tansey

T. N.

Bishop

Smart

J. F.

(2004). Recruitment in rehabilitation counseling: Maximizing benefits for graduate programs and the state-federal VR system. Rehabilitation Education, 18(1), 49–59.

71.

Torchinsky

(2022, March 1). Target is raising its minimum age to as much as $24 an hour. NPR. https://www.npr.org/2022/03/01/1083720431/target-minimum-wage

72.

United States Bureau of Labor Statistics. (2021). Education pays, 2020. https://www.bls.gov/careeroutlook/2021/data-on-display/education-pays.htm

73.

van Breukelen

Van Der Vlist

Steensma

(2004). Voluntary employee turnover: Combining variables from the “traditional” turnover literature with the theory of planned behavior. Journal of Organization Behavior, 25(7), 893–914. https://doi-org.dist.lib.usu.edu/10.1002/job.281

74.

Venables

W. N.

Ripley

B. D.

(2002). Modern applied statistics with S (4th ed.). Springer.

75.

Watkins

C. E.

(2014). The supervisory alliance as quintessential integrative variable. Journal of Contemporary Psychotherapy, 44(3), 151–161. https://doi.org/10.1007/s10879-013-9252-x

76.

Williams

A. C. D. C.

Plotts

H. W.

(2010). Group membership and staff turnover affect outcomes in group CPT for persistent pain. Pain, 148(3), 481–486. https://doi.org/10.1016/j.pain.2009.12.011

77.

Woltmann

E. M.

Whitley

McHugo

G. J.

Brunette

Torrey

W. C.

Coots

Lynde

D. D.

Drake

R. E.

(2008). The role of staff turnover in the implementation of evidence-based practices in mental health care. Psychiatric Services, 59(7), 732–737. https://doi.org/10.1176/ps.2008.59.7.732

78.

Workforce Innovation and Opportunity Act of 2014. Pub. L. 113-128. (2014, July 22). U.S.C. 29 ss. 3101, et. seq.

79.

Yang

(2011). Job embeddedness: A new perspective to predict voluntary turnover. Nakaibusiness Review International, 2, 418–446. https://doi.org/10.1108/20408741111178834

80.

Zanskas

Strohmer

D. C.

(2011). The state-federal rehabilitation counselor work environment. Journal of Applied Rehabilitation Counseling, 42(3), 3–11. https://doi.org/10.1891/0047-2220.42.3.3

81.

Zhai

Y. S.

Herbert

Coduti

(2021). Predictors of intent to leave current employment among rehabilitation counselors. Journal of Rehabilitation, 86(1), 32–40.