Predicting vocational self-efficacy of unemployed adults with disabilities

Abstract

BACKGROUND:

People with disabilities experience barriers to the labor market. Self-efficacy is frequently cited as a barrier to engaging in the workforce, and vocational self-efficacy influences participation in employment. Understanding factors that predict vocational self-efficacy will help promote the inclusion of people with disabilities in the workforce.

OBJECTIVE:

To examine factors that may predict the vocational self-efficacy of unemployed people with disabilities.

METHODS:

Adults with disabilities completed an online survey including demographic, disability, social, and strengths-based factors through a crowdsourcing data-collection tool.

RESULTS:

Results from a hierarchical regression analysis suggests that the ability to connect with others, family support, adaptation to disability, and fatigue are significantly related to vocational self-efficacy when accounting for all other variables. No demographic variables significantly contributed to the prediction of vocational self-efficacy after accounting for other variables. The final model accounted for 43% of the total variance in vocational self-efficacy.

CONCLUSIONS:

Results provide new directions for addressing vocational self-efficacy. The most significant variables in the model represent modifiable factors to be directly or indirectly addressed through rehabilitation counseling. More research is needed to determine best practices for addressing these factors through the rehabilitation process and promote vocational self-efficacy and labor market participation of people with disabilities.

Keywords

Job placement vocational rehabilitation rehabilitation counseling

1 Introduction

People with disabilities are underrepresented in the workforce because of a long-standing mix of social, environmental, and individual barriers. Participation rates were only 20.6% for people with disabilities (PWD) compared to 68.9% for people without disabilities as of October 2019 (Bureau of Labor Statistics, 2019). Some roots of this discrepancy in employment are either not readily accessible to change or outside of the rehabilitation counselor’s scope of practice (e.g., negative societal depictions of disability and health complications requiring medical attention). In the recent push for effective interventions in rehabilitation counseling, researchers are emphasizing the identification of factors amenable to modification in rehabilitation counseling settings. Among these factors, self-efficacy is frequently noted for its relevance to counseling (e.g., Betz, 1992) and to people with disabilities (Klein et al., 1997; Lindley, 2006; Matthewson et al., 2015). Low levels of self-efficacy have been observed among some individuals with disabilities for a variety of reasons that span both person and environment (Strauser et al., 2015). For instance, self-stigma and depression related to disability have both been linked to lowered self-efficacy (Bandura, 1997; Corrigan et al., 2006). Social Cognitive Career Theory, one of the most well-validated career theories builds on Bandura’s work showing low self-efficacy’s negative effect on career interests, intentions, activity, and goal fulfillment (Betz & Hackett, 1981; Donnay & Borgen, 1999; Lent et al., 1994). Bandura argued that self-efficacy is task specific, and best understood within a specific domain such as employment (Woodruff & Cashman, 1993).

Despite the prominence of vocational self-efficacy (VSE) in rehabilitation counseling research, barriers and facilitators to VSE among unemployed adults with disabilities are largely unknown. Greater understanding of these factors may play an important role in increasing VSE of people with disabilities in the process of rehabilitation counseling and, by extension, their employment. We aim this research at identifying factors that predict VSE and that can guide rehabilitation counselors in their efforts to facilitate the employment of people with disabilities. In the present study, we used hierarchical linear regression to explore whether factors related to disability functioning, social environment, and personal strengths and positive characteristics predict VSE among adults with disabilities after controlling for demographic variables of age, gender, race, and education. We proceed with a review of the literature related to the selected variables and VSE.

Rehabilitation counselors seeking to assist individuals with disabilities in achieving employment goals need to understand personal processes that are amenable to intervention as possible targets for change in the counseling process. VSE is one such target that has a well-established relationship with employment outcomes (Michon et al., 2005; Smeets et al., 2007; Strauser & Berven, 2006; Waghorn et al., 2007). Social Cognitive Career Theory and Positive Psychology were used to inform the variables to examine in our study. The purpose of the present study is to explore whether factors related to disability functioning, social environment, and personal strengths and positive characteristics predict VSE among unemployed adults with disabilities.

1.1 Literature review

1.1.1 Vocational self-efficacy

The relationship between VSE and employment outcomes is well established (Michon et al., 2005; Moynihan et al., 2003; Smeets et al., 2007; Strauser & Berven, 2006; Waghorn et al., 2007). Bandura first conceptualized self-efficacy in his seminal work published in 1977. Years later, Betz and Hackett expanded the concept to the vocational arena (Betz & Hackett, 1981; Hackett & Betz, 1981). Their initial research focused on females having a lower self-efficacy to pursue male-dominated jobs. This reduced sense of self-efficacy resulted in females self-restricting their job options. Strauser (1995) and others later applied these concepts to people with disabilities and vocational rehabilitation (e.g., Luzzo et al., 1999). In more recent years, Strauser and colleagues showed a relationship between work behaviors self-efficacy and length of prior employment for participants with disabilities (O’Sullivan et al., 2012; Strauser et al., 2010). Others have shown a relationship between VSE and vocational rehabilitation engagement among people receiving vocational rehabilitation services (Dutta et al., 2017).

1.1.2 Demographic factors

Multiple demographic factors have been found to play a role in an individual’s perceived VSE. For instance, Chiesa and colleagues (2016) reported that both general and domain-specific self-efficacy tend to decrease with age among the general population. Specific to vocational self-efficacy, both Paggi and Jopp (2015) and Chiesa et al. (2016) reported that older workers espousing negative attitudes about their own aging were particularly likely to experience low VSE. As previously stated, gender has been a consideration of VSE from the origination of self-efficacy theory. Rottinghaus et al. (2009) found that gender was predictive of generalized self-efficacy. Brown and Lent (2013) showed systematically lower VSE for women in Science, Technology, Engineering, and Math (STEM) fields than their male counterparts. Regarding disability populations, multiple studies have shown gender differences to work differently among people with intellectual and learning disabilities than the general population (Nota et al., 2010; Panagos & DuBois, 1999). Nota et al. (2010) showed that the typical gendered preferences found among the general population (i.e., boys gravitating toward realistic and investigative areas and girls toward the social and artistic areas) were not present among individuals with intellectual disabilities. Finally, regarding education, O’Sullivan et al. (2012) found no relationship between VSE and educational level in people with disabilities. Although not the focus of this study, previous research suggests it is important to control for the selected demographic variables in the prediction of self-efficacy.

1.1.3 Disability functioning

The literature is mixed on the relationship between disability functioning and VSE. Nota, Ginevra, and Carrieri (2010) found that while individuals with intellectual disabilities showed higher self-efficacy in lower- to mid-level complexity occupations, the level of intellectual disability was not a factor in overall self-efficacy beliefs. For people with multiple sclerosis, level of functioning prior to becoming employed was related to self-efficacy beliefs; lower functioning was related to lower VSE (Clancy et al., 2013). Tenenbaum et al. (2014) found that age moderated the influence of functioning on VSE, but only for those who acquired disability in adulthood. The level of functioning did not predict VSE for those born with a disability or who acquired a disability in childhood. For people who acquired a disability in adulthood, age at onset affected the relationship between functioning and VSE—the older someone was upon acquisition of their disability the more limitations in functioning had the propensity to lower VSE (Tenenbaum et al., 2014). Symptomatology has also shown to be negatively correlated with self-efficacy and attainment of employment goals (Regenold et al., 1999). Fatigue, in particular, is a highly prevalent symptom of many chronic health conditions and has a negative impact on individuals’ ability to perform activities of daily living including work (Salomè et al., 2019).

1.1.4 Environmental factors

Two environmental factors, including family support for work and perceived community stigma were selected for consideration in the present study. The family of an individual with a disability can play a salient role in their environment and VSE. Family support has been shown to impact the VSE of an individual without disabilities as early in their development as the attachment stage. Specifically, Fouad and colleagues (2010) stated that individuals who experience higher levels of parental attachment endorsed greater career-related self-efficacy later in life. This relationship is present in adolescent women, with particular importance on the attachment to their mothers (Fouad et al., 2010). Career decision self-efficacy is influenced by elements of family support, such as informational support and high expectations (Fouad et al., 2010; Metheny & Mcwhirter, 2013). Turner and Lapan (2002) found that perceived parental support for occupation accounted for a substantial portion of the variance in VSE for 139 multiethnic middle school students. This connection of parental support with career self-efficacy has been shown in other studies as well (e.g., Restubog et al., 2010). Overall, family support has been found to play an important role in the development and endorsement of VSE for individuals without disabilities. Little is known about the relationship between family support and VSE specific to people with disabilities.

Watson and Larson (2006) referenced classical theories on the impact of stigma to conclude that individuals in marginalized groups experience a loss of self-efficacy and self-esteem. More recent findings indicate that this negative response is not universal to all group members. Observable indicators of harm from stigma comes from the experience of social rejection, including in the area of employment (Watson & Larson, 2006). The experience of community stigma, including negative beliefs, social distancing, and discrimination, can lead to internalization and decreased self-esteem (Shiloh et al., 2011). The relationship between stigma and self-esteem is relevant to this study because of the strong relationship between self-esteem and self-efficacy (Betz & Klein, 1996). This stigmatization translates to the workplace, both in the hiring and retention process (McMahon et al., 2008). Watson and Larson, in their work with individuals with mental illness, observed that for some members of marginalized groups, the response to stigma is activating rather than defeating, and may prompt individuals to advocate for themselves or others and does not have a negative impact on self-efficacy. There is a paucity of research directly considering the relationship between community stigma and VSE for people with disabilities, and this relationship remains poorly understood.

1.1.5 Strengths-based factors

Several strength-based factors were considered in their relationship to VSE in the present study, including adaptation to disability, positivity, impression management, and the ability to connect with others. Multiple studies have considered the role of self-efficacy in adaptation or adjustment to disability (Dennison et al., 2009; Hampton, 2004; Livneh, 2001; Wassem, 1992). Self-efficacy has been identified as one of two components making up the disability self-concept, where greater disability self-concept also predicted greater adaptation (Bogart, 2014). Typically, self-efficacy is modeled as predicting adaptation to disability; however, it is equally plausible in the dearth of causal evidence that adaptation to disability would promote self-efficacy as one pursues employment. Another strength-based factor is positivity. Dik et al. (2019) invoked the broaden-and-build theory of positive emotions, arguing that positive emotions have a strong role in supporting the human potential to grow and flourish, including within one’s career development. A relationship between positive affect and self-efficacy has been observed in previous study findings. For example, in a study on university students without disabilities, Rottinghaus et al. (2009) found that those with a greater positive affect reported greater self-efficacy. In another study of individuals with traumatic brain injury, vocational self-efficacy was predictive of subjective well-being. Happiness is also predictive of positive career outcomes (Boehm & Lyubomirsky, 2008). These findings suggest the need to further study the relationship between positivity and vocational self-efficacy among people with disabilities.

The last two variables in the set of strength-based factors focused on social interaction and included impression management and the ability to connect with others. Social participation has been shown to be positively correlated with general self-efficacy beliefs for PWD (Martins, 2015). Little is known about the relationship of impression management or the ability to connect with others to self-efficacy. That said, the relationship between social effectiveness and workplace success is well-established (e.g., Elksnin & Elksnin, 2001; Phillips et al., 2016; Phillips et al., 2014), and the ability to connect with others is a key predictor of workplace social effectiveness (Phillips et al., 2018). Similarly, Peck and Levashina (2017) found that impression management can influence employer perceptions of interview and performance at work. Greater ability to manage impressions and connect with others is likely to provide a positive feedback loop that enhances vocational self-efficacy.

2 Methods

2.1 Participants and procedure

The sample consisted of 192 working-age (18–65 years old) U.S. adults with disabilities who were not employed at the time of the survey. The mean age of participants was 40.98 years (SD = 12.48, range = 19–65), with 54 (28.1%) male, 136 (63.0%) female, and 2 (1.0%) not reported. A total of 162 (84.4%) identified as Caucasian and 30 (15.6%) as African American, Latinx/Hispanic, Asian or Pacific Islander, or other. A total of 3 participants (1.6%) did not hold a high school diploma or General Education Diploma (GED), 38 (19.8%) held a high school diploma or GED, 64 (33.3%) had received some post-secondary education, 31 (16.1%) held an associate degree, 45 (23.4%) held a bachelor’s degree, and 11 (5.7%) held a graduate degree. Primary disability, in order of prevalence, included 72 (37.5%) participants with depression or anxiety, 46 (24.0%) with a medical disability such as epilepsy or diabetes, 34 (17.7%) with a physical disability, 18 (9.4%) with a psychiatric disability other than depression or anxiety, and seven (3.6%) with alcohol or other substance abuse disorder. The remaining 15 participants reported a number of other primary disabilities, with none totaling more than 3.0% of the total population. Although primary disabilities were mutually exclusive, 139 (72.4%) of the 192 participants reported experiencing a comorbidity of one or more disabilities.

Human subject approval for this project was granted from the university’s Institutional Review Board. Participants were recruited through a combination of crowdsourcing data collection tools, namely: Amazon Mechanical Turk (MTurk) and TurkPrime. Both MTurk and TurkPrime have become common data collection tools used by thousands of researchers to obtain sample participants (See Sheehan, 2018 and Litman, Robinson, & Abberbock, 2017 for a description of these tools.). Participants were recruited from a panel of individuals known to have a disability or disabilities. Participants were administered an online survey via Qualtrics, with those who completed the survey receiving a $4.00 incentive payment.

2.2 Measures

The measures used in this study are described below. A summary of the measures and their descriptions can be viewed in Table 1.

Table 1
Descriptive Statistics for Scales Used in Study (n = 192)

Constructs Instruments # of Item M(SD) Resp. Range α

Predictor Variable Sets

Functioning

Activities of Daily Living Abbreviated WHODAS-2 5 18.13(5.13) 6–25 0.84

Fatigue PROMIS Fatigue - Short Form 4 14.63(4.47) 4–20 0.95

Environmental Factors

Family Support Family Climate Questionnaire 9 39.94(12.78) 9–63 0.94

Community Stigma Perceived Disability Stigma Scale 14 35.32(10.22) 14–70 0.84

Personal Participation Facilitators

Impression management Impression Management Scale 5 23.34(6.96) 5–35 0.90

Ability to Connect Ability to Connect Scale 7 35.44(7.81) 15–49 0.89

Positivity Positive Mindset Index 6 18.20(5.45) 6–30 0.88

Adaptation to Disability 9-Item Adaptation to Disability Scale-Revised 9 24.49(5.59 9–35 0.90

Outcome Variable

Vocational Self-Efficacy Life Skills Inventory-12 (LSI-12) 12 43.15(8.73) 12–60 0.89

Constructs	Instruments	# of Item	M(SD)	Resp. Range	α
Predictor Variable Sets
Functioning
Activities of Daily Living	Abbreviated WHODAS-2	5	18.13(5.13)	6–25	0.84
Fatigue	PROMIS Fatigue - Short Form	4	14.63(4.47)	4–20	0.95
Environmental Factors
Family Support	Family Climate Questionnaire	9	39.94(12.78)	9–63	0.94
Community Stigma	Perceived Disability Stigma Scale	14	35.32(10.22)	14–70	0.84
Personal Participation Facilitators
Impression management	Impression Management Scale	5	23.34(6.96)	5–35	0.90
Ability to Connect	Ability to Connect Scale	7	35.44(7.81)	15–49	0.89
Positivity	Positive Mindset Index	6	18.20(5.45)	6–30	0.88
Adaptation to Disability	9-Item Adaptation to Disability Scale-Revised	9	24.49(5.59	9–35	0.90
Outcome Variable
Vocational Self-Efficacy	Life Skills Inventory-12 (LSI-12)	12	43.15(8.73)	12–60	0.89

2.2.1 Vocational self-efficacy

Vocational self-efficacy was measured using the Life Skills Inventory - 12 (LSI-12; Tu, 2016). This scale is adapted from the original 30-item version. The LSI-12 consists of 12 items (e.g., “Complete a job application,” and “Communicate your accommodation needs to others.”), with each item rated on a 5-point Likert-type scale ranging from 1 (Very low ability) to 5 (Very high ability). A mean score was computed from all items, with higher scores indicating greater VSE. The Cronbach’s alpha for the full scale has been previously reported at 0.93 (Tu, 2016) and for this study at 0.894.

Ability to perform activities of daily living was measured using an abbreviated 5-item version of the WHODAS 2.0. The WHODAS 2.0 was used to measure limitations in self-care and physical functioning. This scale was abbreviated from the original 12-item version, which has been shown to measure more than a single factor of functioning (Smedema, Ruiz, & Mohr, 2017; Smedema et al., 2016). The abbreviated, 5-item WHODAS 2.0 asks participants how much difficulty they had in the past 30 days with various functions (e.g., “walking for a long distance,” and “washing your whole body.”), with each item rated on a 5-point Likert-type scale ranging from 1 (None) to 5 (Extreme or cannot do). A mean score was computed from all items, with higher scores indicating more limited functioning. The Cronbach’s alpha for the sample in the present study was found to be 0.832.

Fatigue was measured using the 4-item Patient-Reported Outcomes Measurement Information System (PROMIS) Fatigue-Short Form (Kratz et al., 2016). This scale consists of 4 items that ask about the level of fatigue experienced over the past 7 days (e.g., “I have trouble starting things because I am tired.,” and “How run-down did you feel on average?”), with each item rated on a 5-point Likert-type scale ranging from 1 (Not at all) to 5 (Very much). A mean score was computed from all items, with higher scores indicating greater fatigue. The Cronbach’s alpha for the sample in the present study was found to be 0.952.

2.2.2 Community stigma

The perceived stigma of the community was measured using the Perceived Disability Stigma Scale (PDSS). The PDSS was developed to assess perceptions of disability stigma within a community. Items were adapted from the Stigma Scale for Chronic Illness (SSCI; Kaya, 2019; Rao et al., 2009). The PDSS is a 14-item questionnaire given to people with disabilities to assess perceived stigma in their communities (e.g., “People in my community feel uncomfortable with persons with disabilities,” and “People in my community think persons with disabilities are dangerous.”). Each item is rated on a 5-point Likert-type scale ranging from 1 (Never) to 5 (Always). Responses are summed over the 14 items to produce a PDSS total score ranging from 14 to 70, with higher scores indicating higher level of perceived stigma. The Cronbach’s alpha for the sample in the present study was found to be 0.937.

2.2.3 Family employment support

Family employment support was measured using a modified version of The Family Climate Questionnaire (FCQ). The FCQ was adapted from the Health Care Climate Questionnaire (HCCQ; Williams, Grow, Freedman, Ryan, & Deci, 1996) by replacing the referent “health care” with “family” and by shortening the scale to 9 items (Tu, 2016). The 9-item FCQ (e.g., “I feel that my family provides me choices and options for my career” was rated on a 7-point Likert-type scale ranging from 1 (Strongly disagree) to 7 (Strongly agree), with higher mean scores representing a higher level of perceived autonomy support from family. The Cronbach’s alpha for the sample in the present study was found to be 0.940.

2.2.4 Ability to connect with others

The ability to connect with others was measured by a scale created for this project as a measure of how well participants felt they could experience and create a social connection with others (Phillips et al., 2018). The unidimensional scale consists of 7 items (e.g., “I work well with just about anyone.”) that are rated on a 7-point Likert-type rating scale (1 = Strongly disagree to 7 = Strongly agree). Higher scores indicate a greater ability to connect socially with others. The Cronbach’s alpha coefficient for the present study was computed to be 0.881.

2.2.5 Impression management

The ability to manage the impressions of others was measured using the Social Astuteness subscale of the Political Skill Scale (Ferris et al., 2005). This subscale consists of 5 items (e.g., “I understand people very well,” and “I pay close attention to people’s facial expressions.”) that are rated on a 7-item Likert-type rating scale (1 = Strongly disagree to 7 = Strongly agree). Higher scores indicate a greater ability to manage the impressions of others. The Cronbach’s alpha coefficient for the present study was computed to be 0.903.

2.2.6 Positivity

General positivity was measured using the Positive Mindset Index (PMI; Barry et al., 2014). This subscale consists of 6 items measuring participant levels of happiness, confidence, sense of being in control, stability, motivation, and optimism along a bi-polar 5-item Likert-type rating scale. For instance, happiness was measured from 1 (Very unhappy) to 5 (Very happy), with a neutral midpoint of (Moderately happy) in between. Higher scores indicate a greater level of overall positivity. The Cronbach’s alpha coefficient for the present study was computed to be 0.877.

2.2.7 Adaptation to disability

Adaptation to disability was measured using a 9-item version of The Brief Adaptation to Disability Scale-Revised (B-ADS-R; Lin et al., 2006). Three items were removed from the original scale for inclusion in the model due to the assumption of compromised physical functioning (e.g., “Physical wholeness and appearance make a person what he or she is.”). The remaining items (e.g., “There are many things a person with my disability is able to do.”) were rated on a 4-item Likert-type rating scale (1 = Strongly disagree to 4 = Strongly agree). Higher scores indicate a greater adaptation to disability. The Cronbach’s alpha coefficient for the present study was computed to be 0.841.

2.3 Data 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 vocational self-efficacy. The change in R² (ΔR²) was examined as a measure of each predictor set’s unique contribution to the model. Four blocks were entered, namely: (1) demographics (i.e., age, gender, race, and education); (2) functioning (i.e., activities of daily living and fatigue); (3) social environment (i.e., family support for employment and community stigma); and (4) strength-based factors (i.e., impression management, ability to connect with others, positivity, and adaptation to disability). This order of blocks was used to facilitate a more accurate understanding of the effect of the modifiable factors most relevant in rehabilitation counseling while controlling for the other predictors of vocational self-efficacy.

Significance tests for the regression coefficients for each predictor variable were assessed at each block and at the final model to assess unique relationships to the dependent variable (vocational self-efficacy). Prior to the analysis, assumptions of normality, linearity, homoscedasticity, and multicollinearity were tested and met.

3 Results

Hierarchical regression analysis was used to address the research question. Table 2 provides a correlation table for all variables, and Table 3 provides the results of the HRA.

Table 2
Intercorrelations for Variables Used in Structural Equation Model (n = 192)

Variable 1 2 3 4 5 6 7 8 9 10 11 12

1. Age 1 – – – – – – – – – – –

2. Gender 0.212^** 1 – – – – – – – – – –

3. Race –0.087^* –0.047 1 – – – – – – – – –

4. Education 0.123 –0.069 0.093 1 – – – – – – – –

5. ADLs –0.198^ –0.187^ 0.068 0.216^ 1 – – – – – – –

6. Fatigue –0.199^ 0.173^* –0.035 –0.208^ –0.389^ 1 – – – – – –

7. Family Support 0.120 0.006 –0.108 0.049 0.069 –0.148^* 1 – – – – –

8. Community Stigma –0.128 –0.001 0.023 0.006 –0.106 0.284^ –0.244^ 1 – – – –

9. IM 0.167^* 0.095 –0.029 0.109 –0.145^* 0.026 0.242^ –0.214^ 1 – – –

10. Ability to Connect 0.242^ 0.238^ –0.017 0.053 –0.097 –0.124 438^ –0.367^ 0.568^ 1 – –

11. Positivity 0.313^ 0.094 –0.026 0.122 0.022 –0.333^ 408^ –0.347^ 0.298^ 0.618^ 1 –

12. Adjustment to Dis. 0.190^ 0.071 –0.039 0.053 0.298^ –0.421^ 0.390^ –0.466^ 0.209^ 0.450^ 0.512^ 1

13. Vocational SE 0.194^ 0.125 0.068 0.125 0.183^* –0.310^ 0.483^ –0.316^ 0.318^ 0.531^ 0.421^ 0.512^**

Variable	1	2	3	4	5	6	7	8	9	10	11	12
1. Age	1	–	–	–	–	–	–	–	–	–	–	–
2. Gender	0.212^**	1	–	–	–	–	–	–	–	–	–	–
3. Race	–0.087^*	–0.047	1	–	–	–	–	–	–	–	–	–
4. Education	0.123	–0.069	0.093	1	–	–	–	–	–	–	–	–
5. ADLs	–0.198^**	–0.187^**	0.068	0.216^**	1	–	–	–	–	–	–	–
6. Fatigue	–0.199^**	0.173^*	–0.035	–0.208^**	–0.389^**	1	–	–	–	–	–	–
7. Family Support	0.120	0.006	–0.108	0.049	0.069	–0.148^*	1	–	–	–	–	–
8. Community Stigma	–0.128	–0.001	0.023	0.006	–0.106	0.284^**	–0.244^**	1	–	–	–	–
9. IM	0.167^*	0.095	–0.029	0.109	–0.145^*	0.026	0.242^**	–0.214^**	1	–	–	–
10. Ability to Connect	0.242^**	0.238^**	–0.017	0.053	–0.097	–0.124	438^**	–0.367^**	0.568^**	1	–	–
11. Positivity	0.313^**	0.094	–0.026	0.122	0.022	–0.333^**	408^**	–0.347^**	0.298^**	0.618^**	1	–
12. Adjustment to Dis.	0.190^**	0.071	–0.039	0.053	0.298^**	–0.421^**	0.390^**	–0.466^**	0.209^**	0.450^**	0.512^**	1
13. Vocational SE	0.194^**	0.125	0.068	0.125	0.183^*	–0.310^**	0.483^**	–0.316^**	0.318^**	0.531^**	0.421^**	0.512^**

Note:^*p < 0.05; ^**p < 0.01; PPWA = Physician-patient working alliance.

Table 3

Hierarchical Regression Analysis for Prediction of Vocational Self-Efficacy (n = 192)

			At entry into model			Final model
Variable	R²	Δ R²	B	SE B	β	B	SE B	β
Step 1	0.058	0.058^*
Age			0.11	0.05	0.15^*	0.03	0.04	0.05
Gender (Men)			1.98	1.41	0.10	1.68	1.15	0.09
Race (White)			1.75	1.71	0.07	2.41	1.33	0.10
Education			0.69	0.49	0.10	0.12	0.40	0.02
Step 2	0.159	0.101^***
ADLs			0.25	0.13	0.15	0.22	0.12	0.13
Fatigue			–0.49	0.15	–0.25^**	–0.27	0.13	–0.14^*
Step 3	0.367	0.208^***
Family Support			0.28	0.04	0.42^***	0.18	0.04	0.26^***
Community Stigma			–0.12	0.05	–0.14^*	–0.01	0.06	–0.01
Step 4	0.469	0.102^***
Impression Management			0.12	0.09	0.09	0.12	0.09	0.09
Ability to Connect			0.33	0.10	0.29^***	0.33	0.10	0.29^***
Positivity			–0.12	0.13	–0.08	–0.12	0.13	–0.08
Adaptation to Disability			0.27	0.13	0.18^*	0.27	0.13	0.18^*

Note: F(12, 177) = 12.96, p < 0.001 for the full model; ΔF(4, 185) = 2.86, p = 0.025, for Step 1; ΔF(6, 183) = 5.75, p < 0.001 for Step 2; ΔF(8, 181) = 13.01, p < 0.001 for Step 3. ^*p < = 0.05; ^**p < = 0.01; ^***p < = 0.001.

3.1 Research question

The question we sought to address through this study was whether the selected variable set adapted from the self-efficacy theory and positive psychology could each add to the explanation of variance in vocational self-efficacy for unemployed, working-age adults with disabilities. The four variable sets in order of entry were (a) demographics, (b) disability functioning, (c) environmental factors, and (d) strength-based factors. The change in R² (ΔR²) suggested that each set of predictors added significantly to the model at entry. Although significant, the personal demographic factors entered in Step 1 accounted for slightly less than 4% of the variance on vocational self-efficacy, R² = 0.058, F(4, 185) = 2.86, p = 0.025. Age was the only variable in the set that was predictive of vocational self-efficacy, with increases in age related to greater vocational self-efficacy (β= 0.15, p = 0.038). The inclusion of functioning in Step 2 accounted for an additional 10% of the variance, R² = 0.159, F(2, 183) = 10.93, p < .001. Fatigue was negatively correlated with vocational self-efficacy (β= 0.–25, p = 0.002) while activity of daily living restrictions did not significantly contribute to the model. These two sets of variables accounted for 15.9% of the variance. The addition of environmental factors predicted 20.8% more of the variance in vocational self-efficacy, R² = 0.367, F(2, 181) = 29.71, p < 0.001. Both family support for employment goals (β= 0.42, p < 0.001) and, to a lesser extent, perceptions of community stigma (β= 0.–14, p = 0.025) predicted vocational self-efficacy, with greater family support being associated with greater vocational self-efficacy and greater community stigma being associated with lower vocational self-efficacy. The fourth and final step included the entry of personal participation facilitating factors. Entry of this block of factors predicted an additional 10.2% of the model variance, R² = 0.469, F(4, 177) = 8.49, p < 0.001. Of the four variables from this set, only the ability to connect with others (β= 0.290, p = 0.001) and adaptation to disability (β= 0.18, p = 0.037) were significant.

The final model predicted 46.9% (43.3% using Adj. R²) of the total variance in vocational self-efficacy, with each set of factors significantly contributing to the model upon entry. All but the personal demographic factors set had at least one variable significantly predicting vocational self-efficacy in the final model. The significant variables in the final model were (from greatest standardized coefficient beta to smallest), the ability to connect with others (β= 0.29, p = 0.001), family support for employment goals (β= 0.26, p < 0.001), adaptation to disability (β= 0.18, p = 0.037), and fatigue (β= –0.14, p = 0.050).

4 Discussion and Implications

Our final model predicted 46.9% of the total variance in vocational self-efficacy, with ability to connect with others and family support providing the greatest contribution to the final model. As expected, both ability to connect with others and family support for employment goals were positively correlated with vocational self-efficacy, as expected. Adaptation to disability was also positively correlated with vocational self-efficacy, with higher adaptation predicting higher VSE, and fatigue was negatively correlated with vocational self-efficacy. We discuss implications of these findings for research and practice below.

Social effectiveness and social support have long been connected with employment outcomes. Results from this study suggest that these two pillars of employment have an equally important influence on perceptions of employment success. These findings provide yet another point of data supporting the focus on social aspects of the rehabilitation process (see Cottone, 2012; Elksnin & Elksnin, 2001; Phillips et al., 2016; Phillips, Robison, et al., 2014; Phillips, Kaseroff, et al., 2014). It is not surprising that perceptions of ability to connect with others is related to perceived ability to navigate the job search and hiring process, particularly considering the role of “likability” in hiring decisions. It is possible that the ability to connect with others is something that can be improved through rehabilitation counseling intervention. Improving clients’ ability to connect with others in pursuit of employment is aided by a greater understanding of what factors predict ability to connect. Phillips et al. (2018) found that both warmth and positivity increased the ability to connect with others, with warmth being demonstrated by a combination of sincerity, reciprocity, empathy, and humility and positivity by a combination of psychological capital, happiness, and a positive mindset. Rehabilitation counselors might consider these factors as a starting point for boosting a client’s ability to connect with others in the pursuit of employment. If modifiable in rehabilitation counseling settings, improving the ability to connect with others would go beyond improving vocational self-efficacy to improving participation and fostering support generally.

Results suggest that client vocational self-efficacy is closely related with family support for employment goals. Rehabilitation counselors may better facilitate client employment goals by recognizing and, when necessary, seeking to address and improve family support for the employment goals set by their clients. Rehabilitation counseling clients may not always discuss unsupportive family with their counselors. In fact, they may not be aware of its potential influence on vocational self-efficacy. Results suggest that rehabilitation counselors be proactive in seeking to understand family dynamics related to employment goals, particularly for clients presenting as unmotivated or anxious about pursuing work. Any efforts to intervene on the family system or the client’s perceptions of that system must, of course, be made based on the principle of respect for client and family as well as on the principle of client autonomy to involve the family in the change process to whatever degree the client chooses. These principles become critical when working with a client whose family culture or background is more communal than what the counselors may experience in their own family. It is also important that general family support not be confused with support for employment goals. Family members who provide strong support for other aspects of the client’s life may still be unsupportive of the client’s employment goals. Additional counseling may be directed at broadening the clients’ consideration of “family” and seeking support from friends and close others where family support is lacking.

Another factor predicting VSE is adaptation to disability. Understandably, a person who does not feel comfortable with their disability status is less likely to feel confident in their ability to achieve employment. Rehabilitation counselors must continue to make adaptation to disability a central part of the counseling process whenever the need is recognized. More work is needed to build upon the work of Beatrice Wright and others to identify effective counseling practices for facilitating adaptation. Along with other healthcare services, interventions related to identity conceptualization, coping skills, and psychoeducation can be helpful in facilitating adaptation to disability. Based on the level of attention received in public schools and society generally, both those with congenital and acquired disability are likely to know little if anything about the disability rights movement or the individuals with disabilities who initiated it and carry on its work today. One path to supporting adaptation might include rehabilitation counselors providing connections to community organizations, programs, and events that highlight the strengths and competence of persons with disabilities (e.g. Centers for Independent Living, Diversability events, leadership development programs). Counseling may also be directed at connecting clients with other individuals with disabilities for peer mentoring or experience sharing for additional support, strategies, and resources to help with coping and adaptation.

Fatigue was the only negative indicator of vocational self-efficacy identified in the study. General functioning as measured by activities of daily living was not significant. Although reduction of fatigue may sometimes call for greater collaboration with other health and human services, rehabilitation counselors can still do much to recognize and address this diminisher of vocational self-efficacy in the typical vocational rehabilitation process. Given these results, vocational rehabilitation counselors may be able to support successful employment outcomes by helping clients to reduce fatigue by encouraging wellness, attention to physical health, and the reduction of anxiety or other mental and behavioral causes of fatigue. Additional efforts may be implemented to help clients consider and pursue employment opportunities that are more flexible and adaptive to accommodate for possible fatigue during the work day, for example, self-employment or part-time employment.

Finally, it is worth mentioning the variables that were significant upon entry but that no longer remained significant in the final model. These include age and community stigma, with age being positively correlated and community stigma being negatively correlated with vocational self-efficacy. In contrast to previous findings about the relationship of age and self-efficacy where increased age was related with decreasing efficacy, efficacy went up with age in this sample (Chiesa et al., 2016). This could be the result of our limiting this sample to those who were 65 years old and younger in combination with the domain specific emphasis on employment self-efficacy. Regardless, the significance of age diminished with the inclusion of other modifiable factors, suggesting an opportunity to overcome any age-related effects on vocational self-efficacy. Regarding community stigma, there is promise in the findings that the negative effect of community stigma on vocational self-efficacy can be at least partially ameliorated through individual strength-based factors. This finding should not induce complacency about community stigma experienced by clients with disabilities but does provide alternative or complementing pathways to success in situations where the community stigma is not easily addressed.

Our research aimed at identifying factors that contribute to the vocational self-efficacy experienced by unemployed people with disabilities. The results point to multiple indicators of vocational self-efficacy that are within the scope of practice for rehabilitation counselors to address, with the hope that doing so might lead to better employment outcomes. In contrast, the more stable factors of age, gender, race, and level of functioning did not account for a significant amount of the variance in vocational self-efficacy, providing even greater motivation to focus on malleable factors. Suggestions were given for addressing the most significant predictors of vocational self-efficacy among unemployed adults with disabilities, namely: the ability to connect with others, family support, adaptation to disability, and fatigue.

Future research is needed to further elucidate the relationship between vocational self-efficacy and selected variables. Research may also be useful that considers the relationship between independent variables more closely. Some future directions include the targeting of more diverse racial and ethnic samples to gain greater insights about the generalizability of the findings. Another need from this research includes testing the model with specific disability populations. It seems possible that different populations (people with visible vs. invisible disability, congenital or acquired disability, stable or progress disability, physical vs. psychiatric disability, etc.) may experience vocational self-efficacy differently than what was shown from these findings with a general disability population. Finally, studies of individuals who are employed, as well as longitudinal studies to determine if VSE does indeed predict future employment success would allow for greater understanding of this construct.

4.1 Limitations

Although the findings make a meaningful contribution to our understanding of factors that predict vocational self-efficacy, these results must be understood in context of the limitations. First, the data was 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 social factor of ability to connect. Where we may be most interested in the individual’s perceptions of perceived community stigma or friend and family support, it may be helpful to obtain others’ perceptions of ability to connect and impression management in future studies to overcome any limitations in awareness or perception.

Also, we frame the two significant social factors in this model as individual (i.e., ability to connect) and environmental factors (i.e., family support for employment). That said, more research is needed to clarify the person-environment interaction. At present, participant perceptions of their ability to connect with others may be the result of individual strengths and characteristics that are sustained across social environments or the result of being in or seeking out social environments that facilitate social connection. Similarly, family support for employment goals may be the result of a characteristically positive family environment or the result of an individual using his or her ability to connect and other social skills to facilitate a supportive family environment. The truth likely falls somewhere between these possibilities for both of these social factors. One implication for rehabilitation counselors is that social factors must be a priority consideration in the vocational rehabilitation process and will include consideration of the person, the environment, and their interaction. This focus may lead to social skills training in one set of circumstances, social accommodation in another, or perhaps more often, a combination of both. Finally, the use of crowdsourcing tools to collect data, as with the use of most convenience samples, requires caution in generalizing the results to other target populations.

4.2 Conclusion

Assisting PWDs to access desired work opportunities is central to the mission of rehabilitation counseling; therefore, research on VSE in PWDs is important to the field of rehabilitation to gain an understanding of the crucial factors that can foster purposeful and rewarding work. The present study identified factors that can be targeted by rehabilitation counselors to improve VSE in PWDs, with family support and ability to connect playing the largest roles. While less influential, adaptation to disability and fatigue also appear to influence VSE in PWDs and can be addressed in the rehabilitation setting to improve VSE. Further research on the mechanisms of whether and how these factors impact VSE and best practices for targeting said factors in counseling is recommended to shed light on how to assist PWDs in improving their VSE.

Conflict of interest

The authors declare that they have no conflict of interest. Procedures involving experiments on human subjects were done in accord with the ethical standards of the Committee on Human Experimentation of the institution in which the experiments were done or in accord with the Helsinki Declaration of 1975.

Funding

This research was partially supported by the National Institute on Disability, Independent Living and Rehabilitation Research Grant #H133B13001 to Virginia Commonwealth University, Rehabilitation Research and Training Center on Employment of People with Physical Disabilities. The opinions expressed herein do not necessarily reflect the endorsement or position of the U.S. Department of Health and Human Services.

Footnotes

Acknowledgments

None.

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