Career satisfaction among working age individuals with multiple sclerosis or spinal cord injury

Abstract

BACKGROUND:

Joint studies of multiple sclerosis (MS) and spinal cord injury (SCI), both types of spinal cord dysfunction, have identified barriers and facilitators to employment and key outcomes including earnings and job benefits. However, there has been an absence of research on satisfaction with employment over one’s career. Such knowledge would help to establish the foundation for targeted vocational rehabilitation interventions. as well as differences in quality of employment outcomes, establishing the foundation for targeted vocational rehabilitation interventions.

OBJECTIVE:

Identify and quantify demographic, educational, vocational, and functional characteristics associated with career satisfaction, a quality employment outcome, among people with MS and SCI.

METHODS:

There were a total of 3,371 participants, 1,229 with MS and 2,142 with SCI, all of whom were in the traditional working age range (< 65 years old). Participants were identified from the Southeastern and Midwestern regions of the USA and data were collected, processed, and analyzed at a medical university in the Southeastern USA. Econometric modeling identified factors associated with career satisfaction, as defined by a five-item composite scale that was converted to Z-scores, integrating analysis using both diagnostic groups.

RESULTS:

Participants who were gainfully employed and those who had left the labor force (unemployed with no hopes to return to work), reported higher career satisfaction than those who were unemployed and hoping to return to work. Higher career satisfaction scores were associated with more formal education and having worked in management/professional, natural resources, or service occupations. Higher scores were also observed among those older, not single, and who had fewer functional deficits. No differences were identified as a function of diagnosis, race-ethnicity, sex, or time since injury/diagnosis.

CONCLUSION:

Career satisfaction was more strongly related to educational attainment, vocational history, and labor force participation, than to demographic and disability factors. Vocational counselors should target those still in the labor force for skills development, job retention, and reacquisition to promote career satisfaction.

Keywords

Multiple sclerosis spinal cord injuries job satisfaction employment rehabilitation vocational

1 Introduction

Gainful employment is fundamental to adult life and brings financial benefit, self-fulfillment, greater quality of life, and well-being (Modini et al., 2016). Unfortunately, people with disabling conditions experience unemployment rates that are substantially higher than those without disabilities (Bureau of Labor Statistics [BLS], 2020). The majority of this research is limited to identification of employment rates across disabling conditions, typically defined by the presence of at least one limitation in activities for daily living (BLS, 2020). Other studies have focused specifically on certain disabling conditions. These approaches typically do not directly compare outcomes between diagnoses, even when there are distinct similarities in functional limitations, causes, or underlying complications.

Spinal cord dysfunction, whether traumatic or disease related, often presents significant challenges to gainful employment and career accomplishments. There is a substantial body of research focused on employment following multiple sclerosis (MS; Bishop & Rumrill, 2015; Dorstyn et al., 2019; Kavaliunas et al., 2021; Pompeii et al., 2005; Raggi et al., 2016; Schiavolin et al., 2013; Sweetland et al., 2012; Vijayasingham & Mairam, 2018) and spinal cord injury (SCI; Bloom et al., 2020; Ottomanelli & Lind, 2009; Young & Murphy, 2009), two neurologic conditions with spinal cord dysfunction that cause significant disability. The most recent estimates suggest there are nearly one million people living with MS (Nelson et al., 2019; Wallin et al., 2019), and approximately 296,000 individuals (range of 252,000 to 373,000) living with SCI in the United States (National SCI Statistical Center [NSCISC], 2018). MS is typically diagnosed between the ages of 20–50 (National Multiple Sclerosis Society, 2021), and the average age at SCI onset is 43 years old (NSCISC, 2018). Research from the United States suggests that less than half (41–48%; Krause, Dismuke-Greer, Jarnecke, et al., 2019; Roessler et al., 2015) of adults with MS are employed at a given time. Among those with SCI, the reported rate of unemployment is typically somewhat higher, varying between 68–82% (NSCISC, 2021; Krause, Dismuke-Greer, Reed, et al., 2019). This is concerning, as employment has been shown to be protective for adverse physical and mental health outcomes (Andrade et al., 2016; Rueda et al., 2011) and lapses in employment could lead to poor long-term outcomes, including diminished career satisfaction.

Two studies have been conducted comparing quality outcomes, earnings(Krause et al., 2020) and benefits (Krause, Dismuke-Greer, Reed, et al., 2019) between those with MS and SCI within the same data analysis, allowing for direct comparisons between diagnoses, as well as determination of the importance of individual ADLs across diagnoses. These comparisons have highlighted favorable outcomes for those with MS, while at the same time quantifying the relationships of functional limitations and policy factors on quality employment outcomes. Additional research has indicated important differences in barriers and facilitators to employment between those with MS and SCI, helping to highlight targets for intervention across diagnoses, as well as those specific to a particular diagnosis (Krause et al., 2021). Taken together, these studies establish the blueprint for an integrated approach to the study of disability and quality employment, jointly analyzing data from more than diagnosis to identify key similarities and differences in outcomes. However, more quality outcomes need to be investigated, including those that reflect outcomes over the full work life, rather than outcomes only relevant to current job.

Satisfaction with employment is another important indicator of quality employment. Although there has been an emphasis in the vocational literature on job satisfaction after MS (Li et al., 2017; Rumrill et al., 2004, 2015, 2020) or SCI (Meade et al., 2015; Wehman et al., 2000), it reflects satisfaction with a single job, similar to the focus on current employment status. On the other hand, studies of career satisfaction, as defined by an individual’s satisfaction across their work life or progress they have made on achieving their career goals has been noticeably absent. Considering the recent studies comparing quality employment outcomes between those with MS and SCI (Krause, Dismuke-Greer, Reed, et al., 2019; Krause, Dismuke-Greer, Reed, et al., 2020; Krause et al., 2021), and the limited understanding of career satisfaction in these populations, there is a need for further investigation. Studying both MS and SCI together can help us understand to the factors common to both diagnoses that may lead to better career satisfaction, and ultimately improve outcomes for both employers and employees.

1.1 Purpose

Our purpose was to identify and quantify factors associated with career satisfaction among people with MS and SCI within the traditional working age range (18–64), using multiple demographic, educational, functional, and labor force participation characteristics as predictors. Identifying differences in career satisfaction within a single analysis that combines SCI and MS allows for modeling that generalizes across groups, while also identifying differences as a function of diagnostic group. This will enhance understanding of quality employment and inform practice and policy.

2 Methods

2.1 Participants

Institutional review board approval was obtained prior to enrollment. All participants had a diagnosis of either MS or SCI. Data were collected for people with MS and SCI separately, then merged for the current analyses. The SCI data were collected first, followed by the MS cohort using the same blueprint; this approach is planned for future enrollment of additional diagnostic groups.

MS participants were enrolled from a specialty hospital, using the following eligibility criteria: adult with MS, at least 1-year post diagnosis and under the traditional retirement age of 65 at diagnosis. Out of a pool of 3,291 individuals, 1,332 participants completed self-report assessments (686 could not be contacted by phone and 1,272 were nonrespondents).

The participants with SCI were enrolled from the same specialty hospital that was used to identify MS participants or two state-based surveillance systems. All SCI participants were adults under the age of 65 at SCI onset. There were 2,824 respondents from an initial pool of 4,248 potential participants from two SCI longitudinal studies. The combined cohort of both people with MS and SCI was 4,162. The cohort was reduced to 3,371 by eliminating those 65 and older at the time of the study.

2.2 Data collection procedures

Participants were mailed a recruitment letter to describe the self-report study. Assessment packages were mailed to participants 4–6 weeks later. Participants were given the option of completing the measure online. There were 2 follow-up mailings and a follow-up phone call to reach non-respondents. Participants received $50 in remuneration.

2.3 Measures

Several items were developed as part of a larger assessment of employment after SCI and MS, including 5 items regarding career satisfaction, each of which is measured on a five-point scale (1 = not at all satisfied, 5 = very satisfied). The items include: 1) the success I have achieved in my career, 2) progress I have made in meeting my overall career goals, 3) progress I’ve made toward meeting my goals for income, 4) progress I’ve made toward meeting my goals for advancement, and 5) progress I’ve made toward meeting my goal or advancement of new skills.

Covariates include the following demographic, diagnostic and functional deficits, and employment and occupational factors: race ethnicity, chronologic age, relationship status, diagnosis, years since injury/diagnosis, functional limitations, post-disability labor force participation, and occupational classification. The 10 functional limitations were measured using items from two sources, including the United States Census Bureau’s 2009 Current Population Survey (CPS; U.S. Census Bureau, 2009) and the 2014 Medicare Health Outcomes Survey. The CPS includes six dichotomous items to describe differences in function as related to diagnosis, including: difficulty concentrating, remembering, or making decisions; doing errands alone; seeing; and hearing. The 2014 Medicare Health Outcomes Survey also includes six items which assessed difficulty doing activities without special equipment or help from another person, including: bathing, dressing, eating, getting in or out of chairs, walking, or using the toilet (Centers for Medicare and Medicaid Services, 2014). We eliminated two redundant items reducing the total to 10. Occupation was coded using the Standard Industrial Classification (SIC) Codes from the Department of Labor (Bureau of Labor Statistics, 2010), which identified five occupational groups. Labor force participation was measured by a single item and three response options: currently employed, unemployed but with hope to return to work, and unemployed with no hope to return to work (i.e., out of the labor force).

2.4 Data analyses

Basic descriptive analyses were generated using SPSS v25 for Windows (IBM Corp., Armonk, NY, USA). Principal axis factoring was used to identify the dimensionality of the 5 satisfaction items. These items were summed to form a total score and reliability analysis was conducted, generating Cronbach’s alpha. Econometric modeling was used to estimate the association of the covariates with career satisfaction using STATA 15.0 (StataCorp LCC, College Station, TX, USA). We examined the adjusted satisfaction z-score using a generalized linear model with Gaussian family and identity link since the z-score provides a normal distribution. Marginal effects of each covariate are reported and can be interpreted directly. The covariates included: race-ethnicity (reference = non-Hispanic Black), sex (reference = male), age at diagnosis/injury (reference = 0–39 years), relationship status (reference = never married), diagnosis (reference = MS), years since injury/diagnosis (reference = less than 9 years), educational milestone (reference = high school certificate or less), labor force participation (reference = not employed, hopes to work), functional limitations (reference = yes), and occupation (reference = occupation production/transportation/material moving /those who had not been employed post diagnosis/injury).

3 Results

3.1 Descriptive

Just over half of the participants were male (54.3%) and the majority were non-Hispanic white (71.2%; see Table 1). Most participants had SCI (63.5%). Combined, the majority (39.7%) had 9 or less years since injury/diagnosis. The portion of people indicating limitations with activities of daily living ranged from a low of 4.2% for difficulty hearing to a high of 62.8% for difficulty walking. In terms of education, 39.7% of participants had a high school certificate or less, 22.9% completed trade school or a two-year school, 21.9% had a four-year degree, and 15.5% had postgraduate training. For occupations, the highest percent was for management/professional (25.8%), whereas the lowest percentage was for production/transportation/material moving (3.5%; those unemployed did not indicate any occupation).

Table 1
A comparison of participants demographics, functional, educational, and employment characteristics as a function of diagnosis

Characteristic SCI MS Total p-value

N 2,142 1,229 3,371

%

Age at diagnosis/injury < 0.001

39 or less 51.0 28.3 42.8

40–49 20.4 33.5 25.2

50–59 17.2 28.4 21.3

60> 11.4 9.8 10.8

Race-ethnicity < 0.001

Non-Hispanic White 74.5 65.9 71.2

Non-Hispanic Black 19.6 27.5 22.3

Hispanic other 6.0 6.6 6.2

Marital status < 0.001

Divorced/widowed/separated 22.7 20.0 21.7

Never married 32.4 15.3 26.6

Couple 44.9 64.7 51.7

Sex < 0.001

Female 27.2 78.3 45.7

Male 72.8 21.8 54.3

Time since diagnosis/injury < 0.001

9 years or less 36.1 45.9 39.7

10–19 years 35.3 39.3 36.7

20 or more years 28.6 14.8 23.6

Activities of daily living

Difficulty walking 75.1 41.3 62.8 < 0.001

Difficulty bathing 48.4 22.8 39.1 < 0.001

Difficulty dressing 41.2 20.0 33.5 < 0.001

Difficulty eating 16.9 9.2 14.1 < 0.001

Difficulty getting in or out of chairs 46.3 24.6 38.4 < 0.001

Difficulty using the toilet 50.5 15.4 37.8 < 0.001

Difficulty concentrating 19.8 40.3 27.1 < 0.001

Difficulty hearing 3.8 4.9 4.2 0.137

Difficulty seeing 3.0 11.6 6.1 < 0.001

Difficulty shopping 36.2 30.6 34.2 0.001

Education < 0.001

High school certificate or less 46.6 27.4 39.7

Vocational or Associates degree 23.3 22.2 22.9

Bachelor’s degree 18.0 28.9 21.9

Postgraduate training 12.1 21.5 15.5

Occupational field < 0.001

Production/transportation/material moving 7.4 2.9 3.5

Service 13.5 8.1 7.0

Management/professional 35.4 47.9 25.8

Sales/office 32.4 37.2 21.8

Natural resources, construction, and maintenance 11.3 3.9 5.1

Characteristic	SCI	MS	Total	p-value
	%
Age at diagnosis/injury				< 0.001
39 or less	51.0	28.3	42.8
40–49	20.4	33.5	25.2
50–59	17.2	28.4	21.3
60>	11.4	9.8	10.8
Race-ethnicity				< 0.001
Non-Hispanic White	74.5	65.9	71.2
Non-Hispanic Black	19.6	27.5	22.3
Hispanic other	6.0	6.6	6.2
Marital status				< 0.001
Divorced/widowed/separated	22.7	20.0	21.7
Never married	32.4	15.3	26.6
Couple	44.9	64.7	51.7
Sex				< 0.001
Female	27.2	78.3	45.7
Male	72.8	21.8	54.3
Time since diagnosis/injury				< 0.001
9 years or less	36.1	45.9	39.7
10–19 years	35.3	39.3	36.7
20 or more years	28.6	14.8	23.6
Activities of daily living
Difficulty walking	75.1	41.3	62.8	< 0.001
Difficulty bathing	48.4	22.8	39.1	< 0.001
Difficulty dressing	41.2	20.0	33.5	< 0.001
Difficulty eating	16.9	9.2	14.1	< 0.001
Difficulty getting in or out of chairs	46.3	24.6	38.4	< 0.001
Difficulty using the toilet	50.5	15.4	37.8	< 0.001
Difficulty concentrating	19.8	40.3	27.1	< 0.001
Difficulty hearing	3.8	4.9	4.2	0.137
Difficulty seeing	3.0	11.6	6.1	< 0.001
Difficulty shopping	36.2	30.6	34.2	0.001
Education				< 0.001
High school certificate or less	46.6	27.4	39.7
Vocational or Associates degree	23.3	22.2	22.9
Bachelor’s degree	18.0	28.9	21.9
Postgraduate training	12.1	21.5	15.5
Occupational field				< 0.001
Production/transportation/material moving	7.4	2.9	3.5
Service	13.5	8.1	7.0
Management/professional	35.4	47.9	25.8
Sales/office	32.4	37.2	21.8
Natural resources, construction, and maintenance	11.3	3.9	5.1

Those with MS were more likely to be female (MS = 78.3%; SCI = 21.8%), have higher education (21.5% with postgraduate training compared to 12.1% in those with SCI), and be employed in the management/professional sector (47.9% compared with 35.4% for SCI). The highest endorsed limitation in people with MS was difficulty walking (41.3%), followed by difficulty concentrating (40.3%) and difficulty shopping (30.6%). Likewise, the highest endorsed limitation for those with SCI was also walking (75.1%), then followed by difficulty using the toilet (50.5%), difficulty bathing (48.4%), and difficulty getting in or out of chairs (46.3%)

Principal axis factor analysis indicated a single factor with an eigenvalue over 1.0 (4.04), accounting for 76% of the variance. The alpha coefficient was .95 indicating a high level of internal consistency. Therefore, the items measured a single reliable factor which we label satisfaction.

3.2 Econometric modeling

There were multiple sets of significant predictors of career satisfaction (Table 2). In terms of demographic factors, older age at injury/diagnosis was related to higher career satisfaction, as each successively older age group was associated with incrementally higher career satisfaction. Being part of a couple was also associated with greater career satisfaction compared with never married. Neither sex nor race-ethnicity were significant.

Table 2
Econometric analysis of career satisfaction related to demographic, diagnostic, functional, educational, and vocational characteristics

Characteristic Coef. 95% CI p-value

Age at diagnosis/injury (ref: 39 or less)

40–49 0.151 0.069 0.233 < 0.001

50–59 0.323 0.229 0.417 < 0.001

60> 0.489 0.368 0.609 < 0.001

Race-ethnicity (ref: non-Hispanic Black)

Non-Hispanic White 0.043 –0.037 0.123 0.291

Hispanic other –0.019 –0.160 0.121 0.788

Marital status (ref: never married)

Divorced/widowed/separated 0.088 –0.008 0.184 0.074

Couple 0.179 0.098 0.259 < 0.001

Sex (ref: male)

Female 0.058 –0.015 0.131 0.121

Cohort (ref: MS)

SCI 0.006 –0.090 0.092 0.898

Time since diagnosis/injury (ref: 9 years or less)

10–19 years 0.029 –0.046 0.103 0.454

20 or more years 0.116 0.022 0.210 0.015

Activities of daily living

Difficulty walking 0.032 –0.056 0.120 0.477

Difficulty bathing –0.103 –0.213 0.008 0.068

Difficulty dressing 0.088 –0.024 0.201 0.122

Difficulty eating 0.058 –0.051 0.166 0.300

Difficulty getting in or out of chairs –0.061 –0.161 0.039 0.229

Difficulty using the toilet –0.035 –0.139 0.070 0.517

Difficulty concentrating –0.202 –0.279 –0.124 < 0.001

Difficulty hearing 0.051 –0.108 0.210 0.530

Difficulty seeing –0.058 –0.197 0.082 0.416

Difficulty shopping –0.095 –0.178 –0.013 0.023

Education (ref: high school certificate or less)

Vocational or Associate’s degree 0.176 0.094 0.258 < 0.001

Bachelor’s degree 0.370 0.281 0.459 < 0.001

Greater than bachelor’s degree 0.487 0.385 0.588 < 0.001

Labor Force Participation (ref: unemployed with hope to return to work)

Unemployed, no hope 0.408 0.299 0.518 < 0.001

Employed 0.484 0.394 0.574 < 0.001

Occupational field (ref: production/transportation/material moving, never employed)

Service 0.149 0.008 0.290 0.039

Management/professional 0.0227 0.125 0.329 < 0.001

Sales/office 0.026 –0.071 0.124 0.596

Natural resources, construction, and maintenance 0.211 0.056 0.365 0.007

Characteristic	Coef.	95% CI	p-value
Age at diagnosis/injury (ref: 39 or less)
40–49	0.151	0.069	0.233	< 0.001
50–59	0.323	0.229	0.417	< 0.001
60>	0.489	0.368	0.609	< 0.001
Race-ethnicity (ref: non-Hispanic Black)
Non-Hispanic White	0.043	–0.037	0.123	0.291
Hispanic other	–0.019	–0.160	0.121	0.788
Marital status (ref: never married)
Divorced/widowed/separated	0.088	–0.008	0.184	0.074
Couple	0.179	0.098	0.259	< 0.001
Sex (ref: male)
Female	0.058	–0.015	0.131	0.121
Cohort (ref: MS)
SCI	0.006	–0.090	0.092	0.898
Time since diagnosis/injury (ref: 9 years or less)
10–19 years	0.029	–0.046	0.103	0.454
20 or more years	0.116	0.022	0.210	0.015
Activities of daily living
Difficulty walking	0.032	–0.056	0.120	0.477
Difficulty bathing	–0.103	–0.213	0.008	0.068
Difficulty dressing	0.088	–0.024	0.201	0.122
Difficulty eating	0.058	–0.051	0.166	0.300
Difficulty getting in or out of chairs	–0.061	–0.161	0.039	0.229
Difficulty using the toilet	–0.035	–0.139	0.070	0.517
Difficulty concentrating	–0.202	–0.279	–0.124	< 0.001
Difficulty hearing	0.051	–0.108	0.210	0.530
Difficulty seeing	–0.058	–0.197	0.082	0.416
Difficulty shopping	–0.095	–0.178	–0.013	0.023
Education (ref: high school certificate or less)
Vocational or Associate’s degree	0.176	0.094	0.258	< 0.001
Bachelor’s degree	0.370	0.281	0.459	< 0.001
Greater than bachelor’s degree	0.487	0.385	0.588	< 0.001
Labor Force Participation (ref: unemployed with hope to return to work)
Unemployed, no hope	0.408	0.299	0.518	< 0.001
Employed	0.484	0.394	0.574	< 0.001
Occupational field (ref: production/transportation/material moving, never employed)
Service	0.149	0.008	0.290	0.039
Management/professional	0.0227	0.125	0.329	< 0.001
Sales/office	0.026	–0.071	0.124	0.596
Natural resources, construction, and maintenance	0.211	0.056	0.365	0.007

Two ADLs were negatively associated with career satisfaction. Those with difficulties with concentration and those who had difficulty shopping independently reported lower career satisfaction scores. None of the other ADLs were significant, nor was diagnosis or time since diagnosis/injury.

Educational attainment was strongly related to career satisfaction, with each successive level of educational attainment associated with higher career satisfaction scores relative to individuals who had not completed milestones of vocational training or an associate degree. Working in managerial/professional, natural resources/construction/maintenance, service, or sales/office occupations was associated with higher career satisfaction than either being unemployed or working in the production/transportation/material moving industry. Lastly, those who were currently employed reported higher career satisfaction than those who were unemployed, yet maintained hope to return to work. Those unemployed and out of the labor force (i.e., without hope of return to work) also reported higher career satisfaction scores than those who were unemployed and hoping to return to work.

4 Discussion

This study identified and quantified the relationships of several sets of covariates with career satisfaction among people with MS and SCI. Few studies have focused on quality employment outcomes reflecting outcomes beyond current employment, such as career satisfaction. Even though it is common practice to attempt to draw conclusions about employment outcomes among persons with different diagnoses by summarizing findings from different studies, each with their own data analysis, simultaneously investigating outcomes between multiple diagnoses into a single design with joint data analysis is the only way to generate valid comparisons. Our current findings help us better understand how career satisfaction is related to diagnostic and functional limitations, demographic characteristics, and modifiable education and employment factors, laying the foundation for targeted interventions to improve career satisfaction throughout the work lifecycle.

4.1 Implications for vocational Counseling, Policy, and Practice

The findings have several important implications. First, there is a great deal of career dissatisfaction among those who have worked since injury/diagnosis yet maintain hope to return to work. This underscores the importance of obtaining and retaining employment among those who wish to stay in the labor force. Dissatisfaction is motivating. Therefore, individuals who maintain hope for returning to work will make excellent candidates for vocational interventions. Counselors may also need to help individuals to critically evaluate the feasibility of employment and needs for additional training or support services. However, if gainful employment simply is not a feasible option, the individual may benefit from counseling to help them seek alternative types of rewarding activities and to remain productive in the absence of gainful employment and to transition to the next stage of life. Second, the relationships between level of education and working in management with level of career satisfaction emphasize the importance of quality employment. Targeting resources to improve the quality of employment will likely lead to greater career satisfaction. Third, not surprisingly, cognitive functional limitations are associated with lower career satisfaction, as is inability to complete important activities in the community (shopping). Although the latter likely reflects mobility considerations, it also could reflect social aspects of function that, if absent, have a carryover effect employment. The findings related to being part of a committed relationship further suggests the importance of social support to career satisfaction. Some of the same attributes that lead individuals to higher career satisfaction may be the same attributes that help individuals to establish social networks and achieve high educational levels. The current study does not address individual characteristics, such as personality or motivational factors.

Lastly, it was interesting that there were no sex or race-ethnicity differences in career satisfaction when accounting for all other predictive factors. It is well-documented that there are racial-ethnic disparities in multiple employment outcomes, including probability of employment, earnings and quality of employment benefits (Krause et al., 2008; Krause, Dismuke-Greer, Jarnecke, 2020; Krause, Dismuke-Greer, Reed et al., 2019. It is encouraging when we do not find sex or racial-ethnic disparities in employment outcomes and we did not find such disparities in career satisfaction, when accounting for other demographic, educational, and functional or diagnostic variables. However, we also need to be aware that there are disparities in education as a function of race-ethnicity which will lead to disparities and inequities in career satisfaction. In other words, even though there may not be inequities among those with similar education, there may be inequities in opportunity to achieve equal education, such that efforts are needed to promote equity in education among underserved groups.

4.2 Methodologic considerations

The key strengths were the inclusion of more than one diagnosis, evaluation of functional characteristics as well as diagnosis, and the use of econometric modeling to simultaneously evaluate several types of covariates in relation to career satisfaction. Conversion of satisfaction scores to Z scores allowed for quantification of the effects or relationships of each variable with career satisfaction. Combining multiple diagnoses in a single design, accounting for their separate effects, is not simply beneficial, it is necessary to draw valid comparisons. This simply cannot be done by studying each population individually and then comparing and contrasting their outcomes.

In terms of limitations, self-report measures may lead to errors in recall or interpretation. However, we elicited only basic and current information, except for occupation. Career satisfaction is, by definition, subjective. Second, the measurement of career satisfaction was straightforward and reliable, yet limited to global subjective assessments of satisfaction and not specific elements of for satisfaction. Third, the data were cross-sectional. Having longitudinal data over time would have further enhanced the study. Lastly, although we used multiple covariates, additional predictors would always be of interest. Specifically, it would be helpful to have more detailed information on quality of education, outside of the passing of educational milestones. It would also be beneficial to identify personality, motivational, and other psychological characteristics associated with higher career satisfaction.

4.3 Future research

Additional research is needed to better understand multiple long-term employment outcomes. Having longitudinal data would aid within this process of discovery. Second, given the findings related to hope for return to work, many people feel that their careers are falling short. Intervention studies are needed to identify how to better reintroduce employment to those who have worked after disability onset, yet have left the workforce, as these individuals have particularly poor career satisfaction. Lastly, research is needed to further identify factors related to additional neurologic conditions, such as TBI and stroke, using similar models that emphasize diverse covariates, as cognitive limitations were a primary predictor of career satisfaction. It is only through dedicated research that career satisfaction and other outcomes will improve among people with severe disabling conditions.

5 Conclusion

Career satisfaction is strongly related to educational and vocational history factors, particularly whether individuals are currently working or unemployed, yet still hoping to work. Although career satisfaction is positively associated with chronologic age, there are a number of factors that appear to impede or facilitate career satisfaction, including the presence of functional limitations.

Footnotes

Acknowledgments

None to report.

Conflict of interest

The authors have no conflict of interest to disclose.

Ethical approval

Ethical approval was obtained by the institutional review board of Medical University of South Carolina (approval numbers Pro00048236 (SCI) and Pro00048235 (MS) approved on 10/29/2015 and 11/12/2015, respectively).

Funding

The contents of this publication were developed under grants from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR grant numbers 90DP0050 and 90RT5035). NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this publication do not necessarily represent the policy of NIDILRR, ACL, HHS, and you should not assume endorsement by the Federal Government.

Informed consent

As the research presents no more than minimal risk, a waiver of the requirement to obtain written and signed consent was obtained. Return of a completed survey implied the participant’s agreement to participate.

References

Andrade,

F. C

, Wu,

, An,

, Stellrecht,

(2016) Employment status and health outcomes among Brazilian adults. International Health, 8(6), 413–422. https://doi.org/10.1093/inthealth/ihw030

Bishop,

, Rumrill,

P.D.

(2015) Multiple sclerosis: Etiology, symptoms, incidence and prevalence, and implications for community living and employment. Work, 52(4), 725–734. https://doi.org/10.3233/WOR-152200.

Bloom,

, Dorsett,

, McLennan,

(2020) Vocational rehabilitation to empower consumers following newly acquired spinal cord injury. Journal of Vocational Rehabilitation, 53, 131–144. https://doi.org/10.3233/JVR-201091.

Bureau of Labor Statistics. (2010). Standard Occupational Clas-sification. US Department of Labor. Retrieved April 6, 2011 from https://www.bls.gov/soc/major_groups.htm

Bureau of Labor Statistics. (2020). Persons with a disability: Labor force characteristics - 2020. U.S. Bureau of Labor Statis-tics, United States Department of Labor. https://www.bls.gov/news.release/disabl.nr0.htm.

Centers for Medicare and Medicaid Services. (2014). Medicare Health Outcomes Survey (HOS). https://hosonline.org/en/

Dorstyn,

D.S.

, Roberts,

R.M.

, Murphy,

, Haub,

(2019) Employment and multiple sclerosis: A meta-analytic review of psychological correlates. Journal of Health Psychology, 24(1), 38–51. https://doi.org/10.1177/1359105317691587

Kavaliunas,

, Danylaite Karrenbauer

, Hillert,

(2021) Socioeconomic consequences of multiple sclerosis: A systematic literature review, Neurologica Scandinavica143(6), 587–601. https://10.1111/ane.13411

Krause,

J. S.

, Dismuke-Greer,

C. E.

, Jarnecke,

, Li,

, Reed,

K. S.

, Rumrill,

. (2019) Employment and gainful earnings among those with multiple sclerosis. Archives of Physical Medicine and Rehabilitation, 100(5), 931–937. https://doi.org/10.1016/j.apmr.2018.11.005

10.

Krause,

J. S

, Dismuke-Greer,

C. E

, Jarnecke,

, Reed,

K. S.

(2020) Differential odds of employment and estimation of earnings among those with spinal cord injury. Rehabilitation Counseling Bulletin, 63(2), 67–78. https://doi.org/10.1177/0034355219848493

11.

Krause,

J.S.

, Dismuke-Greer,

C.E.

, Reed,

K.S.

, Rumrill,

(2019) Employment and job benefits among those with spinal cord dysfunction: A comon of people with spinal cord injury and multiple sclerosis. Archives of Physical Medicine and Rehabilitation, 100(10), 1932–1938. https://doi.org/10.1016/j.apmr.2019.05.031

12.

Krause,

J.S.

, Dismuke-Greer,

C.E.

, Reed,

K.S.

, Backus,

, Rumrill,

(2020) Gainful employment and earnings among those with spinal cord injury and mutiple sclerosis. Journal of Vocational Rehabilitation, 52(1), 19–28. https://doi.org/10.3233/JVR-191057

13.

Krause,

J. S.

, Li,

, Backus,

, Jarnecke,

, Reed,

, Rembert,

, Rumrill,

, Dismuke-Greer,

C.E.

(2021) Barriers and facilitators to employment: A comon of participants with multiple sclerosis and spinal cord injury. Archives of Physical Medicine and Rehabilitation, 102(8), 1556–1561. https://doi.org/10.1016/j.apmr.2021.02.015

14.

Krause,

J.S.

, Terza,

J.V.

, Dismuke,

(2008) Earnings among people with spinal cord injury. Archives of Physical Medicine and Rehabilitation, 89(8), 1474–1481. https://doi.org/10.1016/j.apmr.2007.12.040.

15.

Li,

, Roessler,

R.T

, Rumrill Jr

P.D.

, Ahmed,

E.R.

(2017) Factors influencing job satisfaction for employed adults with multiple sclerosis. Rehabilitation Counseling Bulletin, 61(1), 28–40. https://doi.org/10.1177/0034355216662616.

16.

Meade,

M.A.

, Reed,

K.S.

, Saunders,

L.L.

, Krause,

J.S..

(2015) It’s all of the above: Benefits of working for individuals with spinal cord injury. Topics in Spinal Cord Injury Rehabilitation, 21(1), 1–9. https://doi.org/10.1310/sci2101-1

17.

Modini,

, Joyce,

, Mykletun,

, Christensen,

, Bryant,

R.A.

, Mitchell,

P.B.

, Harvey,

S.B.

(2016) The mental health benefits of employment: Results of a systematic meta-review. Australasian Psychiatry, 24(4), 331–336. https://doi.org/10.1177/1039856215618523

18.

National Multiple Sclerosis Society. Who gets MS? (Epidemiology). https://www.nationalmssociety.org/What-is-MS/Who-Gets-MS.

19.

Nelson,

L.M.

, Wallin,

M.T.

, Marrie,

R.A.

, Culpepper,

W.J.

, Langer-Gould,

, Campbell,

, Buka,

, Tremlett,

, Cutter,

, Kaye,

, Wagner, Larocca,

N.G.

, United States Multiple Sclerosis Prevalence Workgrou (2019) A new way to estimate neurologic disease prevalence in the United States: Illustrated with MS. Neurology, 92((10)), 469–480. https://doi.org/10.1212/WNL.0000000000007044

20.

NSCISC. (2018, February). Spinal cord injury facts and figures at a glance. Birmingham, AL: University of Alabama.

21.

NSCISC. (2021). Spinal cord injury facts and figures at a glance. National SCI Statistical Center

22.

Ottomanelli,

, Lind,

(2009) Review of critical factors related to employment after spinal cord injury: Implications for research and vocational services. Journal of Spinal Cord Medicine. 32(5), 503–531. https://doi.org/10.1080/10790268.2009.11754553

23.

Pompeii,

L. A.

, Moon,

S.D.

, McCrory,

D. C.

(2005) Measures of physical and cognitive function and work status among individuals with multiple sclerosis: A review of the literature. Journal of Occupational Rehabilitation, 15(1), 69–84. https://doi.org/10.1007/s10926-005-0875-y

24.

Raggi,

, Covelli,

, Schiavolin,

, Scaratti,

, Leonardi,

, Willems,

(2016) Work-related problems in multiple sclerosis: A literature review on its associates and determinants. Disability and Rehabilitation38(10), 936–944. https://doi.org/10.3109/09638288.2015.1070295.

25.

Roessler,

R.T.

, Rumrill Jr

P.D.

, Li,

, Leslie,

M.J.

(2015) Predictors of differential employment statuses of adults with multiple sclerosis. Journal of Vocational Rehabilitation, 42, 141–152. https://doi.org/10.3233/JVR-150731

26.

Rueda,

, Raboud,

, Mustard,

, Bayoumi,

, Lavis,

J.N.

, Rourke,

S.B.

(2011) Employment status is associated with both physical and mental health quality of life in people living with HIV. AIDS Care, 23(4), 435–443. https://doi.org/10.1080/09540121.2010.507952

27.

Rumrill,

P.D.

, Roessler,

R.T.

, Li,

, Daly,

, Leslie,

(2015) The employment concerns of Americans with multiple sclerosis: Perspectives from a national sample. Work, 52(4), 735–748. https://doi.org/10.3233/WOR-152201

28.

Rumrill,

, Li,

, Strauser,

(2020) Personal, health and function, and career maintenance factors as determinants of quality of life among employed people with multiple sclerosis. Work, 67(1), 81–94. https://doi.org/10.3233/WOR-203254

29.

Rumrill,

, Roessler,

, Vierstra,

, Hennessey,

, Staples,

(2004) Workplace barriers and job satisfaction among employed people with multiple sclerosis: An empirical rationale for early intervention, Journal of Vocational Rehabilitation20(3), 177–183.

30.

Schiavolin,

, Leonardi,

, Giovannetti,

A.M.

, Antozzi,

, Brambilla,

, Confalonieri,

, Mantegazza, , Raggi,

(2013) Factors related to difficulties with employment in patients with multiple sclerosis: A review of literature. International Journal of Rehabilitation Research, 36(2), 105–111. https://doi.org/10.1097/MRR.0b013e32835c79ea

31.

Sweetland,

, Howse,

, Playford,

E.D.

(2012) A systematic review of research undertaken in vocational rehabilitation for people with multiple sclerosis, Disability and Rehabilitation34(24), 2031–2038. https://doi.org/10.3109/09638288.2012.669019

32.

US Census Bureau. (2009) Current Population Survey (CPS). https://www.census.gov/programs-surveys/cps/about.html.

33.

Vijayasingham,

, Mairami,

F.F.

(2018) Employment of patients with multiple sclerosis: The influence of psychosocial-structural coping and context. Degenerative Neurological and Neuromuscular Disease, 8, 15–24. https://doi.org/10.2147/DNND.S131729

34.

Wallin,

M.T.

, Culpepper,

W.J.

, Campbell,

J.D.

, Nelson,

L.M.

, Langer-Gould,

, Marrie,

R.A.

, Cutter,

G.R.

, Kaye,

W.E.

, Wagner,

, Tremlett,

, Buka,

S.L.

, Dilokthornsakul,

, Topol,

, Chen,

L.H.

, LaRocca,

N.G.

, US Multiple Sclerosis Prevalence Workgrou (2019) The prevalence of MS in the United States: A population-based estimate using health claims data. Neurology, 92(10), e1029–e1040.. https://doi.org/10.1212/WNL.0000000000007035.

35.

Wehman,

, Wilson,

, Parent,

, Sherron-Targett,

, McKinley,

(2000) Employment satisfaction of individuals with spinal cord injury. American Journal of Physical Medicine and Rehabilitation, 79(2), 161–169. https://doi.org/10.1097/00002060-200003000-00009

36.

WHO. (2001). International Classification of Functioning, Dis- ability, and Health. Geneva: World Health Organization

37.

Young,

A.E.

, Murphy,

G.C.

(2009) Employment status after spinal cord injury (1992-2005): A review with implications for interpretation, evaluation, further research, and clinical practice. International Journal of Rehabilitation Research. 32(1), 1–11. https://doi.org/10.1097/MRR.0b013e32831c8b19