Rehabilitation service patterns: a rural/urban comparison of success factors.
In 2000 the U.S Census Bureau reported that 226 million Americans (80%) lived in a metropolitan area with 55 million people (20%) living in rural areas (U.S. Census Bureau, n.d.). It is estimated that 23% of individuals in rural areas have a disability compared to 18% in metropolitan areas (RTC Rural, 1998). Rural America experiences higher unemployment, lower wages, higher rates of poverty, lower educational levels, limited public transportation, and reduced access to health care (Arnold et al., 1997; Davidson, 1995; Killian & Beaulieu, 1995; U.S. Bureau of Labor Statistics, n.d; U.S. Census Bureau, n.d; U.S. Congress, 1990).
Problems endemic in rural areas also effect service delivery in rural rehabilitation. First, rural rehabilitation consumers typically face higher rates of unemployment than their urban counterparts (Arnold & Seekins, 1998; Rojewski, 1992; RTC Rural, 1995). Second, lack of public transportation has been identified as a critical problem (Arnold & Seekins, 1998; Rojewski, 1992; RTC Rural, 1995). Inaccessible and unresponsive transportation is problematic both in the consumers' ability to access rehabilitation services and to travel to work sites (Rojewski, 1992; RTC Rural, 1995). Forty percent of rural residents live in counties with no public transportation (RTC Rural, 1999). Third, rural rehabilitation consumers have more limited access to rehabilitation services (Rojewski, 1992). For example, rural areas may have fewer educational options, and more limited access to vocational evaluation, job placement specialists, supported employment, and independent living services (Arnold & Seekins, 1998; Rojewski, 1992). Rural rehabilitation consumers may also have to travel long distances for vocational evaluation services or training options (Rojewski, 1992). Fourth, mental health services in rural areas are more limited with fewer than 10% of rural counties having a psychiatrist (RTC Rural, 2002). In addition rural residents are more likely to underutilize mental health services because of a lack of anonymity, stigma associated with treatment, and valuing independence and privacy (Badger, Robinson, & Farley, 1999). Fifth, while access to community-based hospitals is similar to urban residents, rural individuals have higher rates of health problems (McNeil, 1993; U.S. Congress, 1990). Finally, in a study conducted over thirty years ago, the greater geographic distance between counselor and consumer negatively impacts the consumer--counselor relationship because it is more difficult for consumers to travel to appointments and services and counselors to coordinate services (Bitter, 1972; Rojewski, 1992).
Research also provides evidence that there are strengths associated with provision of rehabilitation services in rural areas. Two strengths are noted. First, in a study conducted by Arnold and Seekins (1998), counselors with rural caseloads reported that local human service agencies coordinated services effectively. This was a significant strength only in rural areas. Second, Young, Murphy, and Strasser (2000) found that rehabilitation of individuals with spinal cord injury in rural communities were facilitated by a culture of support from both family and the wider community and an attitude that members of the community take care of each other.
While there are strengths associated with providing rehabilitation services in rural areas, research provides evidence that typically there are more problems associated with providing services in rural areas relative to urban caseloads (Arnold et al., 1997). This study investigated the effect of specific variables on vocational outcomes for rural and urban consumers. While previous research provides evidence that consumer demographic variables (Bolton et al., 2000) and the counselor--consumer relationship (Lustig et al., 2002) effect vocational outcomes of vocational rehabilitation consumers, no studies have investigated the differential impact of these variables in rural and urban consumer populations. Based on previous research, gender, marital status, severe disability, age, ethnicity, educational factors and working alliance were analyzed with respect to their impact on vocational outcomes (Bolton et al., 2000; Capella, 2002; Lustig et al., 2002; Wilson, 2000, 2002; Wilson, Harley & Alston, 2002). The following research question was addressed: What is the effect of demographic characteristics and the working alliance on employment outcomes for rural and urban consumers?
The participants for this study were 2031 Tennessee Division of Rehabilitation Services (TDRS) consumers who were contacted by telephone during fiscal year 2000--2001. Participants were classified as either rural or urban. Participants were also classified either employed full-time (status 26) or unemployed (status 28). Urban participants were individuals who lived in metropolitan statistical areas as defined by the U.S. Census Bureau (U.S. Census Bureau, 2002). Metropolitan statistical areas are geographic areas with one city with 50,000 or more persons or an urbanized area of at least 50,000 and a total metropolitan area of at least 100,000. Rural participants were individuals who lived in areas with less than the required number of persons to be considered a metropolitan statistical areas.
Tennessee Division of Rehabilitation Services participants ranged in age from 17 to 80 (M = 31.7; SD = 12.0), with 43% (n 876) between ages 18 and 24, 30% (n = 623) between ages 25 and 40, and 27% (n = 532) older than 40. Most participants were never married (62%; n = 1263) with 20% (n = 398) married, 18% (n = 370) divorced, separated, or widowed. Most respondents were Caucasian (83%; n = 1687) with 17% (n = 336) African-American, less than 1% (n = 4) American Indian/Alaskan Native, and less than 1% (n = 4) Asian and Pacific Islander. Participants could identify themselves as an individual of Hispanic origin (Cubans, Puerto Ricans, Mexicans, etc.) and also choose one of the racial categories. Forty--seven percent had completed less than a high school diploma (n = 963), while 38% (n = 774) had completed high school, 12% (n = 247) had completed post high school education, and 2% (n = 47) were in special education who received either a certificate of completion upon exiting or aged out of school. More than half (55%; n = 1116) of the respondents were male.
Participants were classified by the counselor with a primary disability and as having either a severe or non-severe disability. Severe disability was defined according to State-Federal rehabilitation regulations. Eighty-two percent (n = 1668) were classified with a severe disability. Participants were classified as individuals with chronic medical conditions (34%; n = 687), psychiatric disorders (23%; n = 470), mobility and orthopedic impairments (23%; n = 463), mental retardation (11%, n = 218), hearing or visual/impairments (7%; n = 151), and traumatic brain injuries (2%; n = 42). Most participants were employed full-time (n 1413; 70%). Table I provides data on urban and rural participants.
The Bureau of Business and Economic Research/Center for Manpower Studies (BBER/CMS) at The University of Memphis developed a 47-item questionnaire regarding consumers' satisfaction with TDRS programs and services, current employment status, and wages and benefits. (1) Employed consumers completed the 47-item questionnaire, while unemployed consumers completed the same questionnaire without questions about benefits or satisfaction with current employment. In addition to demographic questions, the 47-item questionnaire is divided into three sections. The first section, 26 items, assessed consumer satisfaction with services. An example of a question is, Did your counselor try to understand your problems and needs? Participants responded most of the time, some of the time, hardly ever, not sure, does not apply, or no response. The second section, 20 items, assessed employment status, pay, and hours. The third section, used only with employed participants, listed potential fringe benefits and asked the consumer "Which of these benefits does your employer provide?". The BBER/CMS survey did not collect data on the number of employees for each employers.
For purposes of this study, specific questions contained in the BBER/CMS questionnaire were used to measure the construct of working alliance. The nine item instrument, named the Working Alliance Survey (WAS), was developed by Lustig et al., (2002) in a previous study. Working alliance was defined as a collaboration between the consumer and the counselor based on the development of an attachment bond as well as a shared commitment to the goals and tasks of counseling (Bordin, 1979). The WAS produces a standardized score with a negative score indicating a stronger working alliance. In Lustig et al., (2002) the internal consistency reliability coefficient (Cronbach's alpha) was .80. In this study the internal consistency reliability coefficient (Cronbach's alpha) was .78
Each month the Tennessee Division of Rehabilitation Services provided the Bureau of Business and Economic Research/Center for Manpower Studies (BBER/CMS) at The University of Memphis with a list of consumers. Staff at the BBER/CMS contacted consumers by telephone 60 days after closure and administered the questionnaire by phone. If the initial attempt to contact the consumer was unsuccessful, six additional attempts were made to contact the consumer. The BBER/CMS attempted to contact 10,407lients. Of this number, 41% (n = 4284) were contacted and completed the questionnaire. The BBER/CMS was unable to contact 51% (n = 5315) while 8% (n = 808) were contacted but refused to respond. Of the 4284 participants who completed the questionnaire approximately 53% of the questionnaires were unusable because of missing data, frequency of items marked "not sure", "does not apply", and no response answers, and out of range values. A final sample of 2031 participants were used for analysis.
A sequential logistic regression analysis was performed to assess the degree of relationship between employment status (full-time or unemployed) and the remaining variables. Logistic regression was selected due to the dichotomous outcome variable and mixed nature of the levels of measurement on predictors. The choice of a sequential analysis was made in order to ascertain the unique contribution of WAI score beyond that of the demographic variables. An alpha of .01 was adopted as a minimum level of significance for all statistical tests.
A sequential logistic regression analysis was performed on employment status (full-time or unemployed) as the outcome first on the basis of six demographic predictors and then after addition of level of working alliance. The six demographic predictors were gender, marital status, severe/non-severe disability, age, ethnicity, and educational level. After deletion of 80 cases with out of range values, data from 1089 participants were available for analysis with 345 unemployed and 744 employed full-time. Missing and out of range values appeared to be randomly scattered across categories of outcome and predictors. Examination of the correlation matrix suggested that multicollinearity did not appear to be an issue, neither were there any influential outliers in the data set as indicated by a maximum Cook's d of. 10. There was, however, one outlier in the solution as indicated by a standardized residual of -3.26.
There was a good fit (discrimination among groups) on the basis of the six demographic predictors alone, [chi square] (6) = 54.50, p < .001, indicated that the predictors, as a set, reliably distinguished between participants who were employed full-time and those who were unemployed. After addition of the level of working alliance, [chi square] (7) = 91.60, p < .001. Comparison of the log-log-likelihood ratios for models with and without level of working alliance showed reliable improvement with the addition of level of working alliance, [chi square] (1) = 37.10, p < .001.
Prediction rates for consumers in full-time employment were impressive, with 93.9% correctly predicted. Prediction rates for unemployed consumers, however, were small with only 19.7% predicted correctly. Overall, prediction rates were 69.7%.
Table 2 shows the regression coefficients, Wald statistics, significance levels, and 99% confidence intervals for the exponentiated coefficients for the full model. The Wald statistic evaluates the statistical significance of individual independent variables. According to the Wald statistic severe disability, age, educational level, and level of working alliance significantly predicted employment status. Participants who were employed full-time were less likely to have a severe disability, were generally younger, had more education, and had a higher level of working alliance than participants who were unemployed.
Another sequential logistic regression analysis was performed on employment status (full-time or unemployed) as the outcome first on the basis of six demographic predictors and then after addition of level of working alliance. After deletion of 46 cases with out of range values, data from 942 participants were available for analysis with 273 unemployed and 669 employed full-time. Missing and out of range values appeared to be randomly scattered across categories of outcome and predictors. Examination of the correlation matrix suggested that multicollinearity did not appear to be an issue, neither were there any influential outliers in the data set as indicated by a maximum Cook's d of. 15. There were, however, five outliers in the solution as indicated by a standardized residual reaching -4.647.
There was a good fit (discrimination among groups) on the basis of the six demographic predictors alone, [chi square] (6) = 94.31, p < .001, indicating that the predictors, as a set, reliably distinguished between participants who were employed full-time and those who were unemployed. After addition of the level of working alliance, [chi square] (7) = 141.464, p < .001. Comparison of the log-log-like-lihood ratios for models with and without level of working alliance showed reliable improvement with the addition of level of working alliance, [chi square] (1) = 47.154, p < .001.
Prediction rates for consumers employed full-time were notable, with 91.5% correctly predicted. Prediction rates for unemployed consumers, however, were unimpressive with 29.3% predicted correctly. Overall, prediction rates were 73.5%.
Table 3 shows the regression coefficients, Wald statistics, significance levels, and 99% confidence intervals for the exponentiated coefficients for the full model. According to the Wald criterion, a test for statistical significance of individual coefficients, severe disability, age, and level of working alliance reliably predicted employment status. Participants who were employed full-time were less likely to have a severe disability, were generally younger, and had a higher level of working alliance than participants who were unemployed.
Metropolitan versus rural consumer
Comparison of the log-log-likelihood ratios for models for rural and urban participants revealed a significant difference between the fit for the two models, [chi square] (1) = 49.864, p < .001, with overall correct predictions increasing by 4% for rural participants. Therefore, employment status of rural participants may be more accurately predicted from the set of demographic variables and level of working alliance. The only difference in the significance of predictors was educational level. Educational level contributed significantly to predicting employment status for urban participants, however, it did not contribute significantly for rural participants.
For urban participants, the exponentiated coefficients, or odds ratio, indicated that if all other variables are held constant that participants with a severe disability were 0.40 times less likely to be employed full-time than participants with a severe disability. Specifically for a sample of 1089, 311 persons with a severe disability are expected to be employed full time and 778 are not expected to be employed full-time. For each year that a participant is younger their odds of being employed full-time are .966 as large, for each gain in educational level the odds of full-time employment increased by 1.266, and for each additional point on the WAI a participant's odds of being employed full-time increased by .82. Two examples assist in understanding the odds ratio. First, choosing the more typical cases for the nonsignificant variables and the lower values for the significant variables, the following was predicted. For Caucasian males who were never married, have a severe disability, are age 17, have less than a high school education, and have a low level of WIA, the estimated probability of full-time employment for such a client is 42%. Second, for clients described as Caucasian males who were never married, do not have a severe disability, are age 66, have a college degree, and have a high level of WIA the estimated probability of full-time employment for such a client is 80%.
Similarly for rural participants, the exponentiated coefficients indicate that if all other variables are held constant that participants without a severe disability were 1.479 times more likely to be employed full-time than participants with a severe disability, for each year that a consumer is younger their odds of being employed full-time increased by .954, and for each additional point on the WAI a consumer's odds of being employed full-time increased by .772. Two examples assist in understanding the odds ratio. First, selecting the more typical cases for the nonsignificant variables and the lower values for the significant variables, the following was predicted. For Caucasian males without a high school degree who were never married, have a severe disability, are age 17, and have a low level of WIA, the estimated probability of full-time employment for such a client is 43%. Second, for clients described as Caucasian males without a high school degree who were never married, have a severe disability, are age 66, and have a high level of WIA the estimated probability of full-time employment for such a client is 69%.
Particular demographic variables and working alliance were significant predictors for full-time employment for both urban and rural consumers. As the above probability examples indicate, younger consumers with non-severe disabilities who had developed a better working alliance with their counselor were more likely to be employed. Conversely, consumers who were older, had a severe disability, and had a weaker working alliance with their counselor were at a disadvantage in terms of employment outcomes.
The importance of the working alliance in promoting positive employment outcomes is noteworthy. Working alliance refers to the counselor--consumer relationship characterized by mutually agreed upon goals, agreement that the counseling tasks are relevant and effective, and an emotional bond exemplified by trust and common purpose (Bordin, 1979, 1994). An effective working alliance is fostered by the development of a collaborative relationship between counselor and consumer and the counselor expressing respect and interest in the consumer (Safran & Muran, 1998).
There were two interesting differences between urban and rural consumers. First, rural consumers with a non-severe disability were considerably more likely to be employed than urban consumers with a non-severe disability. Thus while it mattered that the consumer did not have a severe disability for all consumers, it gave a bigger employment advantage to rural consumers. Conversely, rural consumers with a severe disability were at a greater employment disadvantage than their urban counterparts. Second, educational level contributed significantly to predicting employment status for urban participants but not for rural participants.
Educational level was a significant predictor of employment for urban consumers but not rural consumers, a somewhat surprising finding. Three factors may explain this result. First, research indicates that the gap between the number of jobs requiring a high school education and the number of workers with a high school education is greater for urban workers than rural workers (Killian & Beaulieu, 1995). The gap between the education required of jobs and the educational attainment of workers is 10% for urban workers and 6% for rural workers (Killian & Beaulieu, 1995). Second, 12.4% of workers in urban areas are under-educated for their jobs. Stated another way, 12.4% of workers in urban areas possess educational attainment less than required for the jobs they hold. Fewer rural workers possess an educational deficit with 10.4% possessing educational attainment less than required for their job (Killian & Beaulieu, 1995). Third, the percentage of jobs requiring more than a high school education is 41% in urban areas and 29% in rural areas suggesting that a lack of appropriate education at higher level jobs is a larger issue in urban areas and consequently produces a larger impact on the consumer's employability. This data provides an explanation for the finding that the educational level of consumers is a more salient issue in urban areas than rural areas.
Rural consumers with a severe disability have a more difficult time securing employment than urban consumers. Rural consumers have (a) higher rates of unemployment, (b) more limited access to vocational and mental health services, (c) more limited access to transportation, (d) higher rates of health problems, and (e) greater geographic distances between consumer and counselor (Arnold & Seekins, 1998; Bitter, 1972; McNeil, 1993; Rojewski, 1992; RTC Rural, 1995, 1999, 2002; U.S. Congress, 1990). Each of these problems are likely to differentially effect more severely disabled consumers who typically need more intensive services.
Limitations and future research
The results are limited by the following considerations. First, the ex post facto design does not provide a basis for a causal link between variables. Second, since interviews were completed during the 2000-2001 fiscal year with Tennessee Division of Rehabilitation Services clients, the interpretation of the results should be limited to the sample examined at the time of the study. Third, the response rate was low and it is unclear whether nonrespondents and respondents differed significantly.
There are number of areas for future research. The finding that rural consumers with a severe disability were at a greater employment disadvantage than urban consumers is interesting. At least two explanations could be investigated. First, there may be service delivery issues specific to rural areas or second, community/employer attitudes are more problematic for individuals with severe disabilities in rural areas relative to urban areas. While there have been studies investigating the effect of employer attitudes on hiring, no study has investigated rural/urban differences (Bordieri & Drehmer, 1988; Bricout & Bentley, 2000; Millington, Szymanski, & Hanley-Maxwell, 1994). The finding that educational levels impacted urban workers but not rural workers is also an area for further study. The positive impact of the working alliance between counselor and client has been investigated previously (Lustig et al., 2002) but the effect of the working alliance in rural areas is an area for further investigation. Finally, a qualitative study interviewing both rural counselors and consumers may provide insight into the nature of the vocational rehabilitation process in rural areas. A qualitative study could identify new issues related to rural employment or assist in understanding the interaction of previously investigated problems.
The results of this study suggest that rehabilitation counselors with rural caseloads should give particular attention to the needs of consumers with severe disabilities. Consumers in rural areas with severe disabilities fare worse than those without a severe disability. These consumers may need more intensive services. In addition counselors should consider focusing on the development of an effective working alliance with the consumer. These efforts will increase the likelihood that the consumer will find employment.
Thirty years ago, Bitter (1972), in a review of rural rehabilitation services, concluded that the vocational rehabilitation services as practiced in urban areas was inappropriate for rural areas because of "inadequate or nonexistent" (p. 354) resources. Rojewski (1992) echoed these thoughts stating that rural consumers have difficulties accessing important services. Today rural consumers face similar problems with higher unemployment, limited access to vocational, mental health, and transportation services, and more health problems (Arnold & Seekins, 1998; Rojewski, 1992; RTC Rural, 1995; RTC Rural, 1999; RTC Rural, n.d.). Addressing the needs of rural consumers with severe disabilities and a weak working alliance with their counselor will assist in ameliorating the problems noted by Bitter.
Table 1 Demographic characteristics of rural and urban participants Total Rural Urban (N = 2031) (n = 942) (n = 1089) Age (M = 31.7; (M = 30.5; (M= 32.7; SD = 12.0) SD = 12.0) SD = 11.8) 17-24 43% 30% 27% 25-40 50% 26% 24% 41+ 37% 34% 29% Male 55% 55% 55% Marital status Never married 62% 63% 61% Married 20% 21% 19% Divorce/widowed/separated 18% 16% 20% Ethnicity Caucasian 83% 90% 77% African-American 17% 10% 23% American Indian < 1% < 1% < 1% Asian/Pacific Islander < 1% 0% 0% Education < High school 47% 38% 39% High school diploma 38% 33% 42% High school diploma + 12% 7% 16% Special education 2% 2% 3% Disability chronic medical condition 34% 36% 32% mobility/orthopedic 23% 24% 22% psychiatric 23% 19% 27% mental retardation 11% 13% 9% hearing/vision 7% 6% 8% traumatic brain injury 2% 2% 2% Severe disability 82% 80% 84% Employment status Full-time 70% 71% 68% Unemployed 30% 29% 32% Table 2 Logistic regression for urban participants Variables B Wald Sign. Odds 99% CI for Ratio Expon (B) Lower Upper Gender -0.009 0.004 .949 0.991 0.695 1.413 Marital Status -0.105 3.689 .055 0.901 0.783 1.036 Severe Disability -0.919 17.930 .000 0.399 0.228 0.698 Age -0.034 22.816 .000 0.966 0.949 0.984 Ethnicity -0.134 0.697 .404 0.874 0.578 1.323 Educ. Level 0.236 9.919 .002 1.266 1.044 1.536 WAI -0.198 36.479 .000 0.820 0.754 0.893 Constant 2.568 25.524 .000 13.037 Table 3 Logistic regression for rural participants Variables B Wald Sign. Odds 99% CI for Ratio Expon (B) Lower Upper Gender -0.098 0.373 .541 0.907 0.600 1.370 Marital Status -0.055 0.761 .383 0.947 0.805 1.113 Severe Disability -1.479 33.211 .000 0.228 0.118 0.441 Age -0.048 33.385 .000 0.954 0.934 0.974 Ethnicity 0.064 0.060 .807 1.066 0.545 2.083 Educ. Level 0.159 3.188 .074 1.172 0.932 1.475 WAI -0.259 45.635 .000 0.772 0.699 8.52 Constant 3.439 31.751 .000 31.150
The Journal's previous Editor, Paul Alston, and his Editorial Review Board, reviewed this article.
(1) Full instrument is available by contacting the authors.
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Daniel C. Lustig
The University of Memphis
Gail H. Weems
The University of Memphis
David R. Strauser
The University of Memphis
Daniel C. Lustig, Department of Counseling; Educational Psychology and Research; The University of Memphis, 113 Patterson Hall, Memphis TN 38111-9890. E-mail firstname.lastname@example.org.
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|Author:||Weems, Gail H.|
|Publication:||The Journal of Rehabilitation|
|Date:||Jul 1, 2004|
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