Logistic Regression In Nursing Practice

Logistic Regression In Nursing Practice

Logistic Regression in Nursing Practice
Logistic regression is used to analyze a wide variety of variables that may surround a singular outcome. For example, logistic regression could be used to identify the likelihood of a patient having a heart attack or stroke based on a variety of factors including age, sex, genetic characteristics, weight, and any preexisting health conditions. The biological systems and issues with which the health care field is concerned represent the kinds of applications for which logistic regression is especially useful.
Logistic regression is used in the health care field for many purposes, including diagnoses, predictions, and forecasting. The three articles in this week’s Learning Resources illustrate the many uses of logistic regression in the health care field. This Discussion allows you to explore the different uses of logistic regression and cultivate a deeper understanding of the application of logistic regression in evidence-based practice.
To prepare:

  • Review      the three articles in this week’s Learning Resources and evaluate their      use of logistic regression. Select one article that interests you to      examine more closely in this Discussion
  • Critically      analyze the article you selected considering the following questions:

 

  1. What       are the goals and purposes of the research study the article describes?
  2. How       is logistic regression used in the study? What are the results of its       use?
  3. What       other quantitative and statistical methods could be used to address the       research issue discussed in the article?
  4. What       are the strengths and weaknesses of the study?
  5. How       could the weaknesses of the study be remedied?
  6. How       could findings from this study contribute to evidence-based practice, the       nursing profession, or society?

By Thursday 10/12/17, 5 pm, write a minimum of 550 words essay in APA format. Use at least 3 references from the list of required reading below.  Include the level one headings as numbered below.
Post a cohesive response that addresses the following:
1) In the first line of your posting, identify the article you examined, providing its correct APA citation. (See attached PDF file for the article).
2) Post your critical analysis of the article as outlined above (make sure to answer all the points asked above in the “To Prepare” area, bullets [1, 2, 3]).
3) Propose potential remedies to address the weaknesses of each study (bullets 4 and 5 in the “To Prepared” area).
4) Analyze the importance of this study to evidence-based practice, the nursing profession, or society (bullet 6 in the “To Prepared” area).
Learning Resources
.
Required Media
Multiple Regression
Used by permission from SPSS VideoTutor.com A division of Consumer Raters LLC., 1121 S Military Trail, 314, Deerfield Beach, FL 33442, USA
Note: The approximate length of this media piece is 5 minutes.
“Logistic Regression
Used by permission from SPSSVideoTutor.com A division of Consumer Raters LLC., 1121 S Military Trail, 314, Deerfield Beach, FL 33442, USA 
Note: The approximate length of this media piece is 15 minutes.
Required Readings
Gray, J.R., Grove, S.K., & Sutherland, S. (2017). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.). St. Louis, MO: Saunders Elsevier.
Chapter 24, “Using Statistics to Predict”
This chapter asserts that predictive analyses are based on probability theory instead of decision theory. It also analyzes how variation plays a critical role in simple linear regression and multiple regression.
Statistics and Data Analysis for Nursing Research
Chapter 9, “Correlation and Simple Regression” (pp. 208–222)
This section of Chapter 9 discusses the simple regression equation and outlines major components of regression, including errors of prediction, residuals, OLS regression, and ordinary least-square regression.
Chapter 10, “Multiple Regression”
Chapter 10 focuses on multiple regression as a statistical procedure and explains multivariate statistics and their relationship to multiple regression concepts, equations, and tests.
Chapter 12, “Logistic Regression”
This chapter provides an overview of logistic regression, which is a form of statistical analysis frequently used in nursing research.
Hoerster, K. D., Mayer, J. A., Gabbard, S., Kronick, R. G., Roesch, S. C., Malcarne, V. L., & Zuniga, M. L. (2011). Impact of individual-, environmental-, and policy-level factors on health care utilization among US farmworkers. American Journal of Public Health, 101(4), 685–692. doi:10.2105/AJPH.2009.190892
This article discusses the results of a study of how many U.S. farmworkers accessed U.S. health care. The study considered this question on several levels—individual, environmental, and policy—and used logistic regression to analyze the multivariate data gathered.
Tritica-Majnaric, L., Zekic-Susac, M., Sarlija, N., & Vitale, B. (2010). Prediction of influenza vaccination outcome by neural networks and logistic regression. Journal of Biomedical Informatics, 43(5), 774–781. doi:10.1016/j.jbi.2010.04.011.
This article describes the methods and results of a neural network study on the effectiveness of the influenza vaccine using historical data in three neural network algorithms. The article also provides a discussion of logistic regression in comparison to the neural network algorithms used.
Xiao, Y., Griffin, M. P., Lake, D. E., & Moorman, J. R. (2010). Nearest-neighbor and logistic regression analyses of clinical and heart rate characteristics in the early diagnosis of neonatal sepsis. Medical Decision Making, 30(2), 258–266. doi:10.1177/0272989X09337791
This article outlines the procedures and findings of a study on the use of two methods of neonatal sepsis diagnosis: nearest-neighbor analysis and logistic regression analysis. The results indicated that each method generates unique information useful to diagnosis, and therefore both methods should be used simultaneously for improved accuracy of diagnoses.
Optional Resources
Walden University. (n.d.). Linear regression. Retrieved August 1, 2011, from http://streaming.waldenu.edu/hdp/researchtutorials/educ8106_player/educ8106_linear_regression.html

Journal of Vocational Rehabilitation 45 (2016) 63–72 DOI:10.3233/JVR-160811 IOS Press
63
Characteristics of people with disabilities receiving assistive technology services in vocational rehabilitation: A logistic regression analysis
I-Chun Huanga,∗, Gladys Cheingb, Philip Rumrillc, Kevin Bengtsond, Fong Chand, Jana Telzlaffd
and Mikael Snitkere aNational Changhua University of Education, Changhua City, Taiwan bThe Hong Kong Polytechnic University, Hung Hom, Hong Kong cKent State University, Kent, OH, USA dUniversity of Wisconsin-Madison, Madison, WI, USA eWilliam S. Middleton Memorial Veterans Hospital, Madison, WI, USA
Revised/Accepted December 2015
Abstract. BACKGROUND: The provision of assistive technology (AT) services could help people with disabilities overcome social and environmental barriers in the workplace to facilitate employment outcomes. However, little is known about the types of con- sumers who receive assistive technology services and who can most benefit from the services in vocational rehabilitation (VR). OBJECTIVE: This study investigated the characteristics of consumers receiving AT services in state VR agencies and identified complementary VR services associated with the provision of AT services. METHODS: A 10% random sample of VR consumers (N = 32,088) whose cases were closed in fiscal year 2009 (FY 2009) were extracted from the Rehabilitation Services Administration (RSA) database for a secondary data analysis multivariate logistic regression. RESULTS: Only 10.4% of VR consumers closed in FY 2009 received AT services. The majority of AT recipients reported sen- sory impairments (60.4%) and physical impairments (27.6%). Consumers older than 65 years of age (odds ratio [OR] = 1.43; 95% confidence intervals [CI]: 1.18–1.74), with associate’s degrees (OR = 1.27; 95% CI: 1.15–1.41) and bachelor’s degrees or higher (OR = 1.77; 95% CI: 1.55–2.01), reporting sensory impairments (OR = 3.78; 95% CI: 3.39–4.21), receiving cash benefits (OR = 1.44; 95% CI: 1.29–1.60) and being employed at the time of application (OR = 1.79; 95% CI: 1.62–1.98) were more likely to receive AT services. Compared to European Americans, African American (OR = 0.77; 95% CI: 0.69–0.87) and Hispanic Americans (OR = 0.84; 95% CI: 0.73–0.98) were less likely to receive AT services. Moreover, AT recipients were more likely to also obtain comprehensive assessment (OR = 1.49; 95% CI: 1.38 to 1.66), college or university training (OR = 1.56; 95% CI: 1.38 to 1.76), occupational or vocational training (OR = 1.20; 95% CI: 1.05 to 1.37), augmentative skills training (OR = 2.48; 95% CI: 2.09 to 2.94), and miscellaneous training (OR = 1.47; 95% CI: 1.30 to 1.66); but less likely to obtain job readiness training (OR = 0.75; 95% CI: 0.64 to 0.87) and job search assistance (OR = 0.87; 95% CI: 0.76 to 0.99).
∗Address for correspondence: Dr. I-Chun Huang, Graduate Institute of Rehabilitation Counseling, National Changhua
University of Education, 1 Jin-De Road, Changhua City 500, Tai- wan. E-mail: ichunhuang@cc.ncue.edu.tw.
1052-2263/16/$35.00 © 2016 – IOS Press and the authors. All rights reserved
 

 
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CONCLUSION: The results provide insights into AT recipients in the state VR system. VR professionals and practitioners need to be aware of AT devices and job accommodation services as resources for people with disabilities to increase employability. Further consideration should be given to developing a systematic understanding of the provision of AT services in the VR system and evaluating its effectiveness.
Keywords: Assistive technology, people with disabilities, vocational rehabilitation
1. Introduction
Participation in competitive employment and other meaningful work activities is fundamental to the physical and psychological well-being of people with and without disabilities (Chan et al., 1997; Dutta, Gervey, Chan, Chou, & Ditchman, 2008). Compared to persons who are employed, those who are unem- ployed tend to experience a higher prevalence of depression, use alcohol more frequently, and report lower levels of self-esteem and quality of life (Dutta et al., 2008). Rehabilitation researchers and prac- titioners have consistently recognized the need to consider contextual personal and environmental factors in the development of efficacious voca- tional rehabilitation interventions. The World Health Organization (WHO) International Classification of Functioning, Disability, and Health (ICF) model has gained wide acceptance among rehabilitation and health professionals as a framework for understand- ing chronic illness and disability across cultures (Chan, Tarvydas, Blalock, Strauser, & Atkins, 2009). Specifically, the ICF paradigm is structured around the following components: (a) body functions and structure, (b) activities (related to tasks and actions by an individual), (c) participation (involvement in a life situation), and (d) individual characteristics and environmental factors. Functioning and disability, as well as personal factors, are viewed as a complex interaction between the health condition of the indi- vidual and the contextual factors of the environment (Chan, Sasson, Ditchman, Kim, & Chiu, 2009).
A major emphasis of the ICF model is the effect of environmental factors on community participation of people with disabilities in all aspects of life, including employment and independent living. Therefore, dis- ability can be conceived as a gap between individuals’ capacities (physical, cognitive, and sensory ability) and their performance in daily activities as well as their capacities and participation in social and work life. The ability of individuals to translate their intrin- sic capacity into successful performance is affected by the contexts in which they perform these activities. The requirements for performing an activity depend
on the specific task (e.g., going to work). In addition, each of these activity demands has an activity-specific environmental context, which can pose barriers for accomplishing that task and can feature dimensions of the environment that facilitate conduction of the specific activity. The extent to which individuals can translate capacity into performance also depends on what they can do to change the environment or to change the demands of the activity by accommoda- tion and compensation, including the use of human help (both formal and informal care). Changes in the way tasks are performed (including the use of technology) and changes that are made to the phys- ical and social environment can facilitate individual performance (Chan et al., 2009).
Environmental factors are defined as external fea- tures of the physical, social, and attitudinal world that can have an impact on an individual’s perfor- mance in each component of the ICF model. These environmental factors comprise both the immediate and background environments and can serve as either facilitators or barriers to promote full inclusion of people with chronic illness or disability. For example, a performance problem in mobility would occur only in an inaccessible building for a person with walk- ing difficulties, not in a building that is accessible to him or her. Specific environmental factors include (a) products and technology; (b) natural environment and human-made changes to the environment; (c) sup- port and relationships; (d) attitudes; and (e) services, systems and policies (Chan et al., 2009).
One of the major factors in the ICF environmental construct includes products and technology. Specif- ically, universal designs, job accommodations, and assistive technology can play a significant role in enhancing a person’s ability to interact with his or her environment and to meaningfully participate in society. This extends to and impacts the ability of peo- ple with disabilities to fulfil meaningful social roles such as being a worker, a volunteer, a homemaker, a spouse or partner, a friend, a parent or grandparent, a citizen, and/or a neighbour. Being an active and pro- ductive member of society who is well integrated into family and the community may contribute to a better
 
 
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quality of life. Although the positive impact that assis- tive technology can have on community participation and quality of life for people with disabilities is well documented (Job Accommodation Network, 2014; Scherer, 2012), LaPlante et al. (1992) reported that only 25% of Americans with disabilities were using some form of assistive technology. Even though peo- ple with disabilities have information on assistive technology devices and their potential benefits, many lack information about the resources available for obtaining assistive technology devices and services (Carlson, Erhlich, Berland, & Bailey, 2001; Rubin, Roessler, & Rumrill, 2016).
A review of studies addressing the factors that impact the use of assistive technology as part of the environmental component of the ICF was conducted by Scherer and Glueckauf (2005). They underscored the importance of providing a comprehensive assess- ment to maximize the opportunities and benefits of utilizing assistive technology. This assessment can be achieved through a comprehensive understanding of the individual’s predispositions, based on vari- ous perceptions of assistive technology. Providing tailored accommodations is necessary for improv- ing individuals with disabilities’ participation in education, employment, and community activities (Rumrill, Fraser, & Johnson, 2013).
Assistive technology has the potential to equalize the capacities of persons with (and without) disabili- ties to improve societal integration and employment outcomes (Merbitz, Lam, Chan, & Thomas, 1999). In the United States, state vocational rehabilitation agencies play an instrumental role in providing reha- bilitation services that can improve the employment rates and quality of employment for people with disabilities. The Rehabilitation Act that authorizes funding for state vocational rehabilitation (VR) ser- vices recognizes that assistive technology has the potential to transform lives. In a recent study, Dutta et al. (2008) analyzed the Rehabilitation Services Administration case services report (RSA-911) data using logistic regression analysis. Assistive technol- ogy services were found to contribute significantly to employment outcomes for VR consumers with sen- sory impairments (OR = 1.97, 95% CI [1.67, 2.33]) and physical impairments (OR = 1.41, 95% CI [1.13, 1.75]).
In spite of the proven benefits of assistive tech- nology, roughly 30% of assistive technology devices are discarded within a year (Scherer & Glueckauf, 2005). State VR agencies are now required to spec- ify how assistive technology devices and services
are to be provided for consumers. However, with only 10% of VR consumers receiving assistive technol- ogy services, these services are vastly underutilized (Dutta et al., 2008). State VR counselors need more information regarding the types of clients who can most benefit from receiving assistive technology ser- vices. Individuals’ predisposition to use assistive technology depends on a variety of factors includ- ing preferences, abilities, personal needs, exposure to technologies, and past experiences. Additional fac- tors related to use of assistive technology may include people’s subjective well-being, personality, percep- tions of their physical capabilities, environmental support, and expectations for future functioning (Scherer, 2005). The purpose of this study was to examine characteristics of people with disabilities who are most likely to receive assistive technology services from state VR agencies and to identify com- plementary VR services that are closely aligned with receiving assistive technology services.
2. Method
2.1. Participants
Data for this study were extracted from the Rehabilitation Services Administration Case Service Report (RSA-911) database. Study participants com- prised a 10% random sample (N = 32,088) of VR consumers whose cases were closed in fiscal year 2009. The sample included 17,515 men (54.6%) and 14,573 women (45.4%). The mean age of the participants was 35.70 years (SD = 15.01). Types of disabilities reported by participants included physical impairments (25.4%), sensory impairments (16.5%), cognitive impairments (28.6%), and mental illness (29.5%). Racial and ethnic backgrounds of these participants were diverse; 64.5% were European Americans, 23.1% were African Americans, 9.2% were Hispanic Americans, 1.8% were Asian Ameri- cans, and 1.5% were Native Americans. In terms of educational attainment, approximately 7% of partic- ipants received special education, 28% had less than high school education, 34% completed high school, 21% had associate’s degrees, and 7% held bachelor’s degrees or higher. The majority (77.5%) of partici- pants were not employed at the time of application for VR services and nearly 39% qualified for medi- cal insurance or cash benefits through Social Security disability programs (i.e., Social Security Disabil- ity Insurance [SSDI], Supplemental Security Income
 
 
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Table 1 Characteristics of the sample (N = 32,088)
Characteristics n (%) M (SD)
Age (16–95 years) 35.68 (15.01) 16–34 years 15,806 (49.3%) 35–54 years 12,655 (39.4%) 55–64 years 2,817 (8.8%) 65 years and older 810 (2.5%) Gender Men 17,515 (54.6%) Women 14,573 (45.4%) Disability type Physical impairments 8,153 (25.4%) Sensory impairments 5,283 (16.5%) Cognitive impairments 9,175 (28.6%) Mental illness 9,477 (29.5%) Race European American 20,704 (64.5%) African American 7,397 (23.1%) Hispanic American 2,958 (9.2%) Asian American 563 (1.8%) Native American 466 (1.5%) Education Special education 2,482 (7.7%) Less than high school 9,043 (28.2%) High school 11,162 (34.8%) Post-secondary/associate 6,958 (21.7%) Bachelor degree or higher 2,443 (7.6%) Medical Benefits Yes 12,382 (38.6%) No 19,706 (61.4%) Cash Benefits Yes 12,807 (39.9%) No 19,281 (60.1%) Employed at application Yes 7,206 (22.5%) No 24,882 (77.5%)
[SSI]). Table 1 summarizes the demographic charac- teristics of the sample.
2.2. Outcome variable
The receipt of assistive technology services was adopted as the outcome variable for the study. According to the Rehabilitation Services Administra- tion (2009), the provision of rehabilitation technology services, also known as assistive technology services, represents a systematic application of technologies, engineering methodologies, or scientific principles to meet the needs of, and address the barriers con- fronted by, individuals with disabilities in areas that include education, rehabilitation, employment, transportation, independent living, and recreation. Assistive technology devices are defined by Public Law 105–394 as any item, piece of equipment or product system, whether acquired commercially off the shelf, modified, or customized, that is used to
increase, maintain or improve functional capabilities of individuals with disabilities. Assistive technol- ogy services are defined as any service that directly assists individuals with disabilities in the selection, acquisition, or use of an assistive technology device. Although Rehabilitation Services Administration and Public Law definitions vary slightly, for the purpose of this study, the terms assistive technology and reha- bilitation technology are used interchangeably.
2.3. Predictor variables
Predictors for this investigation included two sets of variables, demographics and VR services. Demo- graphic variables included gender (men or women), age (16–34, 35–54, 55–64, 65 and older), race (European American, African American, Hispanic American, Asian American, or Native American), education (special education, less than high school education, high school graduate, postsecondary edu- cation, or at least a bachelor’s degree), disability type (physical impairments, sensory impairments, cognitive impairments, or mental illness), work dis- incentives, and employment status at application.
VR service variables included assessment, diag- nostics and treatment of impairments, college or university training, occupational/vocational training, on-the-job training, basic academic remedial or lit- eracy training, job readiness training, augmentative skills training, miscellaneous training, job search assistance, and job placement assistance. A summary of the participants’ usage of each service variable is presented in Table 2.
2.4. Data analysis
Hierarchical logistic regression analysis was used to examine the relationships between demographic characteristics and VR services and the outcome vari- able of receipt of AT services. The odds ratios (ORs) were presented at the 95% confidence interval (CI). The Predictive Analytics SoftWare (PASW 18.0) sta- tistical software package was used to conduct the logistic regression analysis.
3. Results
3.1. Descriptive statistics
For the overall sample, the rate of receipt of AT or rehabilitation technology services was 10.4%. Of
 
 
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Table 2 Percentage of usage of VR services by the participants
(N = 32,088)
Vocational rehabilitation services Used Not Used
Assessment 20740 (64.6%) 11348 (35.4%) Diagnostics & Treatment 13299 (41.4%) 18789 (58.6%) Counseling & Guidance 20962 (65.3%) 11126 (34.7%) College or University Training 4579 (14.3%) 27509 (85.7%) Occupational/Vocational Training 3953 (12.3%) 28135 (87.7%) On-the-job Training 938 (2.9%) 31150 (97.1%) Remedial Training 440 (1.4%) 31648 (98.6%) Job Readiness Training 4625 (14.4%) 27463 (85.6%) Augmentative Skills Training 943 (2.9%) 31145 (97.1%) Miscellaneous Training 3884 (12.1%) 28204 (87.9%) Job Search Assistance 8302 (25.9%) 23786 (74.1%) Job Placement Assistance 11361 (35.4%) 20727 (64.6%)
the 32,088 participants, 3,329 consumers were pro- vided rehabilitation technology services and 28,759 were not. Of these 3,329 individuals receiving AT services, 50.5% were men and 49.5% were women. Approximately 74% were European Americans, 15% were African Americans, 8% were Native Americans, 2% were Asian Americans, and 1% were Hispanic Americans.
The majority of AT recipients reported sensory impairments (60.4%). More than half did not receive medical insurance (62%) or cash benefits (57%) and were not employed (53.5%) at application. However, around 74% of AT recipients also obtained assess- ment services and 70% also received counseling and guidance services. Table 3 summarizes the demo- graphic characteristics of the consumers receiving AT services whose VR cases were closed in FY2009.
3.2. Logistic regression results
To identify significant predictors of the receipt of AT services for consumers in the state VR system, a hierarchical logistic regression analysis was com- puted. Overall, the omnibus test for the model was found to be statistically significant, χ2 (39, N = 3256) = 6034.27 (p < 0.0001). The Nagelkerke R2 was com- puted to be 0.35, a relatively large effect. In the first step, demographic variables were entered into the model to determine whether demographic covari- ates could predict which consumers were more likely to have received AT services. Demographic predic- tors included gender (with women as the reference category), age (with the 16–34 year group as the reference category), education (with high school as the reference category), disability type (with sensory impairments as the reference category), race (with European American as the reference category), cash
Table 3 Characteristics of VR clients receiving AT services (N = 3,329)
Characteristics n (%)
Gender Men 1681 (50.5%) Women 1648 (49.5%) Age (16–92 years) 16–34 years 1044 (31.4%) 35–54 years 1454 (43.7%) 55–64 years 513 (15.4%) 65 years and older 318 (9.6%) Disability type Physical impairments 918 (27.6%) Sensory impairments 2011 (60.4%) Cognitive impairments 225 (6.8%) Mental illness 175 (5.3%) Race European American 2447 (73.5%) African American 501 (15.0%) Hispanic American 35 (1.1%) Asian American 67 (2.0%) Native American 279 (8.4%) Education Special education 69 (2.1%) Less than high school 591 (17.8%) High school 1136 (34.1%) Post-secondary/associate 973 (29.2%) Bachelor degree or higher 560 (16.8%) Medical Benefits Yes 1266 (38.0%) No 2063 (62.0%) Cash Benefits Yes 1432 (43.0%) No 1897 (57.0%) Employed at application Yes 1549 (46.5%) No 1780 (53.5%)
benefits, medical benefits, and employment status at application.
The result of this step of the analysis found age, education, disability type, race, cash benefits, and employment status to be significant predictors (p < 0.05). Specifically, older adults were more likely to receive AT services; consumers younger than 65 years of age had a reduction in odds for receiving AT services compared to those 65 or older obtaining those services. In comparison to high school gradu- ates, people with associate’s degrees (OR = 1.27; 95% CI: 1.15 to 1.41) and bachelor’s degrees or higher (OR = 1.77; 95% CI: 1.55 to 2.01) were more likely to receive AT services. The odds of individuals with sensory impairments to acquire AT services were 4.31 times (OR = 4.31; 95% CI: 3.92 to 4.75) greater than the odds of those with physical impairments receiving those services. Compared to European Americans, African Americans had a 24% reduction in odds (OR = 0.76; 95% CI: 0.68 to 0.76) of receiving AT
 
 
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services. The odds of consumers who were receiv- ing cash benefits at the time of application for VR services to receive AT services were 1.55 times (OR = 1.55; 95% CI: 1.40 to 1.72) greater than the odds of those who did not receive cash benefits. The odds of consumers who were employed at application to obtain AT services were 1.51 times (OR = 1.51; 95% CI: 1.38 to 1.66) greater than the odds of those who were not employed.
In the second step of the logistic regression, 12 other VR service categories were entered into the model to determine what cluster of other services were associated with AT services. Results indicated that, after controlling for the effect of demographic variables, seven services were closely aligned with the receipt of AT services (p < 0.05). Specifically, VR Consumers receiving AT services were more likely to also obtain comprehensive assessment (OR = 1.49; 95% CI: 1.38 to 1.66), college or university train- ing (OR = 1.56; 95% CI: 1.38 to 1.76), occupational or vocational training (OR = 1.20; 95% CI: 1.05 to 1.37), augmentative skills training (OR = 2.48; 95% CI: 2.09 to 2.94), and miscellaneous training (OR = 1.47; 95% CI: 1.30 to 1.66). However, AT recipients were less likely to also obtain job readi- ness training (OR = 0.75; 95% CI: 0.64 to 0.87) and job search assistance (OR = 0.87; 95% CI: 0.76 to 0.99). The results of the hierarchical logistic regres- sion analysis are presented in Table 4.
4. Discussion
Findings from the present study reveal the charac- teristics of consumers who are most likely to receive AT services in VR and identify complementary VR services that are closely aligned with receiving AT ser- vices. The first noteworthy observation is that only about ten percent of VR consumers received AT ser- vices. People with sensory impairments were, by far, most likely to receive AT services, followed by peo- ple with physical impairments. The percentages of people with cognitive impairments and mental ill- ness receiving AT services were relatively small. This finding is not surprising given that a wide variety of technological devices have long been available to help persons with sensory impairments accommodate their limitations in vision, hearing, and communica- tion (Rumrill & Luft, 2006). Indeed, the high rate of AT use among VR consumers with sensory impair- ments is consistent with research reported by Kaye et al. (2008), which found AT usage rates among
people with visual and hearing disabilities ranging from a low of 70% to a high of 88%. Moreover, in a related study, Dutta et al. (2008) found that AT services contributed significantly to employment out- comes for VR consumers with sensory impairments, thereby underscoring the importance of AT services to the ultimate end goal of the VR process, namely, com- petitiveemployment in integratedcommunitysettings (Rubin et al., 2016). Additional research is needed to determine the relationship between AT services and employment outcomes among VR consumers with other disabilities such as mobility impairments, cog- nitive impairments, and mental illness.
Another noteworthy finding of the present study was that individuals over the age of 65 were more likely to receive AT services than those younger than age 65. This is not surprising given the well- documented fact that many disabling conditions related to vision, hearing, and mobility (a) increase in incidence as people age (Smart, 2009) and (b) can be accommodated through the use of a wide variety of AT devices and strategies (Job Accommodation Network, 2014). The link between age and AT use identified in this study is also supportive of findings from the Kaye et al. (2008) study, which found older independent living consumers more likely to use AT services than their younger counterparts. However, AT provided to older adults tended to be low-tech rather than high-tech devices.
Participants in this study with associate’s degrees, bachelor’s degrees, or higher levels of education were more likely to receive AT services than high school dropouts or high school graduates. This finding is not surprising, as people who are more educated are often more conversant with general-use and assistive technology than are people with lower levels of edu- cation (Kaye et al., 2008). These individuals may be more likely to occupy skilled professional, techni- cal, and/or managerial jobs that require considerable technological proficiency, and workplace accommo- dations for jobs of these types often involve the use of AT (Job Accommodation Network, 2014; Rumrill et al., 2013). There were no significant differences in the use of AT between male and female participants.
Findings from the present study also indicated that consumers who received AT services were more likely to receive comprehensive assessment, college or university training, occupational or vocational training, augmentative skills training, and miscel- laneous training. The provision of comprehensive assessment is not surprising, as AT recipients first need an assistive technology assessment to determine
 
 
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Table 4 Predictors of the Receipt of AT services
Independent variables Variables in the Equation � SE� Wald p OR (95% CI)
Step 1. Demographic variables Gender 0.03 0.04 0.36 0.55 1.03 (0.94 – 1.12) Men Age 0.00 35–54 year group 0.07 0.05 1.52 0.22 1.07 (0.96 – 1.19) 55–64 year group 0.00 0.07 0.00 0.97 1.00 (0.87 – 1.16) 65 years or older group 0.36 0.10 13.18 0.00 1.43 (1.18 – 1.74) Education 0.00 Special education –0.56 0.14 15.50 0.00 0.57 (0.44 – 0.76) Less than high school –0.14 0.06 4.74 0.03 0.87 (0.77 – 0.99) Post-secondary/associate 0.19 0.05 12.36 0.00 1.21 (1.09 – 1.34) Bachelor degree or higher 0.52 0.07 57.06 0.00 1.68 (1.47 – 1.92) Disability type 0.00 Sensory impairments 1.33 0.06 586.54 0.00 3.78 (3.39 – 4.21) Cognitive impairments –1.27 0.09 223.22 0.00 0.28 (0.24 – 0.33) Mental illness –1.80 0.09 441.66 0.00 0.17 (0.14 – 0.20) Race/Ethnicity 0.00 African American –0.26 0.06 19.24 0.00 0.77 (0.69 – 0.87) Native American –0.36 0.20 3.28 0.07 0.70 (0.47 – 1.03) Asian American –0.13 0.16 0.71 0.40 0.88 (0.64 – 1.19) Hispanic American –0.17 0.08 4.82 0.03 0.84 (0.73 – 0.98) Social Security Benefits Medicaid and Medicare –0.02 0.04 0.14 0.71 0.98 (0.90 – 1.07) Cash Benefit 0.36 0.05 44.79 0.00 1.44 (1.29 – 1.60) Employment at application 0.58 0.05 127.63 0.00 1.79 (1.62 – 1.98) Step 2. VR service variables Assessment 0.40 0.05 69.34 0.00 1.49 (1.36 – 1.64) Diagnostics & Treatment 0.03 0.05 0.34 0.56 1.03 (0.94 – 1.12) Counseling & Guidance –0.01 0.05 0.05 0.83 0.99 (0.90 – 1.09) College or University Training 0.45 0.06 52.13 0.00 1.56 (1.38 – 1.76) Occupational or Vocational Training 0.18 0.07 6.97 0.01 1.20 (1.05 – 1.37) On-the-job Training 0.23 0.14 2.78 0.10 1.26 (0.96 – 1.65) Remedial Training 0.34 0.18 3.55 0.06 1.40 (0.99 – 2.00) Job Readiness Training –0.29 0.08 13.17 0.00 0.75 (0.64 – 0.87) Augmentative Skills Training 0.91 0.09 107.23 0.00 2.48 (2.09 – 2.94) Miscellaneous Training 0.38 0.06 36.26 0.00 1.47 (1.30 – 1.66) Job Search Assistance –0.14 0.07 4.67 0.03 0.87 (0.76 – 0.99) Job Place Assistance –0.02 0.06 0.18 0.67 0.98 (0.87 – 1.09)
what kinds of devices are needed to help them achieve their vocational goals. It is also possible that people who need AT may have multiple functional limi- tations that require a comprehensive assessment to identify the array of services needed for employment. People who received AT services also required edu- cational and vocational training at higher rates than non-AT users, possibly indicating that VR counselors are more willing to provide AT services for con- sumers with potential for higher-level employment outcomes.
Merbitz, Merbitz, and Scherer (2005) suggested that the effectiveness of any AT device depends largely on whether the device meets the consumer’s personal needs that are unique to his or her idiosyn- cratic situation. To that end, understanding the
characteristics of consumers who are most likely to receive AT services in VR and complementary VR services that are closely aligned with the receipt of AT services is a step closer to reducing the gap between consumers’ functional capabilities and their interests in performing a variety of personal, social, and voca- tional activities.
4.1. Implications for VR practice
In spite of the growing need for AT devices and strategies among Americans with disabilities in gen- eral (National Center for Health Statistics, 1999; Rubin et al., 2016; Scherer, 2012), only about 10 percent of VR consumers nationwide receive AT ser- vices. This indicates a strong need for rehabilitation
 
 
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counsellors to familiarize themselves with the AT devices and strategies that are most likely to benefit consumers with different types of disabilities, assess- ment strategies to identify individual consumers’ AT needs, and resources that can assist with the provision of needed AT services. Rehabilitation counsellors may also need training and technical assistance regarding best practices for monitoring the ongoing effectiveness of AT services, including strategies for replacing AT devices that fall into disrepair or become outdated (Job Accommodation Network, 2014).
Results of this study suggest that VR consumers who are less educated, younger, and not of European descent were less likely to receive AT services than were other consumers. In addition, non-recipients of AT services were less likely than AT recipients to obtain comprehensive assessment, college or uni- versity training, occupational or vocational training, augmentative skills training, and miscellaneous train- ing. This suggests that a more thorough assessment may need to take place in the beginning phases of the VR process to assist those who are less likely to advo- cate for themselves in identifying AT devices and strategies that might help them meet their employ- ment and community living goals.
It is also important for VR counsellors to keep in mind that disability type significantly differ- entiated AT recipients from non-recipients in this study. Compared to people with sensory and physical impairments, those with cognitive impairments and mental illness were considerably less likely to receive AT services. This result reveals a lack of considera- tion on the part of VR counsellors of AT devices and strategies that could aid individuals with cognitive impairments and mental illness. This low rate of AT service provision for people with cognitive impair- ments and mental illness is especially problematic given that these individuals comprise more than half (58.1%) of VR consumers nationwide. Adams and colleagues (2008) indicated that recent designers of assistive technologies have turned much attention to the needs of individuals whose limitations are pri- marily cognitive. Such devices as memory aids, time management devices, prompting systems, assistive technology for cognition, and stimuli control devices are commercially available and have the potential to increase employability and quality of life for peo- ple with intellectual disabilities, learning disabilities, traumatic brain injuries, and cerebral vascular acci- dents. Scherer (2012) described her cognitive support technology (CST) model for promoting academic and employment success among people with cognitive
impairments resulting from traumatic brain injuries, learning disabilities, and multiple sclerosis. The CST approach utilizes universal-access tablet computers such as iPads coupled with cognitive enhancement applications (aka ‘apps’) that consumers can down- load to address such issues as memory, executive functioning, organizational skills, time management, and professional networking. There is some evi- dence that effective use of these customized assistive technology strategies leads to improved employment outcomes for people with cognitive impairments (Sauer, Parks, & Heyn, 2010). It is recommended that rehabilitation counsellors gain an understanding of AT devices and strategies to support consumers with cognitive limitations.
Formal training programs such as short courses, webinars, and continuing education workshops are needed to help rehabilitation counsellors understand environmental factors of the ICF model that impact how assistive technology services are identified and provided for consumers. Further research should also address the long-term effectiveness of AT devices and strategies; the cost-benefits of AT services; and the relationship between receipt of VR services and broader indices of community participation and qual- ity of life, both of which are acknowledged as important end goals of the VR process (Rubin et al., 2016). A more systematic understanding of AT service provision in the VR system could lead to a corresponding increase in the percentage of VR con- sumers receiving AT services, ultimately leading to better employment and independent living outcomes. The better the match between AT devices and the per- son’s personal and social needs, the more effective AT becomes in decreasing barriers to successful life outcomes. Merbitz, Lam, Chan, and Thomas (1999) stated that, although appropriate technology is criti- cal, poor technology choices can be harmful or even fatal, and that a tremendous breadth of knowledge can be required to serve individuals with assistive technology needs.
4.2. Limitations
The present study had several limitations that must be kept in mind when interpreting the results. First, this study examined data throughout the United States and did not investigate differences that may occur within and between states. Such information is criti- cal if individual states and VR agencies are to improve the quality and effectiveness of AT services. Also, the present study examined data from only one fis-
 
 
I.-C. Huang et al. / Assistive technology and VR 71
cal year, 2009. Consequently, it is unclear whether the provision of AT services to certain types of VR consumers changes over time. Multi-year inves- tigations would enable researchers to understand VR-sponsored AT services within important (and often dynamic) historical, political, and economic contexts.
Third, data for this study were extracted from the RSA-911 database generated from informa- tion recorded by rehabilitation counselors at various stages in the case service process. Subjective recall bias could have occurred if counselors did not consult case files to verify which services were provided and relied solely on memory. Data input errors may have also occurred. Although the RSA has developed 18 cross-checks to ensure data accuracy, there remain an unknown number of errors. However, the errors are assumed to be random and therefore should not lead to systematic bias in the data.
Fourth, the RSA-911 database does not identify specific types of assistive technology services that VR consumers receive. Further research from other data sources is needed to explicate the use of specific AT devices among people with different disabling condi- tions. Finally, the ex post facto nature of this research and the correlational design made it impossible to systematically manipulate the independent variables, which is a requirement for studies seeking to draw causal inferences (Bellini & Rumrill, 2009). There- fore, the results of the logistic regression analysis do not imply cause-and-effect relationships among study variables.
5. Conclusion
Characteristics of people with disabilities most likely to receive and not receive assistive technol- ogy services from state VR agencies were identified in this study, along with complementary VR services that are closely aligned with receiving AT services. Only about ten percent of VR consumers received AT services, with the majority of AT recipients being people with sensory or physical impairments. Indi- viduals with lower levels of educational attainment, of younger ages, and from non-white racial/ethnic groups were less likely to receive AT services. VR consumers who received AT services were more likely to receive a comprehensive assessment, sug- gesting that a thorough assessment may need to take place in the beginning phases of the VR process to determine AT service needs. Persons with cognitive
impairments and mental illness were considerably less likely to receive AT services, despite these indi- viduals comprising more than half (58.1%) of VR consumers nationwide. This suggests a need for reha- bilitation counselors to learn more about the AT devices and job accommodation strategies available to support consumers with cognitive limitations and mental illness. Lastly, further research is needed to develop a more systematic understanding of AT ser- vice provision in the VR system, leading to greater use of AT services that ultimately results in better employment and independent living outcomes.
Conflict of interest
The contents of this article were developed with support from the Rehabilitation Research and Train- ing Center on Evidence-Based Practice in Vocational Rehabilitation (RRTC-EBP VR) at the Univer- sity of Wisconsin-Madison and the University of Wisconsin-Stout and with funding provided by the U.S. Department of Health and Human Services, National Institute on Disability, Independent Living and Rehabilitation Research (Grant H133B100034). The ideas, opinions, and conclusions expressed, how- ever, are those of the authors and do not represent recommendations, endorsements, or policies of the U.S. Department of Health and Human Services.
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