Briefs

• Employment Programs for Transition-Age Youth with Disabilities that Feature Vocational Rehabilitation Services and Supports: A Literature Review
  By: David Mann
  PDF | WORD

• Supporting Transition-Aged Youth After WIOA: A Look At State Vocational Rehabilitation Agencies
  By: Kaylyn Swankoski, Stephanie Rosenbluth and Purvi Sevak
  PDF | WORD

PowerPoint Presentations

• A Project of the Rehabilitation, Research and Training Center (RRTC) on VR Practices and Youth presented to CSAVR
  By: Purvi Sevak
  PDF | WORD

Resources

• Database of SVRA Programs
  EXCEL

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This project has two sequential goals: (1) to review current State VR programs serving youth and young adults with disabilities, including efforts to assess the efficacy of these programs, and (2) to examine ways to use administrative data and data analytics to improve agency practices and promote youth outcomes. Our findings will help agencies identify better ways of collecting and using data on their clients so they can track services and outcomes for the youth they serve and identify and implement the most effective practices.

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We will test three hypotheses on existing VR practices, the evidence base, and the potential of data analytics to expand the evidence base.

• RQ 1 – VR agencies will differ in the range of programs they offer to youth with disabilities, reflecting variation in local and regional resources and preferences for services, the diversity of needs and interests among youth with disabilities, and lack of a strong consensus on what works best in promoting transition outcomes.
• RQ 2 – VR agencies produce little rigorous evidence regarding the efficacy of these programs. Agency use of data analytics is in its infancy, and agencies can rarely afford the cost of other approaches to the production of rigorous evidence.
• RQ 3 – agencies can use well-developed DASs to systematically and cost-effectively improve services to transition-age youth clients.

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For the environmental scan, the sample will include all 51 general and combined VR agencies in a state or the District of Columbia. We will exclude agencies serving only individuals with blindness, as well as those in U.S. territories, because such agencies serve a relatively small number of people, and the applicability of findings would be limited. From these, we will select five to seven agencies with multiple programs for transition-age youth for further follow-up on evidence they may collect on program efficacy. This limited sample will allow us to collect detailed qualitative information on practices and their efficacy in a cost-efficient manner. For the development and testing of a DAS, we will work with a single agency that serves enough transition-age youth each year to support the testing of the system. We will confine our attention to the 25 or so general or combined agencies with the most clients. Based on analysis of 911 data, we expect each of these agencies serves at least 2,000 youth in a year, and some will serve many more.

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For the environmental scan, we will conduct a review of recent published and unpublished studies, RSA monitoring reports, and agencies’ annual stage plans. State plans include data on the agency’s involvement with state departments of education and local schools, programs specific to youth with disabilities, and transition staffing. Since 2011, the monitoring reports have included a section on transition service and employment outcomes for youth, based on RSA visits to VR agencies that occur every six years to assess performance and collect information on areas of special interest. From this review, we will identify agencies with multiple programs for transition-age youth and select five to seven agencies for follow-up interviews. We will design the interviews to confirm the list of policies and programs that we developed as part of the environmental scan and ask about evidence the agency may collect on program efficacy, its current use of data analytics, and its interest and ability to partner with us in the second stage.

For the DAS, as we proceed with the first stage, we will contact the agencies we have identified as having an array of services and supports for youth, as well as the information technology staff and software capability needed to support a DAS. We will choose one agency and negotiate an agreement under which our team will provide technical assistance to design and implement a DAS. Together we will (1) develop a low-cost system that the VR agency can use with minimal external technical assistance, and (2) demonstrate that this system works well and has high value relative to its cost.

We will use two data analytic tools: (1) predictive analysis (PA), and (2) rapid-cycle evaluation (RCE). PA refers to statistical models to predict outcomes for individuals based on information available. VR outcome examples include measures of client engagement, cooperation, and follow-up; employment outcomes; and resource use and expenditures. Predictors would come from the VR client’s application and any information from the current engagement to date. Predictions of the models are used to target resources more efficiently to current clients. For example, if a model’s prediction indicates that a client is likely to not stay engaged, the client’s counselor can take steps to ensure continued engagement—steps that would waste effort if used for those very likely to stay engaged on their own. RCE refers to modest, rapid experiments, usually involving promising but unproven innovations in processes or services, most often designed to address specific challenges. We will work with the agency to identify a known challenge (for example, lack of engagement after applying for VR services), develop one or more innovations to address the challenge, then test the efficacy of the solution by implementing it for targeted clients.

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