Phase I development of the SGA Model: Use of administrative data and expert opinion to identify key components of the SGA Model.
Saved in:
| Title: | Phase I development of the SGA Model: Use of administrative data and expert opinion to identify key components of the SGA Model. |
|---|---|
| Authors: | Foley, Susan M.1 Susan.Foley@umb.edu, Haines, Kelly1, Mock, Linda1, Foley, Susan, Sevak, Purvi |
| Source: | Journal of Vocational Rehabilitation. 2020, Vol. 53 Issue 3, p261-272. 12p. 8 Charts. |
| Subject Terms: | *Counseling, *Delphi method, *Employment of people with disabilities, *Motivation (Psychology), *Vocational rehabilitation, *Theory, Interviewing, Mathematical models, Social security |
| Abstract: | BACKGROUND: The SGA Model Demonstration tested a coordinated team approach that integrated specific components of vocational rehabilitation services to determine if the model would increase earnings outcomes of Social Security Disability income beneficiaries who sought VR services in Kentucky and Minnesota. OBJECTIVE: This paper provides details on the first phase of development of the SGA intervention. METHODS: Researchers combined a Delphi process, key informant interviews, and administrative data review to identify practices that were high priority for inclusion in the model. RESULTS: Researchers reached a consensus on the high priority components to construct a testable intervention under the control of the vocational rehabilitation system. CONCLUSIONS: The SGA Project team identified three practice domains to guide an intensive case study for Phase II development of the intervention. These included pacing of services, work incentives counseling, and strategies for employment service delivery. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Vocational Rehabilitation is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!