Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Date of Graduation


Document Type


Degree Name

Master of Arts (MA)


Department of Graduate Psychology


Bernice Marcopulos

Kethera Fogler

Bryan K. Saville


For many individuals, recovery from moderate to severe brain injuries involves returning to a level of pre-injury productivity. Specifically, previous research has focused extensively on factors predicting return to employment, where students are inconsistently categorized with those in competitive employment. Moreover, research dedicated to return to school for students in secondary and tertiary education is largely qualitative; very few studies have utilized predictive modeling on a sample composed solely of students. For this study, a model including days of post-traumatic amnesia (PTA), length of stay (LOS), rehabilitation discharge Disability Rating Scale (DRS) scores, and educational level was used to predict return to school one year post-injury in a sample of 196 students within the Traumatic Brain Injury Model Systems National Database. For this sample, the overall return to school rate was 63.78%. Logistic regression results indicated that lower scores on the DRS and being in high school pre-injury resulted in the highest probabilities of returning to school one year post-injury. Results also suggested that for some, productivity post-injury was possible outside of the school setting, and for others, returning to school did not indicate long-term productivity, which highlights that productivity defined within a school setting and within a work setting may be somewhat distinct concepts. College students were much less likely to return to school within a year following injury than high school students. Consequently, more outreach and support for those students may improve awareness of disability services and heighten the return to school rate in the future. Further implications and suggestions by which to improve future models are discussed.



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