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Exploring Age Differences in the Experiences of Academic Supports Among College Students with Mental Health Conditions

Sabella, Kathryn
Costa, Amanda
Salzer, Mark
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2020-02-14
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Abstract

College students with mental health conditions struggle to succeed academically potentially limiting their future. Previous research has shown that college students of all ages with mental health conditions under-utilize academic supports. However traditional (i.e. young adult) and non-traditional (i.e. older adult) students have different academic learning experiences and may also have different academic support experiences. This research explored the academic support experiences of young adult college students with mental health conditions and compared them to those of older adult college students with mental health conditions. A secondary analysis of a previously collected dataset on the academic experiences of college students with mental health conditions was conducted. The sample was limited to only current students at time of survey and respondents were categorized as either young adult (ages 18-24) or older adult (age 25 and over). Descriptive and exploratory quantitative analysis compared their mental health experiences, utilization of academic supports, and engagement on campus. Young adults were less likely to access formal disability services, less satisfied in their college experience and reported lower quality relationships with staff and faculty as compared to their older peers. Further investigation of the many potential reasons for these differences is warranted. As college student bodies become more diversified, staff and faculty need to recognize that student capacities and experience vary not just due to the potential impact of gender, race, or class, but also due to age and life experiences.

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10.7191/pib.1142
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Funding and Acknowledgements
We’d like to thank Mark Salzer, Ph.D. and the Temple University Collaborative on Community Inclusion of Individuals with Psychiatric Disabilities for providing the original dataset and their guidance during data analysis.
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© 2020 University of Massachusetts