Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
This thesis addresses the global and national health landscape of disparities to provide insight into the social factors such as education, socioeconomic status, and environment that impact marginalized groups. A positive correlation between race, residency, and lower socioeconomic status among global and national landscapes was made with oral health disparities demonstrating poorer health outcomes among these groups. Through a multistep approach this thesis aimed to provide solutions to contribute to the efforts of developing effective policies and interventions that aim to promote oral health equity.
Understanding and predicting health outcomes of adults by examining adverse childhood experiences (ACE) is one tool available to healthcare professionals. This tool originated from the 1988 ACE study, and because of its findings, it has been widely implemented and utilized. This literature review investigates how practical and applicable those findings are to validate its current widespread practice. It is concluded that the original study is not comprehensive enough to justify its use in a significant way; however, this review discusses how it can be built upon and modernized to capture more demographics, validate its results to apply to more populations, and become a better predictive model.