This thesis project is the result of close collaboration with the Arizona State University Biodesign Clinical Testing Laboratory (ABCTL) to document the characteristics of saliva as a test sample, preanalytical considerations, and how the ABCTL utilized saliva testing to develop swift COVID-19 diagnostic tests for the Arizona community. As of April 2021, there have been over 130 million recorded cases of COVID-19 globally, with the United States taking the lead with approximately 31.5 million cases. Developing highly accurate and timely diagnostics has been an important need of our country that the ABCTL has had tremendous success in delivering. Near the start of the pandemic, the ABCTL utilized saliva as a testing sample rather than nasopharyngeal (NP) swabs that were limited in supply, required highly trained medical personnel, and were generally uncomfortable for participants. Results from literature across the globe showed how saliva performed just as well as the NP swabs (the golden standard) while being an easier test to collect and analyze. Going forward, the ABCTL will continue to develop high quality diagnostic tools and adapt to the ever-evolving needs our communities face regarding the COVID-19 pandemic.

The COVID-19 pandemic has resulted in preventative measures and has led to extensive changes in lifestyle for the vast majority of the American population. As the pandemic progresses, a growing amount of evidence shows that minority groups, such as the Deaf community, are often disproportionately and uniquely affected. Deaf people are directly affected in their ability to personally socialize and continue with daily routines. More specifically, this can constitute their ability to meet new people, connect with friends/family, and to perform in their work or learning environment. It also may result in further mental health changes and an increased reliance on technology. The impact of COVID-19 on the Deaf community in clinical settings must also be considered. This includes changes in policies for in-person interpreters and a rise in telehealth. Often, these effects can be representative of the pre-existing low health literacy, frequency of miscommunication, poor treatment, and the inconvenience felt by Deaf people when trying to access healthcare. Ultimately, these effects on the Deaf community must be taken into account when attempting to create a full picture of the societal shift caused by COVID-19.

Cosplay, or the act of dressing up as a character, is a hobby that has become increasingly popular over the last several decades. Since its conception in 1939, the practice of cosplaying has always been heavily tied to that of conventions. Until 2019 these conventions were utilized by cosplayers to showcase their outfits and creations – in a phenomenon called “masking” – but with the widespread emergence of the COVID-19 pandemic, conventions were put on hold for over a year. This paper questions how convention culture has changed in a world where cosplay can no longer rely on it due to the COVID-19 pandemic. Through analysis, personal narrative, and a critical reflection, the authors compare Saboten Con 2019 and 2021 to discuss that despite the numerous changes to the physical culture of conventions, the social essence of conventions has remained the same.

Cosplay, or the act of dressing up as a character, is a hobby that has become increasingly popular over the last several decades. Since its conception in 1939, the practice of cosplaying has always been heavily tied to that of conventions. Until 2019 these conventions were utilized by cosplayers to showcase their outfits and creations – in a phenomenon called “masking” – but with the widespread emergence of the COVID-19 pandemic, conventions were put on hold for over a year. This paper questions how convention culture has changed in a world where cosplay can no longer rely on it due to the COVID-19 pandemic. Through analysis, personal narrative, and a critical reflection, the authors compare Saboten Con 2019 and 2021 to discuss that despite the numerous changes to the physical culture of conventions, the social essence of conventions has remained the same.

Our thesis project is a 5-person group thesis that was created over the span of two years. In the summer of 2020, at the height of the first wave of the COVID-19 pandemic, our group first met and discussed our shared interests in mask-wearing and individual factors that we each thought had significant impacts on mask-wearing among Barrett students. We each decided on factors that we wanted to investigate and subsequently split into three main groups based on our interests: culture and geography, medical humanities, and medical and psychological conditions. Despite these different interests, we continued to treat our thesis as a five-person project rather than three different projects. We then constructed a survey, followed by several focus group sessions and interview questions to ask Honors students. In January 2021, we received approval from the IRB for our project, and we quickly finalized our survey, focus group and interview questions. In February 2021, we sent out our survey via the Barrett Digest, which we kept open for approximately one month. We also sent out advertisements for our survey via social media platforms such as Twitter and Discord. Following completion of the survey, we contacted all of the respondents who stated that they were interested in participating in focus groups and interviews. Focus groups and interviews were conducted in March and April 2021, and results were analyzed and correlated to our individual subtopics. Each of the focus group and interview participants received $50 each, and three randomly-selected students who completed the survey received $25 each. From April 2021 until April 2022, we analyzed our results, came to conclusions based on our initial topics of interest, and constructed our paper.

In the following paper, I aim to form relationships between different patient factors and no-show rates. The culmination of these relationships will then be used in a logistic regression model. Data collected from a survey at 26 HonorHealth clinics were analyzed using odds ratios and relative risk methods. Of 310,307 visits collected, 22,280 of them were no shows (7.2%), an 11% decrease from national averages (18.8%). This fueled the study, along with a grant filed by HonorHealth looking at the impact of telehealth on the working poor. A binary logistic regression method was run over the data, and less than 1% of patients' no-shows were predicted correctly. By adding factors, and improving the diversity in the data collected, model accuracy can be improved.