Quantum computing is an emerging and promising alternative to classical computing due to its ability to perform rapidly complex computations in a parallel manner. In this thesis, we aim to design an audio classification algorithm using a hybrid quantum-classical neural network. The thesis concentrated on healthcare applications and focused specifically on COVID-19 cough sound classification. All machine learning algorithms developed or implemented in this study were trained using features from Log Mel Spectrograms of healthy and COVID-19 coughing audio. Results are first presented from a study in which an ensemble of a VGG13, CRNN, GCNN, and GCRNN are utilized to classify audio using classical computing. Then, improved results attained using an optimized VGG13 neural network are presented. Finally, our quantum-classical hybrid neural network is designed and assessed in terms of accuracy and number of quantum layers and qubits. Comparisons are made to classical recurrent and convolutional neural networks.

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.

The following study was designed to better understand the perceived stress levels and common coping strategies of nursing students attending Arizona State University during the COVID-19 pandemic. Lazarus and Folkman’s (1984) transactional theory of stress and coping suggests that stress is a product of the transaction between an individual and their complex environment. During the COVID-19 pandemic nursing students were forced to close and utilize online learning methods to ensure the safety of students, faculty, and staff. The current study identifies the perceived stress levels and common coping strategies of undergraduate nursing students at Arizona State University. A survey was sent to the identified population which asked participants to rate their stress level of attending nursing school during COVID-19 on a scale from one to ten. In addition, the Carver’s COPE inventory was utilized to determine common coping strategies among the nursing student population. The results of this study indicate that the closure of universities, and utilization of online learning methods increased stress levels of nursing students attending Arizona State University during COVID-19. In addition, nursing students most common coping strategies were identified as the use of planning, a problem-focused coping strategy, reinterpretation and growth, and active coping.

The COVID 19 pandemic has highlighted the necessity of accurately and simply relying scientific discovery and information to the public. Among scientists, the practice is to reduce jargon, engage the audience through storytelling, and include enough detail to give a broad understanding of a narrow topic. Conflict between journalists and scientists leads to a creation of a different narrative for the general public. The news site CNN.com was searched with the google archive function by year for articles that included the keyword vaccine. Articles were sorted into categories of main focus such as political, cultural and scientific or mixed. Results were analyzed and conclusions made about the amount of content in each category for the kind of narrative being written about vaccines, with most years having most articles in the political category. Possible effectiveness of mixed categories were discussed and areas future research identified.

The COVID-19 pandemic has led to high demand for testing. It also has affected minorities at disproportionate rates, and the impact is made worse due to the underlying mistrust minorities hold against medical institutions. A similar theme is observed against physicians, especially Asian American healthcare providers experiencing firsthand discrimination. Therefore, there is this reciprocal relationship between the patient and provider rooted in mistrust and discriminatory biases. This thesis aims to analyze how ASU Biodesign COVID-19 Testing Lab (ABCTL) functions to implicitly modify potential effects of a patient-provider relationship based on the concepts of health equity and cultural humility. The analysis then suggests that ABCTL accomplished the concept of health equity through community partnerships and by recruiting community healthcare workers for long-term stability, it successfully achieved cultural humility.

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.