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- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Status: Published
Description
COVID-19 has shocked the bedrock of society, impacting both human life and the economy. Accompanying this shock has been the psychological distress inflicted onto the general population as a result of the emotion strain stemming from isolation/quarantine policies, being sick with COVID-19, dealing with COVID-19 losses, and post-COVID syndrome and its effect on quality of life. The psychological distress has been experienced by the general population, but compared to middle age (30-50) and older adults (>50 years of age), it has been young adults (18-30 years old) who have been more psychologically affected (Glowacz & Schmits, 2020). Psychological distress, specifically anxiety and depression, has been exacerbated by feelings of uncertainty, fear of illness, losing loved ones, and fear of post-COVID syndrome. Post-COVID syndrome, as with other post-viral syndromes such as post viral SARS involve lingering symptoms such as myalgic encephalomyelitis or Chronic Fatigue Syndrome (CFS), and loss of motivation (Underhill, 2015). In addition to these symptoms, patients suffering from post-COVID syndrome have also presented brain inflammation and damaged brain blood vessels (Meinhardt et al., 2021), Endotheliitis (Varga et al., 2020), CV abnormalities and changes in glucose metabolism (Williams et al., 2020). CV abnormalities and changes in glucose metabolism are connected to chronic illnesses like diabetes and heart disease respectively. These chronic illnesses are then associated with higher risk for depression as a result of the stress induced by the symptoms and their impact on quality of life (NIMH, 2021). Further monitoring, and research will be important to gauge ultimate physiological and psychological impact of COVID-19.
ContributorsPiedra Gonzalez, Michael (Author) / Vargas, Perla (Thesis director) / Oh, Hyunsung (Committee member) / College of Health Solutions (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This paper is regarding the nutritional choices college students in Arizona choose. This is based on many factors, but ranks and investigates why students choose this one factor. Students value time over all other factors, money, health, and location.
ContributorsJohnson, Ashleigh (Author) / Kingsbury, Jeffrey (Thesis director) / Culbertson, Jade (Committee member) / Sealey, Joshua (Committee member) / Swerzenski, Jared (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Over the last decade, plant-based diets have grown in popularity. However, these diets have a significant problem- diet adherence and maintenance. Social Support is a key factor in long-term adherence. In response, we created a scale to measure perceived Social Support in the context of plant-based diets to further this growing area of scholarly research.
ContributorsHinsberger, Emily (Author) / Wharton, Christopher (Thesis director) / Vizcaino, Maricarmen (Committee member) / College of Health Solutions (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
Background: Natural Language Processing models have been trained to locate questions and answers in forum settings before but on topics such as cancer and diabetes. Also, studies have used filtering methods to understand themes in forum settings regarding opioid use. However, studies have not been conducted regarding training an NLP model to locate the questions people addicted to opioids are asking their peers and the answers they are receiving in forums. There are a variety of annotation tools available to help aid the data collection to train NLP models. For academic purposes, brat is the best tool for this purpose. This study will inform clinical practice by indicating what the inner thoughts of their patients who are addicted to opioids are so that they will be able to have more meaningful conversations during appointments that the patient may be too afraid to start.
Methods: The standard NLP process was used for this study in which a gold standard was reached through matched paired annotations of the forum text in brat and a neural network was trained on the content. Following the annotation process, adjudication occurred to increase the inter-annotator agreement. Categories were developed by local physicians to describe the questions and three pilots were run to test the best way to categorize the questions.
Results: The inter-annotator agreement, calculated via F-score, before adjudication for a 0.7 threshold was 0.378 for the annotation activity. After adjudication at a threshold of 0.7, the inter-annotator agreement increased to 0.560. Pilots 1, 2, and 3 of the categorization activity had an inter-annotator agreement of 0.375, 0.5, and 0.966 respectively.
Discussion: The inter-annotator agreement of the annotation activity may have been low initially since the annotators were students who may have not been as invested in the project as necessary to accurately annotate the text. Also, as everyone interprets the text slightly differently, it is possible that that contributed to the differences in the matched pairs’ annotations. The F-score variation for the categorization activity partially had to do with different delivery systems of the instructions and partially with the area of study of the participants. The first pilot did not mandate the use of the original context located in brat and the instructions were provided in the form of a downloadable document. The participants were computer science graduate students. The second pilot also had the instructions delivered via a document, but it was strongly suggested that the context be used to gain an understanding of the questions’ meanings. The participants were also computer science graduate students who upon a discussion of their results after the pilot expressed that they did not have a good understanding of the medical jargon in the posts. The final pilot used a combination of students with and without medical background, required to use the context, and included verbal instructions in combination with the written ones. The combination of these factors increased the F-score significantly. For a full-scale experiment, students with a medical background should be used to categorize the questions.
Methods: The standard NLP process was used for this study in which a gold standard was reached through matched paired annotations of the forum text in brat and a neural network was trained on the content. Following the annotation process, adjudication occurred to increase the inter-annotator agreement. Categories were developed by local physicians to describe the questions and three pilots were run to test the best way to categorize the questions.
Results: The inter-annotator agreement, calculated via F-score, before adjudication for a 0.7 threshold was 0.378 for the annotation activity. After adjudication at a threshold of 0.7, the inter-annotator agreement increased to 0.560. Pilots 1, 2, and 3 of the categorization activity had an inter-annotator agreement of 0.375, 0.5, and 0.966 respectively.
Discussion: The inter-annotator agreement of the annotation activity may have been low initially since the annotators were students who may have not been as invested in the project as necessary to accurately annotate the text. Also, as everyone interprets the text slightly differently, it is possible that that contributed to the differences in the matched pairs’ annotations. The F-score variation for the categorization activity partially had to do with different delivery systems of the instructions and partially with the area of study of the participants. The first pilot did not mandate the use of the original context located in brat and the instructions were provided in the form of a downloadable document. The participants were computer science graduate students. The second pilot also had the instructions delivered via a document, but it was strongly suggested that the context be used to gain an understanding of the questions’ meanings. The participants were also computer science graduate students who upon a discussion of their results after the pilot expressed that they did not have a good understanding of the medical jargon in the posts. The final pilot used a combination of students with and without medical background, required to use the context, and included verbal instructions in combination with the written ones. The combination of these factors increased the F-score significantly. For a full-scale experiment, students with a medical background should be used to categorize the questions.
ContributorsPawlik, Katie (Author) / Devarakonda, Murthy (Thesis director) / Murcko, Anita (Committee member) / Green, Ellen (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Artificial intelligence (AI) and machine learning (ML) is rapidly evolving with enormous impact on a wide range of individual and societal matters including in health care, now and in the future. The goal of this research project is to assess the current knowledge level of AI and ML in health care among healthcare professionals and the lay public. Results from this research will identify knowledge gaps and educational opportunities to improve future use and applications of AI and ML in health care.
ContributorsShen, Maria (Author) / Martin, Thomas (Thesis director) / Wheatley-Guy, Courtney (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2022-05
Evaluating the Heterogeneity of Logistic Regression Models to Predict Coronary Artery Disease Status
Description
Coronary artery disease (CAD) is one of the most diagnosed heart diseases globally, affecting about 5% of adults over the age of twenty[1]. Lifestyle changes can positively impact risk of developing CAD and are especially important for individuals with high genetic risk [1]. In this study, we sought to predict the likelihood of developing CAD using genetic, demographic, and clinical variables. Leveraging genetic and clinical data from the UK Biobank on over 500,000 individuals, we classified and separated 500 genetically similar individuals to a target individual from another 500 genetically dissimilar individuals. This process was repeated for 10 target individuals as a proof-of-concept. Then, CAD-related variables were used and these include age, relevant clinical factors, and polygenic risk score to train models for predicting CAD status for the 500 genetically similar and 500 genetically dissimilar groups, and determine which group predicts the likelihood of CAD more accurately. To compute genetic similarity to the target individuals we used the Mahalanobis distance. To reduce the heterogeneity between sexes and races, the studies were restricted to British male Caucasians. The models using the more similar individuals demonstrated better predictive performance. The area under the receiver operating characteristic curve (AUC) was found to be significantly higher for the ‘similar’ rather than the ’dissimilar’ groups, indicating better predictive capability (AUC=0.67 vs. 0.65, respectively; p-value<0.05). These findings support the potential of precision prevention strategies, since one should build predictive models of disease for any one target individual from more similar individuals to that target even within an otherwise homogenous group of individuals (e.g., British Caucasians). Although intuitive, such practices are not done routinely. Further validation and exploration of additional predictors are warranted to enhance the predictive accuracy and applicability of the model.
ContributorsPandari, Sadhana (Author) / Ghassamzadeh, Hassan (Thesis director) / Scotch, Matthew (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2024-05
Description
The pursuance of higher education has always been a competitive feat and as the years progress competition amongst students becomes tighter. This competition increases when focus is placed upon the Asian American student population and the stressors that are placed on them to excel in their respective fields of academics. The Asian American population in particular also has a high prevalence of not seeking out mental health services as a whole due to high stigma associated with the topic (Zhang et al 2019). This study intended to determine the psychological and social impacts that academic stress may have on female Asian American students and why university mental health services go underutilized by this crowd. The impacts of academic stressors on student’s lives were measured using a 5 point survey scale (1 being not stressful and 5 being extremely stressful). Willing participants were then selected for an interview in which 10 individuals' experiences were recounted. The results indicate that an overwhelming majority of students reported experiencing negative impacts to multiple aspects of psychological well being. A high number of these students also reported feeling uncomfortable to seek mental health aid due to familial judgment and cultural taboos. These findings indicate significant numbers of students struggling to cope with the implications of poor mental health in their lives. This study serves to decrease the prevalence of academic stress in the lives of Asian American students by increasing their therapy seeking behaviors. Upon its completion, the researcher provided ASU counseling services with suggestions to increase utilization by female Asian Americans.
ContributorsJones, Shredha (Author) / Kappes, Janelle (Thesis director) / Jimenez, Laura (Committee member) / Barrett, The Honors College (Contributor) / School of Counseling and Counseling Psychology (Contributor) / College of Health Solutions (Contributor)
Created2024-05