Matching Items (296)
Description
The purpose of this thesis project is to analyze the impact that patient death has on long-term care providers. This study draws upon my own experience working as a licensed nursing assistant in a long-term care facility and also uses a qualitative analysis of six semi-structured interviews with other nursing assistants and hospice volunteers. With patient death being an unavoidable part of working in this area of healthcare, I explore how these care providers cope with losing their patients and the effectiveness of these coping mechanisms. Some strategies found that aided in coping with grief included staying detached from patients, being distracted by other aspects of the job, receiving support from co-workers, family members and/or supervisors, and having a religious outlook on what happens following death. In addition to these, I argue that care providers also utilize the unconscious defense mechanism of repression to avoid their feelings of grief and guilt. Repressing the grief and emotions that come along with patient death can protect the individual from additional pain in order for them to continue to do their difficult jobs. Being distracted by other patients also aids in the repression process by avoiding personal feelings temporarily. I also look into factors that have been found to affect the level of grief including the caregiver’s closeness to the patient, level of preparedness for the death, and first experience of losing a patient. Ultimately, I show that the common feelings accompanied by patient death (sadness, anger and stress) and the occurrence of burnout are harmful symptoms of the repression taking place.
ContributorsMasterson, Kaitlin (Author) / Loebenberg, Abby (Thesis director) / Mack, Robert (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This project revolves around the enhancement of an existing data collection device utilized for patient monitoring within the framework of the leadership of Shad Roundy's team. The initial deployment involved a 10-Axis Internal Measurement Unit (IMU) sourced from MetaMotionS (MMS) for comprehensive data acquisition from patients at University of Utah’s Downtown Behavioral Health Clinic (BHC). The primary objective transitioned towards optimizing the device's functionality, particularly addressing challenges related to limited battery life, device size, and data transfer efficiency. A systematic approach was undertaken to address these challenges, involving meticulous research into alternative batteries, with the CL 582728 identified as a promising solution capable of extending the device's operational lifespan to around one month. Additionally, the initiative aimed at refining data collection processes through real-time transmission facilitated by Raspberry Pi devices at BHC via Bluetooth, leveraging the energy-efficient Nordic Semiconductor nRF52840 Bluetooth chip. The project also entailed intricate circuit design endeavors utilizing Autodesk Eagle, with reference to a model provided by MMS. Despite encountering programming challenges for the microcontroller, the groundwork was laid for a conceptual solution, with plans to delegate the programming task to a team member possessing advanced expertise. Though the device has yet to be fabricated, the design is near completion.
ContributorsJust, William (Author) / Andersen, Erik (Thesis director) / Roundy, Shad (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05
Description
This project is a zine about the histories of the feminist self-help movement and the Treatment and Data Committee of ACT UP during the AIDS crisis. It also includes an interview with Peter Rodriguez, an original ACT UP NYC member. The zine explores these movements' connection to citizen science, layperson expertise, and knowledge production.
ContributorsZelinka, Audrey (Author) / Nelson, Elizabeth (Thesis director) / Brian, Jennifer (Committee member) / Boyles, David (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Human Evolution & Social Change (Contributor) / Sanford School of Social and Family Dynamics (Contributor)
Created2024-05
Description
The thesis explores the avenues of machine learning principles in object detection using TensorFlow 2 Object Detection API Libraries for implementation. Integrating object detection capabilities into ESP-32 cameras can enhance functionality in the capstone dragster application and potential applications, such as autonomous robots. The research implements the TensorFlow 2 Object Detection API, a widely used framework for training and deploying object detection models. By leveraging the pre-trained models available in the API, the system can detect a wide range of objects with high accuracy and speed. Fine-tuning these models using a custom dataset allows us to enhance their performance in detecting specific objects of interest. Experiments to identify strengths and weaknesses of each model's implementation before and after training using similar images were evaluated The thesis also explores the potential limitations and challenges of deploying object detection on real-time ESP-32 cameras, such as limited computational resources, costs, and power constraints. The results obtained from the experiments demonstrate the feasibility and effectiveness of implementing object detection on ESP-32 cameras using the TensorFlow2 Object Detection API. The system achieves satisfactory accuracy and real-time processing capabilities, making it suitable for various practical applications. Overall, this thesis provides a foundation for further advancements and optimizations in the integration of object detection capabilities into small, low-power devices such as ESP-32 cameras and a crossroad to explore its applicability for other image-capturing and processing devices in industrial, automotive, and defense sectors of industry.
ContributorsMani, Vinesh (Author) / Tsakalis, Konstantinos (Thesis director) / Jayasuriya, Suren (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05
Description
Criminal justice discourse has so far almost ignored a growing movement in police DNA collection, felony arrest DNA collection. Felony arrest collection is the practice of taking a DNA sample from everyone arrested for a felony, not charged nor convicted of a felony. To address this, this thesis project examines the history, concerns, and future of felony arrest DNA collection. It will use a failed attempt to pass felony arrest DNA collection bills in Arizona from 2022, as a case study to understand why this issue is so contentious, and what current evidence there is to support the two emerging sides of this debate. These bills, HB 2102 and 2572, started and died in the state legislature without much fanfare. However, felony arrest DNA collection has received academic attention, with discussions ranging from racial inequality to the security of databases. To bridge the gap, this project will bring academic information and original reporting to the public through the lens of the 2022 bills. It will contribute interviews with relevant political parties, a synthesis from academic sources, and transparent DNA collection rates in Arizona to create a base of knowledge for the future discussions surrounding felony arrest DNA collection.
ContributorsRamirez, Sophia (Author) / Brian, Jennifer (Thesis director) / Gomez, Alan (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2024-05
Description
The vast majority of matter found within the universe is from the dark sector composed of 75% dark energy and 20% dark matter. While the accelerated expansion rate of the universe is attributable to dark energy, dark matter is fundamentally defined as an unknown substance that interacts gravitationally with its surroundings. The research presented here investigates the methods derived from observational signatures to construct theoretical models of dark matter.
ContributorsFigueroa, Natalie (Author) / Shovkovy, Igor (Thesis director) / Lebed, Richard (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05