A Comparison of Plaintiff and Defense Expert Witness H-Index Scores in Mild Traumatic Brain Injury Civil Litigation (open access)

A Comparison of Plaintiff and Defense Expert Witness H-Index Scores in Mild Traumatic Brain Injury Civil Litigation

This study examines the background and qualifications of plaintiff and defense experts using the H-Index score as quantification of expert background and qualifications. The goal is to better understand the similarities and differences among the professionals offering paid expert witness testimony in mild traumatic brain injury (mTBI) civil litigation. In this quantitative study, descriptive statistics include the mean and standard deviation scores for the data to support examining measures of central tendency and variance, respectively. The study includes the use of logistic regression and the Wilcoxon signed-rank test, and their statistical assumptions were tested to determine whether they would be used or if it was more appropriate to use a non-parametric test. The study included two research questions: How do the qualifications of plaintiff and defense expert witnesses in mild traumatic brain injury civil litigation compare? and to what extent does a higher h-index correlate with a favorable litigation outcome in a mild traumatic brain injury case? The findings for the hypothesis tests associated with the research questions led to the acceptance of the null hypothesis in each test. There was a lack of asymptotic significance in Hypothesis 1 and a lack of significance in Hypothesis 2. The findings from …
Date: August 2022
Creator: Victor, Elise C
System: The UNT Digital Library

Digital Equity in K-12 Education: Conceptualization and Analysis of Students' Digital Opportunity

Although digital equity is a recognized challenge in our K-12 school system, there is little research in using a holistic framework to investigate pre-conditions necessary for K-12 students to participate in digital learning and online processes. A conceptual framework of students' digital opportunity (SDO) is developed to represent the essential components of digital connectivity. The four key components are broadband internet availability, broadband usage, digital device ownership, and speed quality. A composite measure of SDO was created to quantitatively represent and measure the differences across 3,138 counties in the United States. Furthermore, spatial autocorrelation was applied to evaluate if the distribution of the SDO score is associated with geographical characteristics at the county level. The result showed the presence of significant county-level clusters with concentrations of high or low SDO scores. While the spatial analysis provided evidence of where the gaps in digital opportunities are located, there are underlying factors at the micro level that would need further investigation. This study suggests a collective approach between private and public entities to address the K-12 digital equity issue. The necessary conditions presented in the SDO model must be addressed first in order to bring change to K-12 students and schools in …
Date: May 2022
Creator: Jim, Cary Ka Wai
System: The UNT Digital Library
Data Quality Evaluation and Improvement for Machine Learning (open access)

Data Quality Evaluation and Improvement for Machine Learning

In this research the focus is on data-centric AI with a specific concentration on data quality evaluation and improvement for machine learning. We first present a practical framework for data quality evaluation and improvement, using a legal domain as a case study and build a corpus for legal argument mining. We first created an initial corpus with 4,937 instances that were manually labeled. We define five data quality evaluation dimensions: comprehensiveness, correctness, variety, class imbalance, and duplication, and conducted a quantitative evaluation on these dimensions for the legal dataset and two existing datasets in the medical domain for medical concept normalization. The first group of experiments showed that class imbalance and insufficient training data are the two major data quality issues that negatively impacted the quality of the system that was built on the legal corpus. The second group of experiments showed that the overlap between the test datasets and the training datasets, which we defined as "duplication," is the major data quality issue for the two medical corpora. We explore several widely used machine learning methods for data quality improvement. Compared to pseudo-labeling, co-training, and expectation-maximization (EM), generative adversarial network (GAN) is more effective for automated data augmentation, especially …
Date: May 2022
Creator: Chen, Haihua
System: The UNT Digital Library

Adoption of Wearable Devices by Older Adults

This dissertation is organized in a traditional format while including three essays that address specific research questions. Essay 1 examined the relationship between physical activity and community engagement and their effect on mental well-being among older men and women. Data from National Health and Aging Trends Study (NHATS) from 2018 to 2020 were explored and the posited relationships were tested. This essay provides empirical support that older adults who are reasonably active and involved in the community have greater mental well-being than those who isolate themselves. Essay 2 provides insight into older adults' motivation to improve their physical activity through the use of a fitness tracker. The key finding from this study is that wearables, especially fitness trackers, can significantly facilitate increased physical activity. Essay 3 is a mixed-methods study to understand older adults' perception of the usefulness of fitness trackers and interaction with such devices. Findings suggest that to increase the adoption of fitness trackers among older adults, makers could improve the esthetics and quality of the wristband in addition to the battery life of the tracker.
Date: May 2022
Creator: Enamela, Pranathy
System: The UNT Digital Library