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 …

This study explores information seeking behavior of second-generation Muslim immigrants utilizing factors such as critical, cultural, and religious literacy skills. The study examined the second-generation immigrants' ability to balance their parents' and grandparents' native culture and traditions with the culture and traditions of their country. The interview questions were designed using the cognitive authority theory and the figured worlds theory that provides an explanation for the mentality of those who are in environments influenced by culture or religion. An interesting main finding of the study is that participants sought more religious-based rather than culturally-based information. Participants seek information from their parents, communities, and religious leaders, but are particular with who they consider credible and reliable; if the person providing the information follows a similar lifestyle to the participants, they are more likely to hold cognitive authority. Four different themes emerged from the study. The first is "religious focus" where many participants stated that religion is rather static whereas culture can evolve and change with time, location, and events. The second theme emerged is the reliance on family members for religious literacy given the close upbringing of Muslim extended family system. The third theme indicated that although information seeking behavior relied …

The goal of this work is to improve the robustness and generalization of deep learning models, using a similar approach to the unsupervised "innate learning" strategy in visual development. A series of research studies are presented to demonstrate how an unsupervised machine learning efficient coding approach can create filters similar to the receptive fields of the primary visual cortex (V1) in the brain, and these filters are capable of pretraining convolutional neural networks (CNNs) to enable faster training times and higher accuracy with less dependency on the source data. Independent component analysis (ICA) is used for unsupervised feature extraction as it has shown success in both applied machine learning and modeling biological neural receptive fields. This pretraining applies equally well to various forms of visual input, including natural color images, black and white, binocular, and video to drive the V1-like Gabor filters in the brain. For efficient processing of typical visual scenes, the filters that ICA produces are developed by encoding natural images. These filters are then used to initialize the kernels in the first layer of a CNN to train on the CIFAR-10 dataset to perform image classification. Results show that the ICA initialization for a custom made CNN …

This study explored issues related to the adoption and implementation of electronic health record (EHR) systems including building the awareness, knowledge, and experience of physicians toward cyber hygiene. This study used a qualitative research method to assess (a) the barriers to EHR systems adoption and implementation in Kuwait and (b) the level of awareness, knowledge and experiences related to cyber hygiene practices in Kuwait. The findings of the study supported the conceptual framework used to guide the research of the factors impacting the adoption and implementation of EHR systems in Kuwait as well as explore the level of awareness, knowledge, and experience of physicians about both EHR systems and cyber hygiene. The results from the systematic literature review analysis identified seven major barriers. These are financial barriers, time, difficulty of using technology, lack of support, negative attitude, legal and ethical (policies), and cultural barriers. The findings from the semistructured interviews supported the literature findings and provided more in-depth insights into the structural and social issues affecting the adoption and implementation of EHR systems. Given that Kuwait is a member of the Gulf Cooperation Countries (GCC), the results from the literature analysis showed that the problems in Kuwait are similar to …

This study aims to apply user reviews and numerical ratings toward items to create an aspect-aware high-order representation for a recommendation system. We propose a novel aspect-aware knowledge graph recommendation model (AKGR) with the deep learning method to predict users' ratings on non-interacted items, from which more personalized recommendations can be made. First, we create a sequence-to-sequence encoder and decoder model by exploiting contextual and syntactic information in user reviews to extract aspects critical to items. Then we utilize the principal component analysis (PCA) and the K-means clustering to analyze the extracted aspects for category classification. Based on the aspects, we construct a graph structure to connect users and items which share the same aspect-based opinions for mining user preferences and item attributes. Finally, we combine the user and item latent features from the reviews and the user-item rating matrix to complete the rating prediction task by applying the factorization machine model. We conducted experiments on three aspect extraction datasets and five rating prediction datasets. To verify the effectiveness of the proposed aspect extraction model and rating prediction model, comparison experiments were made with some state-of-the-art baseline models, such as double embeddings convolutional neural network (DE-CNN) and dual graph convolutional …

The purpose of this study was to assess visual analytics technology acceptance and user experience among in vitro fertilization (IVF) consumers. The research aimed to show how visual analytics tools and technologies can be applied in the consumer space to enhance how users interpret healthcare success rate data. This exploratory user evaluation study utilized a quantitative dominant, mixed-methods approach with a convergent parallel design based on the data-validation variant. Survey data were collected from consumers who were currently seeking information about IVF treatment in the United States. The study findings indicated that the extended unified theory of acceptance and use of technology (UTAUT2) constructs of performance expectancy and hedonic motivation influenced consumer behavioral intention to use visual analytics technology, while effort expectancy did not. Further, the findings from the user experience and qualitative analyses indicated that there is strong support for consumer adoption of visual analytics technology for personal healthcare decision-making. These findings may help in the design and development of modern, interactive visualization tools that could be used to visualize public or private healthcare data for analysis by consumers. Stakeholders, including the US Centers for Disease Control and Prevention, the World Health Organization, and medical practitioners, may use the …

In this study we explore the factors impacting physician patient interaction time and how these factors can be used to improve health knowledge transfer from physicians to patients. We also investigate how technology tools can be used to improve this interaction time. Physician patient interaction time is important because this is the time when both sides engage with each other, exchange information so that physicians understand their patients' health issues. Given the increasing health care costs and demand for physician time, over the years, this interaction time has been distracted due to many factors. It is important to explore these factors and provide some possible solutions, such as advanced knowledge management systems, such as knowledge portals. To identify the factors impacting this interaction time and the role of technology, we first identified the variables and then developed hypotheses. We used data from the surveys administered by the AHRQ and NCHS to test these hypotheses. We conducted correlation analyses to determine the factors that can be used to improve health knowledge transfer from physicians to patients and how technology tools can be used to improve physician patient interaction time. Our analyses indicate that the factors we identified to improve health knowledge …

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 …

This dissertation addresses research questions related to defining user's trust and risk perceptions associated with social networking usage behavior in relation to the repeated privacy and security breaches. The general research question is explored in the dissertation via the conduct of three related studies. The finding from these three investigations are presented in the results section as 3 essays that collectively examine the social networking sites usage behavior. Essay 1 proposes a conceptual model based on the review of multiple breaches. The review provides a conceptual model which is further analyzed using a quantitative survey in the second essay. Essay 2 measures the trust and risk perceptions associated with different sources of information when presented with multiple breaches. This portion of the research used a quantitative method that included surveying of college students from University of North Texas (UNT) to understand the relation between user's trust and risk perceptions. Essay 3 examines the social networking usage behavior on account of repeated privacy and security breaches. This essay uses the insights from the other two essays to identify the usage behavior and how it is affected. The proposed model was tested using a survey questionnaire method. Results show a significant relationship …

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.

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 …

Life coaching is a rapidly expanding industry that focuses on client development, enhancement of life experience, and goal attainment often when clients are experiencing personal, professional, and social change. Online communities of practice (CoPs) provide opportunity for individuals to connect, share experiences, and learn from each other under the auspices of a unifying theme or subject. Since the 1990s, CoPs have spread from education to other areas of business and industry and continue to shape participant professional development. However, the everyday life information seeking and practices of life coaches remains unexplored within information science literature from the perspectives of life coach engagement in seeking information, life coach engagement in CoPs, and life coach interactions with other coaches. The purpose of this research study was to explore life coach perspectives of coaching, the diverse information needs of life coaches, the types and strength of relationships between life coaches and CoPs, the role of coaching certification and/or licensing as contributing to the professionalization of life coaching, and the means of communication exchange by life coaches through information communication technologies. This mixed method study focused on life coaches who self-identify as belonging to a CoP and those that do not. Theoretical frameworks for …