The National University System Repository exists to increase public access to research and other materials created by students and faculty of the affiliate institutions of National University System. Most items in the repository are open access, freely available to everyone.
Recent Submissions
Item Experiences of Stress by Nursing Students During Clinical Education: A Qualitative Descriptive, Phenomenological Study(2026-04)Nursing students report stress during clinical education hours, which may lead to withdrawal from nursing school and limit the number of graduating nurses, further depleting the workforce. This study addressed the problem of stress among nursing students during clinical education hours. The purpose of this qualitative, descriptive, phenomenological research study was to explore the lived experiences of stress among community college nursing students during clinical education hours and to identify programmatic supports that help alleviate these feelings. Guided by Neuman’s Systems Model (NSM), which describes the impact of unmanaged stress on groups and individuals, it is critical to understand the impact of stress experiences of nursing students during clinical education hours. Data were collected from five participants using semi-structured interviews. Participants identified that the traditional components of a nursing program, including caring for complex patients and clinical assignments, were sources of stress. The participants identified the clinical instructor as a stress reducer. Despite these stressful experiences, the participants remained dedicated to completing nursing school and beginning a career as a nurse. Findings were linked to academic, theoretical, and professional implications Recommendations for nursing program administrators include: (1) Close monitoring of students by clinical instructors, (2) Support synergy for lectures, lab, and clinical assignments, (3) Increase support for clinical instructors, (4) Encourage students to join a professional organization (5) Review collegewide services available to nursing students (6) Integrate stress management techniques and (7) Integrate Emotional Intelligence into the nursing program. Future research includes repeating this type of study using a quantitative research design or a sample population of baccalaureate nursing students.Item AI-Driven DRG Validation in Healthcare RCM: Challenges, Solutions, and the Path Forward(2026-05)Artificial intelligence-enabled natural language processing tools are increasingly used in diagnosis-related group validation workflows to improve coding accuracy and reduce revenue leakage. However, there is no clear evidence of the types of errors these systems generate and the organizational factors that influence their performance. The purpose of this applied doctoral project was to understand the nature, frequency, and operational impact of errors generated by artificial intelligence-driven diagnosis-related group validation tools and how human and organizational factors influence their identification and management. The project was guided by the Human Artificial Intelligence Integration Framework. A qualitative descriptive design was employed, combining structured artifact analysis with thematic analysis of stakeholder interviews. A structured audit of 40 synthetic inpatient cases was conducted to assess differences between artificial intelligence-generated recommendations and manual coding validation. Root cause analysis was performed for different types of errors, and the operational and financial impacts were assessed. Ten semi-structured interviews were conducted with stakeholders in coding, clinical documentation improvement, auditing, and revenue cycle leadership. The audit results showed that discrepancies occurred most often in clinically complex cases, particularly respiratory and sepsis-related cases. False positives were detected in 50% of cases and were the most common discrepancy types. Thematic analysis highlighted common elements: the need for human validation, limitations in contextual interpretation, documentation inconsistencies, workflow burden, and the importance of feedback loops. Artificial intelligence tools can help with case identification, but human review is needed to ensure correctness and compliance. We suggest enhancing human validation processes, standardizing the documentation, making the AI logic transparent, and developing feedback loops. As such, we provide practical guidance for integrating artificial intelligence into revenue cycle workflows whilst maintaining coding accuracy, operational efficiency, and compliance.Item Quantitative Exploration of AI's Impact on Financial Cybersecurity: Trends, Data Privacy, and Human Expertise(2026-03)The integration of artificial intelligence (AI) into financial cybersecurity has introduced both significant opportunities and complex challenges, particularly in relation to regulatory compliance, data privacy, and human–AI collaboration. This quantitative, non-experimental correlational study examined the extent to which key innovation attributes, compatibility, complexity, and relative advantage, impact the adoption and effectiveness of AI-driven cybersecurity technologies within U.S. financial institutions. Grounded in the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory, the study explored relationships between these independent variables and institutional outcomes, including system performance, adaptability, human–AI collaboration, and regulatory compliance. Data were collected from 90 cybersecurity professionals, IT managers, and compliance officers using a structured Likert-scale survey administered via Qualtrics. Statistical analysis was conducted using exploratory factor analysis (EFA) and multivariate analysis of variance (MANOVA), with Pillai’s Trace employed due to assumption violations. Results indicated that relative advantage had a statistically significant impact on institutional outcomes, while compatibility and complexity did not demonstrate significant independent effects. However, the combined model of all three variables produced a significant multivariate effect, highlighting the importance of integrated adoption strategies. The findings contribute to existing literature by emphasizing the critical role of perceived value and holistic implementation approaches in AI adoption within highly regulated financial environments. Practical implications include the need for financial institutions to prioritize strategic alignment, workforce integration, and governance frameworks when deploying AI-driven cybersecurity solutions.Item The Role of Quality Management in Enhancing Stakeholder Engagement and Operational Effectiveness in Nonprofit Organizations(2026-04)This study examined how irregularities in organizational processes within large United States–based health and human services nonprofit organizations, particularly those engaged in fundraising, influence stakeholder engagement and operational effectiveness. The problem addressed is that irregularities in governance, accountability, and transparency may undermine stakeholder trust and operational performance, affecting donors, organizational leaders, and the communities served by these organizations. The purpose of this qualitative descriptive study was to explore how these process irregularities shape stakeholder engagement and operational effectiveness in fundraising-driven nonprofit environments. The study was guided by a conceptual framework integrating quality management, stakeholder theory, and contingency theory to examine how governance credibility, operational reliability, and organizational readiness interact in practice. A qualitative descriptive design was used to capture real-world stakeholder experiences and organizational conditions. Data were collected through open-ended survey responses and archival analysis of publicly available organizational records. A purposive sample included internal stakeholders such as executives and fundraising leaders, and external stakeholders such as donors and volunteers. Data were analyzed using thematic analysis, with triangulation across survey and archival sources to strengthen credibility and interpretive alignment. Findings supported the three research questions. Research question one, irregularities in donor-facing communication and operational transparency were associated with reduced stakeholder trust and engagement, indicating that the reliability of routine processes strongly influences trust. Research question two: structured and incremental quality management practices were perceived as improving coordination, reducing rework, and strengthening operational predictability without requiring a comprehensive organization-wide implementation. For the third, implementation challenges were consistently linked to staffing constraints, workload imbalance, limited training capacity, and technology barriers, indicating that readiness and capacity alignment are central conditions for sustainable improvement. The study's findings suggested that stakeholder trust was associated with consistent operational performance, governance oversight, capacity-aligned quality practices, and readiness-based implementation. Recommendations emphasize strengthening operational transparency in donor-facing processes, embedding trust-related indicators into oversight routines, adopting incremental quality practices aligned with capacity, and conducting readiness assessments before primary process or technology changes. Future research should extend these findings through longitudinal and mixed-methods designs and comparative studies across nonprofit contexts.Item Multi-Modal Features of Trading Candle Chart Imagery & Volume For Predicting Financial Market Movements, Using the Proposed BLENNs Architecture.(2026-04)Financial forecasting models face significant challenges due to reliance on single-source data and lack of transparency required by regulators, impacting institutional investors, retail traders, and regulators who need reliable and interpretable AI systems for market decisions. This study developed and evaluated the Blended Neural Networks model, known as BLENNS, a hybrid deep learning framework integrating convolutional neural networks for pattern recognition, long short-term memory networks for sequential data, attention mechanisms for feature weighting, interpretability techniques, and probabilistic uncertainty estimation. Guided by multimodal learning, signal detection, and explainable AI theories, the study investigated whether multimodal fusion improves forecasting accuracy, if the Blended Filtered Candles preprocessing method enhances noise robustness, and whether interpretability aligns with expert trading rules. Using daily financial data from 2010 to 2025 on six diverse assets with over 21,000 observations, the Blended Filtered Candles method applied a three-stage filtering process including exponential smoothing, an enhanced candle transformation, and adaptive Kalman filtering. Walk-forward validation with multiple expanding windows ensured rigorous out-of-sample testing. BLENNs achieved 97.55% directional accuracy, a 113.77% improvement over traditional models, while BFC preprocessing improved the signal-to-noise ratio by 134.8%, outperforming common smoothing techniques by large margins. Interpretability analysis showed statistically significant, though modest, agreement with expert trading principles, emphasizing the value of explainable AI combined with human oversight. Simulated and live trading demonstrated strong returns and win rates, with live performance reflecting realistic trading costs and execution factors. This framework offers a foundation for meeting regulatory transparency requirements, though further compliance testing, extended live validation, scalability assessment, and transaction cost considerations are needed for practical deployment. The consistent noise reduction by BFC across multiple assets supports its broader application. Resources are available for further research exploring additional markets, higher-frequency data, institutional trading, adaptive parameter tuning, and establishing interpretability standards for regulation.
Communities in National University System Repository
Select a community to browse its collections.