Nursing (NU)

This course introduces learners to foundational concepts of online education and instructional design models. Qualities of effective use of technology and digital learning experiences to enhance teaching and learning remotely in nursing education are emphasized.

This course is designed to assist students in the completion of their project. Focus is placed on the business, policy, technology, and leadership implications of students’ projects and dissemination strategies. Students evaluate sustainability of the project, appropriateness of key indicators and adoption for larger scale implementation in clinical practice or health systems. Strategies and tools to help students proactively navigate their careers as clinical practice leaders are discussed. This course has practicum hours.

This course introduces the doctoral nurse anesthesia student to the principles of crisis management in anesthesia through a series of case studies and simulation modalities. Students explore dynamic clinical reasoning, human performance theory, and anesthesia crisis resource management (ACRM) principles. Crisis management and clinical correlation through simulation, clinical reasoning, and debriefing of complex crises occurring in the anesthesia environment. Students apply prior knowledge learned in the classroom and clinical arena, demonstrating how to minimize risk, create a crisis plan, manage, and recover from crisis events.

This course lays the groundwork for database design, data collection and data management. The approach focuses on identification, formalization, and verification of study data, and is appropriate for both straightforward and complex clinical research studies. Students will learn how to ask for information and organize it into well-defined data collection instruments. Students completing this course will develop a fundamental facility for data collection, data organization, and data analysis for research projects, including data cleaning, coding, determining shape of distribution and outliers, and handling missing data. Students will learn to use SPSS for database design and data analysis, and to use electronic data collection platforms such as Qualtrics, REDCap (Research Electronic Data Capture) and Microsoft Office Excel. Students will derive their familiarity with each application through a series of research project simulations.

In this course, students will survey selected determinants of health including social, cultural, biological, ecological, psychological factors, and structure and function of health services relative to access, acceptability, and affordability.

The focus of this course is to engage nursing PhD students in a team science experience within a multidisciplinary research team. Students will apply principles of research design and innovation to develop and implement a project that supports and/or extends the work of the research team.

This course provides advanced study in the student’s discipline-specific research interest leading to the completion of the dissertation proposal. The course will cover developing and identifying the key elements/ dimensions of the project and structuring that into a significance and background/literature review section clearly articulating the healthcare context. In addition, the course will help students develop a well-defined research question(s)/problem statement and specific aims for the proposed project. Students will also learn to identify the methodology or process plan that is in alignment with the research question(s)/problem statement. This will include the target population, recruitment approach, data collection, project implementation, analytic strategy, human subjects and/or other steps. Students will be expected to draw on all their coursework as they prepare a dissertation proposal.

During dissertation advisement, students work with their Dissertation chair and committee to complete the dissertation research, further develop, and defend the dissertation proposal, complete IRB submission and approval, and successfully defend the final dissertation. The course may be repeated up to 5 times. Additional repetitions must be approved by the Dissertation chair. Students must make satisfactory progress on the dissertation work to receive course credit. The course is graded on a pass/fail basis.

During dissertation advisement, students work with their Dissertation chair and committee to complete the dissertation research, further develop, and defend the dissertation proposal, complete IRB submission and approval, and successfully defend the final dissertation. The course may be repeated up to 5 times. Additional repetitions must be approved by the Dissertation chair. Students must make satisfactory progress on the dissertation work to receive course credit. The course is graded on a pass/fail basis.