Using the format of a research seminar highlighting research “challenges” of the DNSc faculty , this course is designed to strengthen the student’s ability to integrate and synthesize knowledge in statistics and nursing research methodologies, and to apply this integrated knowledge to common problems in study design and data analysis.
The student works with a faculty member or other scientist who is conducting a research project. The specific nature of the experience depends on the nature and stage of the research, but might include search and review of relevant literature, data collection, data analysis and/or grant preparation.
The student works with a faculty member or other scientist who is conducting a research project. The specific nature of the experience depends on the nature and stage of the research, but might include search and review of relevant literature, data collection, data analysis and/or grant preparation.
The course is intended to provide a hands‐on introduction to delivering data visualizations to serve as a critical lens through which individual and population level health can be examined. The course will combine concepts and theory in data visualization and exploration and practice to prepare the learner to begin using graphics and statistics to explore data, find and construct a narrative, and share findings in ways colleagues and decision-makers can readily understand and act upon. Topics may include: (a) principles of human perception and attention that inform visualization design; (b) The use of visualization to explore data and discover a narrative; (c) the use of visualization to communicate effectively with others; 4) the development of practical skills, including preparing datasets and applying programing language to analyze data and produce visualizations.
Interaction with practitioners/guest speakers is an integral part of this course as a way for students to understand real data and information challenges. Concept lectures and case studies concentrate on learning to scope and manage complex data science projects. Lab work will focus on gaining competency with data science/visualization tools and techniques (e.g., Jupyter Notebook, R programming language) applied to an integrated health-relevant data set.
The DNP student will provide comprehensive, evidence-based care to patients and promote optimal patient outcomes. The DNP student will demonstrate integration of comprehensive assessment, advanced differential diagnosis, therapeutic intervention, synthesis of evidence-based practice while managing patients in a clinical setting.
The DNP Intensive Portfolio Advisement is designed to provide DNP students with guidance to demonstrate achievement of intended program outcomes and advanced practice competencies through written case narratives from clinical based encounters and oral presentation. The student will be assigned an advisor who will review all case narrative work and provide guidance as indicated.
The DNP portfolio is designed to assist students in developing written comprehensive patient-focused clinical encounters. Students are expected to integrate best available evidence when developing a plan of care and demonstrate critical reflection through narrative writing
The DNP portfolio is designed to assist students in meeting CUSON DNP competencies as demonstrated in written case narrative and competency based clinical encounters. Students will be assigned a faculty member who will provide guidance in identifying appropriate patient encounters, reviewing and editing all written work associated with demonstrating competency-based learning. This course repeats sequentially for 3 semesters.
This course is intended for PhD students who are engaged in relevant scholarly activities that are associated with dissertation research.
The course is intended for PhD students who are engaged in relevant scholarly activities that are not associated with the required course sequence. Such activities must accrue more than 20 hours/week.