Grants and Funded Projects

Using Nonlinear Models To Predict Nursing Turnover
(National Institute of Nursing Research – F31 NR008461)
Principal Investigator: Cheryl Wagner, MSN, MBA, RN

This innovative study will answer the question: “Can catastrophe theory contribute to our understanding of nursing turnover?” The study proposes the application of nonlinear principlies to the organization and evaluation of nursing care delivery. It emphasizes a mathematical analysis of the dynamic changes occurring during a turn of events, which has proven useful in previous non-nursing research to gain a better understanding of these dynamic changes, such as job absence, turnover, and job performance. Secondly, it proposes comparing a non-linear dynamic process model (catastrophe theory) to a current linear model in order to circumvent some of the shortcomings associated with the use of linear models and ascertain whether nonlinear models have better predictive ability in nursing turnover. The study consists of a mixed-method analysis making use of quantitative data related to nursing turnover, and a qualitative narrative examination of nurses’ turnover stories. The research will address the following specific aims:

• To determine the personal characteristics of participating nurses related to turnover;
• To determine whether the cusp catastrophe nonlinear model is a better quantitative predictor of nursing turnover than a linear model;
• To examine via a narrative analysis whether nurses’ stories of leaving depict the process they went through as linear or nonlinear in nature.