1. Wagner, Cheryl M. MSN/MBA, RN, OCN
  2. Huber, Diane L. PhD, RN, FAAN, CNAA


Creating a culture of retention is one antidote to high costs of nurse turnover. However, nurse turnover behavior has proven to be largely uncertain and unpredictable. A new approach to analyzing nurse turnover attitudes and behavior is discussed. A cusp catastrophe nonlinear model of nurse turnover is presented as having usefulness for the prediction of turnover for managerial decision making in nursing care delivery systems. Viewing nursing from the perspective of nonlinear dynamics can create new strategies for effective and efficient nursing services.


Nurse turnover exemplifies an undesirable outcome that results from uncertain and unpredictable human behavior. Historically, the design of nursing turnover models has incorporated linear assumptions that turnover behavior is a stepwise and sequential building of attitudes and intentions that leads to a higher level of withdrawal, ultimately reaching a climactic point at which turnover occurs. These models have demonstrated low predictive ability even though they are complex and capture many factors that should explain more than they do. To better understand and predict nursing turnover, a radical new examination is needed. Viewing nursing and nursing care delivery from the perspective of nonlinear dynamics such as chaos and catastrophe theories offers promise for greater precision in prediction, leading to more effective managerial decision making about how to create a culture of retention.


Quantum and chaos theories postulate that the world is fairly unpredictable. Disorganized yet curiously organized patterns occurring in nature, such as in snowflake formation, may be the natural patterns of the universe, as opposed to linear models. When disorganized pattern models are applied in certain situations, such as to human behavior in volatile circumstances, predictive ability can be dramatically increased. McDaniel 1 suggested that the current uncertainty in health care is an outcome of the chaotic and fragile nature of the human condition, resulting in a need to embrace this unpredictability and exploit associated diversity for strategic advantage.


It is possible that linear representations simply do not capture the essence of human emotion and the impulsive aspects of nurses' affective responses to issues leading to turnover. This raises the question of whether nonlinear assumptions and techniques would better explain nursing turnover. Nonlinear modeling methods have become more popular because they allow for the analysis of dynamic and discontinuous changes in behavior. Moreover, recent evidence has shown that nursing theories and nursing research benefit from the use of nonlinear dynamics. 2-4 The purpose of this article is to discuss linear and nonlinear conceptual models of nurse turnover and to highlight a model that has usefulness for prediction of turnover for managerial decision making in nursing care delivery.