2.50
Hdl Handle:
http://hdl.handle.net/10755/147537
Type:
Presentation
Title:
Predicting Nursing Turnover with Catastrophe Theory
Abstract:
Predicting Nursing Turnover with Catastrophe Theory
Conference Sponsor:Sigma Theta Tau International
Conference Year:2007
Author:Wagner, Cheryl M., PhD, RN, MSN/MBA
P.I. Institution Name:Kaplan University
Title:Associate Professor of Nursing
[Symposium scientific presentation] Prevention of turnover in a rising and dire international and sustained nursing shortage is crucial in managing shortage sequela.  In order for administrators to intervene effectively, there is a need to target accurately the staff population at risk for turnover. Current forays into the realm of complexity sciences indicate that managerial decision-making may not be adequate in the existing healthcare systems environment. Nonlinear modeling methods offer promise for capturing more of the essence of human emotion and the impulsive aspect of nurses? affective responses to issues leading to turnover, because they account for varying and apparently insignificant changes that linear representations simply do not portray. The purpose of this study was to examine the relationships between nursing turnover behavior and three known predictors of nurse turnover behavior:  nurses' perceptions of anticipated turnover, organizational commitment and job-related tension, in order to compare the predictability of a cusp catastrophe model and a traditional linear model of nursing turnover behavior. The results of this descriptive, correlational survey with a longitudinal cohort prospective data collection plan demonstrated that a highly predictive cusp catastrophe model could be generated from the convenience sample population involved (N = 1033; return rate 77.2%  T1, 64.9% T2), with 80.4% correct predictions overall and 53.6% correct predictions of the actual terminations. An examination of a dynamic cusp model demonstrated that movements of participants across the bifurcation plane in the cusp model were indicative of retention or termination.  However, the unique characteristics of the sample may have impeded the ability to design a more predictive model; thus, more research in this area is indicated.  Nursing research, nursing administration and nursing practice should act on this evidence to benefit future studies and the profession of nursing. Research Supported by a grant from NIH and NRSA, 5 F31 NR008461-02, 2003-2007
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titlePredicting Nursing Turnover with Catastrophe Theoryen_GB
dc.identifier.urihttp://hdl.handle.net/10755/147537-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Predicting Nursing Turnover with Catastrophe Theory</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2007</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Wagner, Cheryl M., PhD, RN, MSN/MBA</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">Kaplan University</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Associate Professor of Nursing</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">c.wagner@mchsi.com</td></tr><tr><td colspan="2" class="item-abstract">[Symposium scientific presentation] Prevention of turnover in a rising and dire international and sustained nursing shortage is crucial in managing shortage sequela.&nbsp; In order for administrators to intervene effectively, there is a need to target accurately the staff population at risk for turnover. Current forays into the realm of complexity sciences indicate that managerial decision-making may not be adequate in the existing healthcare systems environment.&nbsp;Nonlinear modeling methods offer promise for capturing more of the essence of human emotion and the impulsive aspect of nurses? affective responses to issues leading to turnover, because they account for varying and apparently insignificant changes that linear representations simply do not portray.&nbsp;The purpose of this study was to examine the relationships between nursing turnover behavior and three known predictors of nurse turnover behavior:&nbsp; nurses' perceptions of anticipated turnover, organizational commitment and job-related tension, in order to compare the predictability of a cusp catastrophe model and a traditional linear model of nursing turnover behavior.&nbsp;The results of this descriptive, correlational survey with a longitudinal cohort prospective data collection plan demonstrated that a highly predictive cusp catastrophe model could be generated from the convenience sample population involved (N = 1033; return rate 77.2%&nbsp; T1, 64.9% T2), with 80.4% correct predictions overall and 53.6% correct predictions of the actual terminations. An examination of a dynamic cusp model demonstrated that movements of participants across the bifurcation plane in the cusp model were indicative of retention or termination.&nbsp; However, the unique characteristics of the sample may have impeded the ability to design a more predictive model; thus, more research in this area is indicated.&nbsp; Nursing research, nursing administration and nursing practice should act on this evidence to benefit future studies and the profession of nursing.&nbsp;Research Supported by a grant from NIH and NRSA, 5 F31 NR008461-02, 2003-2007</td></tr></table>en_GB
dc.date.available2011-10-26T09:33:26Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T09:33:26Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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