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Measuring workload of nurses on a neurosurgical care unit
John Nelson1, Linda Valentino, Laura Iacono
1Questions or comments about this article may be directed to John Nelson, PhD, at jn@hcenvironment.com. He is the President, Healthcare Environment, Inc., New Brighton, MN. Linda Valentino, MSN RN, is the Executive Vice President, Visiting Nurse Service of New York, New York, NY. Laura Iacono, MSN RN, is the Nurse Manager Neurosurgical Intensive Care Unit, Department of Nursing, North Shore University Hospital, Manhasset, NY. Peter Ropollo, BA DC BSN MPA, is a Nursing Administrator, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY. Natalia Cineas, MS RN, is the Senior Director of Nursing, Mount Sinai St. Luke's Hospital and Mount Sinai Roosevelt, New York, NY. Stephanie Stuart, BSN RN, is a Staff Nurse, Neurosurgery Care Unit, New York Presbyterian Hospital, Columbia Medical Center, New York, NY.
Aim Of The Study:
The aim of this study was to create a model of workload that could be used to manage workload and increase satisfaction of workload for nurses on a neuroscience care unit.
Background:
No study was found that delineated a model of workload that could be used to manage or improve satisfaction with workload for a neuroscience care unit at either the individual nurse or unit level.
Methods:
Staff, management, and a researcher collaboratively developed a model to examine workload on a neuroscience care unit. Forty-three independent variables of workload and the dependent variable of satisfaction with workload were studied over 28 days using stepwise regression. Stepwise regression is appropriate for model building. Criteria to enter any independent variable into a regression equation included correlating with the dependent variable of satisfaction with workload, validation of central tendency assumptions, and good data fit using residual diagnostics.
Results:
Independent variables of workload that explained the variance of satisfaction with workload included time (15.9%), undelegated work (4.0%), number of isolation patients (2.9%), individual employees (2.1%), number of patients (1.3%), and number of postoperative neurosurgical patients (1.1%). On the unit level, satisfaction with workload was predicted by time (42.5%) and the number of nurses on duty (7.7%).
Conclusions:
Satisfaction with workload as reported by staff nurses is predicted by both individual- and unit-level factors of workload. Staff input is crucial to the development of a model of workload on clinical specialty units like neuroscience care. Staff nurses identify key variables, otherwise overlooked, affecting workload and satisfaction and satisfaction with workload.
Implications For Nursing Management:
It is vital to develop unit-specific models of workload and consider both individual- and unit-level factors. Such models have potential for deeper research into both management and increasing satisfaction of workload at the level of clinical specialty/unit.
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