Multilevel analysis exploring the relative importance of workplace resources in explaining nurses' workload perceptions: Are we setting the right focus?. Issue 2 (8th April 2021) - Oppel, Eva-Maria; Mohr, David C.
Abstract : Background: Nurses' workload has become increasingly recognized as an important determinant of nurse turnover and shortage and has been also associated with poorer quality of care. Despite strong evidence that heavy workloads have negative consequences, we still lack a comprehensive understanding of the workplace characteristics that contribute most to improving nurses' workload, the relative importance of each in doing so, or indeed the workplace characteristics and other factors that drive nurses' perceptions of their workload. Purpose: The aim of this study was to examine workplace resources as antecedents of nurses' perceptions of their workload and to investigate their relative importance in explaining workload perceptions. We considered workplace resources related to staffing, professional relationships, and technology. Methodology: The study sample comprised nurse-reported and administrative data from U.S. Veterans Health Administration hospitals between 2014 and 2017. Our multilevel analyses are based on data from 20, 330 nurses working in 273 work groups at 123 hospitals. We developed and empirically tested a theoretical model using multilevel mixed-effects linear regression. The relative importance of workplace resources was assessed by dominance analysis. Results: Staffing levels, relational climate, and information technology were significantly associated with nurses' workload perceptions. Dominance analysis indicated that relational resources are the most important measure in explaining nurses' workload perceptions. Practice Implications: This is the first study to examine the relative importance of workplace resources in explaining nurses' perceptions of their workload. Our results suggest that much might be gained by investing in interventions to boost relational resources. In turn, these findings could lead to more targeted, effective, and resource efficient interventions to improve nurses' workload.