ABSTRACT
Background: The COVID-19 pandemic has amplified long-standing issues of burnout and stress among the U.S. nursing workforce, renewing concerns of projected staffing shortages. Understanding how these issues affect nurses' intent to leave the profession is critical to accurate workforce modeling. Purpose: To identify the personal and professional characteristics of nurses experiencing heightened workplace burnout and stress. Methods: We used a subset of data from the 2022 National Nursing Workforce Survey for analysis. Binary logistic regression models and natural language processing were used to determine the significance of observed trends. Results: Data from a total of 29,472 registered nurses (including advanced practice registered nurses) and 24,061 licensed practical nurses/licensed vocational nurses across 45 states were included in this analysis. More than half of the sample (62%) reported an increase in their workload during the COVID-19 pandemic. Similarly high proportions reported feeling emotionally drained (50.8%), used up (56.4%), fatigued (49.7%), burned out (45.1%), or at the end of their rope (29.4%) "a few times a week" or "every day." These issues were most pronounced among nurses with 10 or fewer years of experience, driving an overall 3.3% decline in the U.S. nursing workforce during the past 2 years. Conclusion: High workloads and unprecedented levels of burnout during the COVID-19 pandemic have stressed the U.S. nursing workforce, particularly younger, less experienced RNs. These factors have already resulted in high levels of turnover with the potential for further declines. Coupled with disruptions to prelicensure nursing education and comparable declines among nursing support staff, this report calls for significant policy interventions to foster a more resilient and safe U.S. nursing workforce moving forward.
ABSTRACT
Workforce burnout is an increasing problem across many industries and professions, with significant impacts on both manufacturing and service sectors. For example, burnout is a major problem for more than 80% of healthcare systems, with the costs of replacing doctors who leave their job and reduced clinical hours related to burnout are estimated at $4.6 billion annually. Productivity is reduced, mental health is affected, family relations are weakened, and a solution to all of this is not clear. More than 10 years ago, the global cost of burnout was estimated to exceed $300 billion annually. Major societal events, such as the recent ongoing COVID-19 pandemic or political unrest, can further exacerbate individual burnout and its impacts. Advancements in burnout research over the past two decades have mostly been to develop methods for measuring and classifying burnout and in lessons learned empirically from various intervention implementations, but with little-to-no analytic modeling research to help inform effective policies. In a recent report in fact, the National Academy of Medicine emphasized the need to develop analytic models that better quantify the extent of the problem in a way that translates into actionable results in addition to approaches for understanding the impact of interventions. The overall aim of our proposed research accordingly is to develop and apply analytic disease progression models to help understand burnout dynamics and evaluate the long-term benefits of interventions prior to wide-scale implementation testing. The proposed dissertation includes three fundamental contributions. First, we develop and introduce two disease progression models of individual and organizational burnout based on Markov chains, parameterized and linked from limited data via optimization and simulation models. We also illustrate the use of the developed models to estimate and compare the relative effectiveness of various strategies and interventions to reduce burnout, with a focus on estimating long-term impacts from limited early testing data, contributing to pre-randomized trial methods. Second, we leverage the models to estimate the effect of COVID-19 on two healthcare professional populations in two case studies. Finally, we propose several potential methodological extensions to disease progression modeling including investigating the effect of higher order nesting, bootstrapping and time non homogeneity. Results indicate that the disease progression models of the proposed type can accurately model individual and institutional burnout progression to help better understand the dynamics of burnout and analyze the effectiveness of potential interventions to make more informed decisions. Sensitivity analysis investigates the impact of data limitations on model accuracy, while sampling provides limits for model results. Model extensions provide empirical approach to the time non-homogenous problem which if approached mathematically requires extensive longitudinal data. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
The DAISY Foundation expresses gratitude to nurses with programs that recognize them for the extraordinary skillful and compassionate care they provide patients and families. Meaningful recognition of extraordinary compassionate care is a way to protect the invaluable nursing workforce and the quality of care that connects us worldwide, supporting nurses as they address global challenges with resiliency and strength.