Prevalence and correlates of stress and burnout among U.S. healthcare workers during the COVID-19 pandemic: A national cross-sectional survey study
Abstract
Background COVID-19 has put extraordinary stress on healthcare workers. Few studies have evaluated stress by worker role, or focused on experiences of women and people of color. Methods The “Coping with COVID” survey assessed US healthcare worker stress. A stress summary score (SSS) incorporated stress, fear of exposure, anxiety/depression and workload (Omega 0.78). Differences from mean were expressed as Cohen's d Effect Sizes (ESs). Regression analyses tested associations with stress and burnout. Findings Between May 28 and October 1, 2020, 20,947 healthcare workers responded from 42 organizations (median response rate 20%, Interquartile range 7% to 35%). Sixty one percent reported fear of exposure or transmission, 38% reported anxiety/depression, 43% suffered work overload, and 49% had burnout. Stress scores were highest among nursing assistants, medical assistants, and social workers (small to moderate ESs, p < 0.001), inpatient vs outpatient workers (small ES, p < 0.001), women vs men (small ES, p < 0.001), and in Black and Latinx workers vs Whites (small ESs, p < 0.001). Fear of exposure was prevalent among nursing assistants and Black and Latinx workers, while housekeepers and Black and Latinx workers most often experienced enhanced meaning and purpose. In multilevel models, odds of burnout were 40% lower in those feeling valued by their organizations (odds ratio 0.60, 95% CIs [0.58, 0.63], p< 0.001). Interpretation Stress is higher among nursing assistants, medical…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
Read the paper
Related studies
- eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses
- Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer
- OptiType: precision HLA typing from next-generation sequencing data
- Nicotinic Cholinergic System and COVID-19: In Silico Identification of an Interaction between SARS-CoV-2 and Nicotinic Receptors with Potential Therapeutic Targeting Implications
- Saccharomyces cerevisiae cells have three Omega class glutathione S-transferases acting as 1-Cys thiol transferases
- Hemolysis at low blood flow rates: in-vitro and in-silico evaluation of a centrifugal blood pump
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.