Construction and evaluation of a predictive model for compassion fatigue among emergency department nurses: A cross-sectional study

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BackgroundCompassion fatigue is a syndrome resulting from long-term work-related traumatic event stress exposure of medical staff. The emergency department is considered to be a high-risk, high-intensity and high-stress work environment, with a high prevalence of trauma and violence. Nurses in the emergency department are more prone to compassion fatigue than nurses in other departments. Compassion fatigue not only affects the physical and mental health, and job satisfaction of emergency department nurses, but also causes serious consequences for patients, such as poor patient outcome, medical errors, and increased patient mortality during hospitalization. ObjectivesOur study aims to develop and evaluate a predictive model for compassion fatigue among emergency department nurses. DesignA cross-sectional study. Data sourcesThe emergency department nurses (N = 1014) were recruited from 21 tertiary hospitals (from Chengdu, Chongqing, Guiyang, Guangzhou and Shanghai) in central, southwestern, southern, and eastern China from July 25, 2022 to October 30, 2022. MethodsUnivariate and multiple logistic regression analyses were used to determine the potential predictive factors associated with compassion fatigue in emergency department nurses. A nomogram was built based on the predictive factors and internally evaluated using a bootstrap resampling method (1000 bootstrap resamples). The performance of the predictive model was evaluated by measuring the Hosmer–Lemeshow goodness of fit test and calibration curve. ResultsThe prevalence of compassion fatigue among emergency department nurses was 75.9 %. The multiple logistic regression analysis revealed that the independent predictive factors for compassion fatigue among emergency department nurses were working position, job satisfaction, diet habit, sleep hours per day, occupational stress, physical harassment and the level of workplace violence, all of which were identified to create the nomogram. The Hosmer–Lemeshow goodness of fit test indicated that the predictive model was well calibrated (χ2 = 11.520, P = 0.174). The bootstrap-corrected concordance index of nomogram was 0.821 (95 % CI: 0.791–0.851). The calibration curve of the nomogram showed good consistency between the predicted and actual probabilities. ConclusionsA predictive model of compassion fatigue among emergency department nurses has been developed, based on the general demographic, work-related and lifestyle characteristics, occupational stress, and workplace violence, with satisfactory predictive ability. This model can identify emergency department nurses who are at high risk of compassion fatigue. Our study provides an empirical basis for early detection, early diagnosis and early intervention of emergency department nurses at high risk of compassion fatigue.