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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Effort-Reward Imbalance Among Emergency Department Nurses in China: Construction and Evaluation of a Nomogram
Luying Zhong1,2,3, Ling Wang1,2,3, Hao Zhang1,2,3
1Department of Emergency Medicine, West China Hospital, Sichuan University/West China School of Nursing, Chengdu, China.
Abstract:
Background: Emergency department nurses face severe occupational stress. Effort-reward imbalance (ERI) has been shown to be a significant psychosocial stressor closely linked to adverse health consequences. Objectives: The primary objective of this study was to construct and rigorously evaluate a predictive model for ERI in emergency department nurses. The model is intended to precisely identify high-risk populations and provide a crucial reference in the formulation of targeted intervention strategies. Design: A descriptive cross-sectional survey design was employed. Methods: The study sample comprised 1540 registered nurses from 30 tertiary hospitals in China. The demographic characteristics of the respondents, their responses to the Chinese version of the ERI questionnaire, and their responses to the Chinese Nursing Work Environment (C-NWE) scale were collected via an anonymous online questionnaire. We used multiple logistic regression to develop our predictive model. Subsequently, a nomogram was plotted to simplify the model, and its performance was comprehensively evaluated using the area under the curve (AUC) and bootstrap resampling. Results: The prevalence of ERI among emergency department nurses was determined to be 26.2%. Overcommitment and weekly work hours (≥ 59 h) were identified as independent predictors of ERI. The AUC of the model reached 0.891, demonstrating robust discriminatory power. Conclusions: We constructed a precise predictive model that accurately quantifies the contributions of overcommitment and weekly work hours (≥ 59 h) to the risk of ERI among emergency department nurses. These findings have significant implications for the early identification and effective prevention of ERI in high-stress nursing environments. Implications for Nursing Management: Healthcare administrators can use our model to identify nurses at high risk of ERI. By taking steps to address overcommitment and manage work hours, they can mitigate the negative impact of ERI, thereby improving the health of emergency department nurses and enhancing the quality of care.

