{"title":"中国急诊科护士付出-回报失衡:Nomogram预测模型的构建与评价","authors":"Luying Zhong, Ling Wang, Hao Zhang, Dongmei Diao, Xiaoli Chen, Liqun Zou","doi":"10.1155/jonm/1412700","DOIUrl":null,"url":null,"abstract":"<div>\n <p><b>Background:</b> 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.</p>\n <p><b>Objectives:</b> 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.</p>\n <p><b>Design:</b> A descriptive cross-sectional survey design was employed.</p>\n <p><b>Methods:</b> 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.</p>\n <p><b>Results:</b> 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.</p>\n <p><b>Conclusions:</b> 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.</p>\n <p><b>Implications for Nursing Management:</b> 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.</p>\n </div>","PeriodicalId":49297,"journal":{"name":"Journal of Nursing Management","volume":"2025 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/jonm/1412700","citationCount":"0","resultStr":"{\"title\":\"Effort–Reward Imbalance Among Emergency Department Nurses in China: Construction and Evaluation of a Nomogram Predictive Model\",\"authors\":\"Luying Zhong, Ling Wang, Hao Zhang, Dongmei Diao, Xiaoli Chen, Liqun Zou\",\"doi\":\"10.1155/jonm/1412700\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p><b>Background:</b> 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.</p>\\n <p><b>Objectives:</b> 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.</p>\\n <p><b>Design:</b> A descriptive cross-sectional survey design was employed.</p>\\n <p><b>Methods:</b> 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.</p>\\n <p><b>Results:</b> 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.</p>\\n <p><b>Conclusions:</b> 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.</p>\\n <p><b>Implications for Nursing Management:</b> Healthcare administrators can use our model to identify nurses at high risk of ERI. 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引用次数: 0
摘要
背景:急诊科护士面临着严重的职业压力。努力-回报失衡(ERI)已被证明是一个重要的社会心理压力源,与不良健康后果密切相关。目的:本研究的主要目的是建立并严格评估急诊科护士ERI的预测模型。该模型旨在精确识别高危人群,为制定有针对性的干预策略提供重要参考。设计:采用描述性横断面调查设计。方法:以全国30家三级医院的1540名注册护士为研究对象。通过匿名在线问卷收集被调查者的人口学特征、ERI中文版问卷的回答以及中国护理工作环境(C-NWE)量表的回答。我们使用多元逻辑回归来建立我们的预测模型。随后,绘制了一个nomogram来简化模型,并利用曲线下面积(area under the curve, AUC)和bootstrap重采样对其性能进行了综合评价。结果:急诊科护士ERI患病率为26.2%。过度工作和每周工作时间(≥59小时)被确定为ERI的独立预测因子。模型的AUC达到0.891,具有较强的判别能力。结论:我们构建了一个精确的预测模型,准确量化了急诊科护士加班和每周工作时间(≥59小时)对ERI风险的贡献。这些发现对于在高压力护理环境中早期识别和有效预防ERI具有重要意义。对护理管理的启示:医疗保健管理人员可以使用我们的模型来识别ERI高风险的护士。通过采取措施解决过度承诺和管理工作时间,他们可以减轻ERI的负面影响,从而改善急诊科护士的健康状况并提高护理质量。
Effort–Reward Imbalance Among Emergency Department Nurses in China: Construction and Evaluation of a Nomogram Predictive Model
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.
期刊介绍:
The Journal of Nursing Management is an international forum which informs and advances the discipline of nursing management and leadership. The Journal encourages scholarly debate and critical analysis resulting in a rich source of evidence which underpins and illuminates the practice of management, innovation and leadership in nursing and health care. It publishes current issues and developments in practice in the form of research papers, in-depth commentaries and analyses.
The complex and rapidly changing nature of global health care is constantly generating new challenges and questions. The Journal of Nursing Management welcomes papers from researchers, academics, practitioners, managers, and policy makers from a range of countries and backgrounds which examine these issues and contribute to the body of knowledge in international nursing management and leadership worldwide.
The Journal of Nursing Management aims to:
-Inform practitioners and researchers in nursing management and leadership
-Explore and debate current issues in nursing management and leadership
-Assess the evidence for current practice
-Develop best practice in nursing management and leadership
-Examine the impact of policy developments
-Address issues in governance, quality and safety