Department of Nursing Innovation and Development, Campus Bio-Medico of Rome University Hospital, Rome, Italy.
Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
J Nurs Manag. 2022 Mar;30(2):473-481. doi: 10.1111/jonm.13523. Epub 2021 Dec 12.
To explore predictors of perceived nursing workload in relation to patients, nurses and workflow.
Nursing workload is important to health care organisations. It determines nurses' well-being and quality of care. Nevertheless, its predictors are barely studied.
A cross-sectional prospective design based on the complex adaptive systems theory was used. An online survey asked nurses to describe perceived workload at the end of every shift. Data were gathered from five medical-surgical wards over three consecutive weeks. We received 205 completed surveys and tested multivariate regression models.
Patient acuity, staffing resources, patient transfers, documentation, patient isolation, unscheduled activities and patient specialties were significant in predicting perceived workload. Nurse-to-patient ratio proved not to be a predictor of workload.
This study significantly contributed to literature by identifying some workload predictors. Complexity of patient care, staffing adequacy and some workflow aspects were prominent in determining the shift workload among nurses.
Our findings provide valuable information for top and middle hospital management, as well as for policymakers. Identification of predictors and measurement of workload are essential for optimizing staff resources, workflow processes and work environment. Future research should focus on the appraisal of more determinants.
探讨与患者、护士和工作流程有关的感知护理工作量的预测因素。
护理工作量对医疗保健组织很重要。它决定了护士的幸福感和护理质量。然而,其预测因素几乎没有研究过。
基于复杂适应系统理论的横断面前瞻性设计。在线调查要求护士在每次轮班结束时描述他们的工作负荷。在连续三周内,从五个内科和外科病房收集数据。我们收到了 205 份完整的调查问卷,并测试了多元回归模型。
患者病情严重程度、人员配备资源、患者转科、记录、患者隔离、非计划性活动和患者专业是预测感知工作量的重要因素。护士与患者的比例证明不是工作量的预测因素。
本研究通过确定一些工作量预测因素,为文献做出了重要贡献。患者护理的复杂性、人员配备的充足性以及一些工作流程方面在确定护士轮班工作量方面起着重要作用。
我们的研究结果为医院高层和中层管理人员以及政策制定者提供了有价值的信息。确定预测因素和测量工作量对于优化人员配备资源、工作流程和工作环境至关重要。未来的研究应集中在评估更多的决定因素上。