Brzozowski Sarah L, Cho Hyeonmi, Arsenault Knudsen Élise N, Steege Linsey M
School of Nursing, University of Wisconsin - Madison, 701 Highland Avenue, Madison, WI, 53705, USA.
Appl Ergon. 2021 Apr;92:103337. doi: 10.1016/j.apergo.2020.103337. Epub 2020 Nov 29.
Fatigue arising from excessive work demands is a known safety challenge in hospital nurses. This study aimed to determine which measures of work demands during nursing work are most predictive of hospital nurse fatigue levels at the end of the work shift. Measures of work demands of registered nurses from two hospital units in the United States were collected from organizational data sources, wearable sensors, and questionnaires. Fatigue levels were measured at the start and end of each shift using the Brief Fatigue Inventory. Multilevel linear regression analysis was used to predict end of shift fatigue based on work demand variables. The best fit model included multiple variables from organizational data sources and a physical activity variable measured by a wearable sensor. Organizational data can be used to create dynamic measures of work demands as they occur and predict end of shift fatigue levels in hospital nurses.
因工作要求过高而产生的疲劳是医院护士面临的一个已知安全挑战。本研究旨在确定护理工作期间哪些工作要求指标最能预测工作班次结束时医院护士的疲劳程度。从组织数据源、可穿戴传感器和问卷中收集了美国两个医院科室注册护士的工作要求指标。使用简明疲劳量表在每个班次开始和结束时测量疲劳程度。采用多水平线性回归分析，根据工作需求变量预测班次结束时的疲劳程度。最佳拟合模型包括来自组织数据源的多个变量和一个由可穿戴传感器测量的身体活动变量。组织数据可用于在工作需求出现时创建动态指标，并预测医院护士班次结束时的疲劳程度。
