Gander Philippa, O'Keeffe Karyn, Santos-Fernandez Edgar, Huntington Annette, Walker Leonie, Willis Jinny
Sleep/Wake Research Centre, Massey University, Private Box 756, Wellington 6140, New Zealand.
Sleep/Wake Research Centre, Massey University, Private Box 756, Wellington 6140, New Zealand.
Int J Nurs Stud. 2020 Dec;112:103573. doi: 10.1016/j.ijnurstu.2020.103573. Epub 2020 Mar 14.
Multiple aspects of nurses' rosters interact to affect the quality of patient care they can provide and their own health, safety and wellbeing.
(1) Develop and test a matrix incorporating multiple aspects of rosters and recovery sleep that are individually associated with three fatigue-related outcomes - fatigue-related clinical errors, excessive sleepiness and sleepy driving; and (2) evaluate whether the matrix also predicts nurses' ratings of the effects of rosters on aspects of life outside work.
Develop and test the matrix using data from a national survey of nurses' fatigue and work patterns in six hospital-based practice areas with high fatigue risk.
Survey data included demographics, work patterns (previous 14 days), choice about shifts, and the extent to which work patterns cause problems with social life, home life, personal relationships, and other commitments (rated 1 = not at all to 5 = very much). Matrix variables were selected based on univariate associations with the fatigue-related outcomes, limits in the collective employment contract, and previous research. Each variable was categorised as lower (score 0), significant (score 1), or higher risk (score 2). Logistic multiple regression modelling tested the independent predictive power of matrix scores against models including all the (uncategorised) work pattern and recovery sleep variables with significant univariate associations with each outcome variable. Model fit was measured using Akaike and Bayesian Information Criterion statistics.
Data were included from 2358 nurses who averaged at least 30 h/week in the previous fortnight in one of the target practice areas. Final matrix variables were: total hours worked; number of shift extensions >30 min, night shifts; breaks < 9 h; breaks ≥ 24 h; nights with sleep 11pm to 7am; days waking fully rested; and roster change. After controlling for gender, ethnicity, years of nursing experience, and the extent of shift choice, the matrix score was a significant independent predictor of each of the three fatigue-related outcomes, and for all four aspects of life outside work. For all outcome variables, the model including the matrix score was a better fit to the data than the equivalent model including all the (uncategorised) work pattern variables.
A matrix that predicts the likelihood of nurses reporting fatigue-related safety outcomes can be used to compare the impact of rosters both at work and outside work. It can be used for roster design and management, and to guide nurses' choices about the shifts they work.
护士排班的多个方面相互作用,会影响她们所能提供的患者护理质量以及自身的健康、安全和福祉。
(1)开发并测试一个矩阵,该矩阵纳入排班和恢复性睡眠的多个方面,这些方面分别与三个与疲劳相关的结果相关——与疲劳相关的临床失误、过度嗜睡和困倦驾驶;(2)评估该矩阵是否还能预测护士对排班对工作以外生活方面影响的评分。
利用来自对六个高疲劳风险的医院实践领域护士疲劳和工作模式的全国性调查数据,开发并测试该矩阵。
调查数据包括人口统计学信息、工作模式(前14天)、班次选择,以及工作模式对社交生活、家庭生活、人际关系和其他事务造成问题的程度(评分从1 = 完全没有到5 = 非常严重)。基于与疲劳相关结果的单变量关联、集体雇佣合同的限制以及先前的研究,选择矩阵变量。每个变量分为低风险(得分0)、显著风险(得分1)或高风险(得分2)。逻辑多元回归建模测试了矩阵得分相对于包含所有与每个结果变量有显著单变量关联的(未分类的)工作模式和恢复性睡眠变量的模型的独立预测能力。使用赤池信息准则和贝叶斯信息准则统计量来衡量模型拟合度。
数据来自2358名护士,他们在前两周内在其中一个目标实践领域平均每周工作至少30小时。最终的矩阵变量为:总工作时长;延长超过30分钟的班次数量、夜班;休息时间<9小时;休息时间≥24小时;晚上11点至早上7点的睡眠;完全休息后醒来的天数;以及排班变化。在控制了性别、种族、护理经验年限和班次选择程度后,矩阵得分是三个与疲劳相关结果以及工作以外生活所有四个方面的显著独立预测因素。对于所有结果变量,包含矩阵得分的模型比包含所有(未分类的)工作模式变量的等效模型对数据的拟合度更好。
一个能够预测护士报告与疲劳相关安全结果可能性的矩阵,可用于比较排班在工作中和工作外的影响。它可用于排班设计和管理,并指导护士对工作班次的选择。