Guo Yu-Fang, Wang Si-Jia, Plummer Virginia, Du Yun, Song Tian-Ping, Wang Ning
School of Nursing and Rehabilitation Shandong University, Jinan, Shandong, China.
Institute of Health and Wellbeing Federation University Australia, Victoria, Australia.
J Nurs Manag. 2024 Jun 20;2024:9428519. doi: 10.1155/2024/9428519. eCollection 2024.
To explore the status of job crafting, leisure crafting, and burnout among nurses and to examine the impact of job crafting and leisure crafting variations on burnout using machine learning-based models.
The prevalence of burnout among nurses poses a severe risk to their job performance, quality of healthcare, and the cohesiveness of nurse teams. Numerous studies have explored factors influencing nurse burnout; however, few involved job crafting and leisure crafting synchronously and elucidated the effect differences of the two crafting behaviors on nurse burnout.
Multicentre cross-sectional survey study. Nurses ( = 1235) from four Chinese tertiary hospitals were included. The Maslach Burnout Inventory-General Survey, the Job Crafting Scale, and the Leisure Crafting Scale were employed for data collection. Four machine learning algorithms (logistic regression model, support vector machine, random forest, and gradient boosting tree) were used to analyze the data.
Nurses experienced mild to moderate levels of burnout and moderate to high levels of job crafting and leisure crafting. The AUC (in full) for the four models was from 0.809 to 0.821, among which the gradient boosting tree performed best, with 0.821 AUC, 0.739 accuracy, 0.470 sensitivity, 0.919 specificity, and 0.161 Brier. All models showed that job crafting was the most important predictor for burnout, while leisure crafting was identified as the second important predictor for burnout in the random forest model and gradient boosting tree model.
Even if nurses experienced mild to moderate burnout, nurse managers should develop efficient interventions to reduce nurse burnout. Job crafting and leisure crafting may be beneficial preventative strategies against burnout among nurses at present. . Job and leisure crafting were identified as effective methods to reduce nurse burnout. Nurse managers should provide more opportunities for nurses' job crafting and encourage nurses crafting at their leisure time.
探讨护士的工作重塑、休闲重塑及职业倦怠状况,并使用基于机器学习的模型检验工作重塑和休闲重塑变化对职业倦怠的影响。
护士职业倦怠的普遍存在对其工作绩效、医疗质量和护士团队凝聚力构成严重风险。众多研究探讨了影响护士职业倦怠的因素;然而,很少有研究同时涉及工作重塑和休闲重塑,并阐明这两种重塑行为对护士职业倦怠的影响差异。
多中心横断面调查研究。纳入来自中国四家三级医院的1235名护士。采用马氏职业倦怠通用问卷、工作重塑量表和休闲重塑量表进行数据收集。使用四种机器学习算法(逻辑回归模型、支持向量机、随机森林和梯度提升树)进行数据分析。
护士经历了轻度至中度的职业倦怠以及中度至高度的工作重塑和休闲重塑。四个模型的曲线下面积(完整)为0.809至0.821,其中梯度提升树表现最佳,曲线下面积为0.821,准确率为0.739,灵敏度为0.470,特异度为0.919,布里尔分数为0.161。所有模型均表明,工作重塑是职业倦怠最重要的预测因素,而在随机森林模型和梯度提升树模型中,休闲重塑被确定为职业倦怠的第二重要预测因素。
即使护士经历了轻度至中度的职业倦怠,护士长也应制定有效的干预措施以减轻护士的职业倦怠。工作重塑和休闲重塑目前可能是预防护士职业倦怠的有益策略。工作和休闲重塑被确定为减轻护士职业倦怠的有效方法。护士长应为护士提供更多工作重塑的机会,并鼓励护士在闲暇时间进行重塑。