- Title
- Effects of job crafting and leisure crafting on nurses' burnout : a machine learning-based prediction analysis
- Creator
- Guo, Yu-Fang; Wang, Si-Jia; Plummer, Virginia; Du, Yun; Song, Tian-Ping; Wang, Ning
- Date
- 2024
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/202151
- Identifier
- vital:19638
- Identifier
-
https://doi.org/10.1155/2024/9428519
- Identifier
- ISSN:0966-0429 (ISSN)
- Abstract
- Aim. 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. Background. 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. Methods. Multicentre cross-sectional survey study. Nurses (n = 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. Results. 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. Conclusion. 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. Implications for Nursing Management. 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. © 2024 Yu-Fang Guo et al.
- Publisher
- Wiley-Hindawi
- Relation
- Journal of Nursing Management Vol. 2024, no. (2024), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright 2024 Yu-Fang Guo et al
- Rights
- Open Access
- Subject
- 4205 Nursing; Nurse burnout; Job crafting; Leisure crafting; Machine learning; Prediction analysis; Healthcare management
- Full Text
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