Effects of Job Crafting and Leisure Crafting on Nurses’ Burnout: A Machine Learning‐Based Prediction Analysis

倦怠 心理学 应用心理学 社会心理学 护理部 医学 临床心理学
作者
Yufang Guo,Si-Jia Wang,Virginia Plummer,Yun Du,Tian-Ping Song,Na Chuan Wang
出处
期刊:Journal of Nursing Management [Wiley]
卷期号:2024 (1)
标识
DOI:10.1155/2024/9428519
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Danny关注了科研通微信公众号
刚刚
nocap666发布了新的文献求助10
刚刚
开朗的柜子完成签到,获得积分10
刚刚
刚刚
銪志青年发布了新的文献求助10
1秒前
jinjun发布了新的文献求助10
1秒前
Dreamable完成签到,获得积分10
2秒前
Owen应助秋老虎采纳,获得10
2秒前
3秒前
3秒前
3秒前
3秒前
汉关明月完成签到,获得积分10
3秒前
星辰大海应助Daniel_wu采纳,获得10
3秒前
彭于晏应助Jasen采纳,获得10
4秒前
newplayer发布了新的文献求助10
4秒前
4秒前
5秒前
简单应助感谢大哥的帮助采纳,获得20
6秒前
Wayne72完成签到,获得积分10
6秒前
鳗鱼灵寒发布了新的文献求助10
6秒前
朴素觅风发布了新的文献求助10
7秒前
8秒前
freemoe发布了新的文献求助10
8秒前
CodeCraft应助nocap666采纳,获得10
8秒前
乔心发布了新的文献求助10
8秒前
慕青应助wanwu采纳,获得10
9秒前
creed发布了新的文献求助30
9秒前
Alice发布了新的文献求助10
9秒前
SYLH应助不想当打工人采纳,获得10
9秒前
zengyangyu完成签到,获得积分10
10秒前
sdvsd发布了新的文献求助30
10秒前
10秒前
10秒前
10秒前
11秒前
科研通AI5应助是滴是滴采纳,获得10
11秒前
labulabu应助乔心采纳,获得10
12秒前
希望天下0贩的0应助乔心采纳,获得10
12秒前
QAQ1233发布了新的文献求助30
12秒前
高分求助中
Genetics: From Genes to Genomes 3000
Continuum thermodynamics and material modelling 3000
Production Logging: Theoretical and Interpretive Elements 2500
Healthcare Finance: Modern Financial Analysis for Accelerating Biomedical Innovation 2000
Applications of Emerging Nanomaterials and Nanotechnology 1111
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Theory of Block Polymer Self-Assembly 750
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3474464
求助须知:如何正确求助?哪些是违规求助? 3066697
关于积分的说明 9100406
捐赠科研通 2758051
什么是DOI,文献DOI怎么找? 1513292
邀请新用户注册赠送积分活动 699484
科研通“疑难数据库(出版商)”最低求助积分说明 698995