Liang Xue, Wang Jue
Department of Anesthesiology, Shandong Provincial Hospital, Shandong First Medical University, Jinan, China.
Department of Opthmology, Shandong Second Provincial General Hospital, Jinan, Shandong, 271016, China.
BMC Psychol. 2025 Jul 1;13(1):676. doi: 10.1186/s40359-025-03000-8.
Nurses, as the largest workforce in healthcare, play a crucial role in achieving universal health coverage. However, they continually face both physical and emotional burdens. Identifying key stressors that contribute to turnover intention and examining whether professional grief moderates the relationship between job stress and turnover intention is essential for reducing nurse attrition.
This study utilized linear regression models and the Extreme Gradient Boosting machine (XGBoost) learning algorithm to analyze the impact of the total score of job stress and their dimensions on turnover intention. XGBoost, known for enhancing sensitivity in detection and improving generalization performance, is particularly beneficial for high-dimensional problems and data heterogeneity. It integrates multiple variables and accommodates small sample sizes, making it a valuable supplement to conventional regression techniques. Through hierarchical regression, the moderating role of professional grief between job stress and turnover intention was explored. Additionally, an interactive tool was used to visually present the results.
Among the dimensions of job stress, patient care issues exhibited the strongest association with turnover intention, followed by nursing profession and work problems, time allocation and workload, management and interpersonal issues, and working environment and equipment problems. Notably, professional grief significantly moderated the relationship between job stress and turnover intention. Specifically, for overall job stress and the dimensions of nursing profession and work-related problems, time allocation and workload, and patient care issues, higher levels of professional grief intensified their impact on turnover intention. However, this moderating effect was not observed for stressors related to management and interpersonal issues or working environment and equipment problems.
In emotionally labor-intensive work environments, professional grief tends to amplify turnover intention, while its impact on issues related to management and material resources is less pronounced.Healthcare policymakers should focus on job stress and professional grief to reduce turnover intention, ultimately benefiting patient care and treatment outcomes.
护士作为医疗保健领域最大的劳动力群体,在实现全民健康覆盖方面发挥着关键作用。然而,他们持续面临身体和情感负担。识别导致离职意愿的关键压力源,并考察职业悲伤是否调节工作压力与离职意愿之间的关系,对于减少护士流失至关重要。
本研究采用线性回归模型和极端梯度提升机(XGBoost)学习算法,分析工作压力总分及其维度对离职意愿的影响。XGBoost以提高检测灵敏度和改善泛化性能而闻名,对高维问题和数据异质性特别有益。它整合多个变量并能处理小样本量,是传统回归技术的宝贵补充。通过分层回归,探讨职业悲伤在工作压力和离职意愿之间的调节作用。此外,使用交互式工具直观呈现结果。
在工作压力维度中,患者护理问题与离职意愿的关联最强,其次是护理职业与工作问题、时间分配与工作量、管理与人际问题以及工作环境与设备问题。值得注意的是,职业悲伤显著调节了工作压力与离职意愿之间的关系。具体而言,对于总体工作压力以及护理职业与工作相关问题、时间分配与工作量、患者护理问题等维度,较高水平的职业悲伤加剧了它们对离职意愿的影响。然而,对于与管理和人际问题或工作环境与设备问题相关的压力源,未观察到这种调节作用。
在情感劳动密集型的工作环境中,职业悲伤往往会加剧离职意愿,而其对与管理和物质资源相关问题的影响则不那么明显。医疗保健政策制定者应关注工作压力和职业悲伤,以降低离职意愿,最终有益于患者护理和治疗结果。