Patterns and predictors of mental workload in intern nursing students: a latent profile analysis

实习护理学生心理负荷模式及预测因素:潜在剖面分析

阅读:2

Abstract

BACKGROUND: Intern nursing students are facing considerable psychological burdens, which impact their mental well-being and career progression. Although numerous studies have explored the psychological status of intern nursing students and its influencing factors, the majority of these investigations have primarily focused on single-factor linear relationships. To date, there has been limited research analyzing the individual differences among intern nursing students. OBJECTIVE: This study aimed to investigate the mental workload patterns of intern nursing students and identify the factors that predict these patterns. METHODS: A total of 320 intern nursing students were recruited for this study via convenience sampling, 302 of whom completed the survey. A pattern of intern nursing students' mental workload was identified through a latent profile analysis of 6 items on the NASA-Task Load Index scale. The analysis of latent profiles was performed using Mplus 8.7 software, while χ2 test and logistic regression analysis were carried out using SPSS 27.0 software. RESULTS: Three patterns of mental workload of intern nursing students were identified as "low MWL-high self-rated (n = 45, 14.9%)", "moderate MWL (n = 152, 50.33%)", and "high MWL-low self-rated (n = 105, 34.77%)". Age and monthly income of 3000-5000 RMB were the main predictors of low MWL-high self-rated pattern. In contrast, long internships, passive coping strategies, college degree and monthly income < 3000 RMB were predictors of moderate MWL pattern. CONCLUSION: This study provided novel insights into the mental workload patterns among intern nursing students. The findings highlighted the heterogeneity of MWL and provide evidence-based guidance for nursing administrators to identify groups of intern nursing students with high mental workload and to develop targeted psychological interventions and management strategies. CLINICAL TRIAL NUMBER: Not applicable.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。