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

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.