Influencing factors associated with mental workload among nurses: A latent profile analysis

Abstract

Objective

This study aimed to examine the latent profile of nurses' mental workload (MWL) and explore the influencing factors via a person-centred approach.

Methods

From March to July 2023, a quantitative cross-sectional study was carried out to investigate 526 Chinese clinical nurses from five tertiary hospitals in Sichuan Province, China, by using demographic information, the Perceived Social Support Scale, Simplified Coping Skill Questionnaire, and NASA-Task Load Index. Latent profile analyses were performed using Mplus 7.3 software. Pearson’s chi-squared and logistic regression analysis was done using SPSS 24.0 software.

Results

Three profiles of mental workload were identified based on the nurses’ responses to the mental workload assessment, designated as “low MWL-high self-rated (n = 70, 13.3%)”, “moderate MWL (n = 273, 51.9%)”, and “high MWL-low self-rated (n = 183, 34.8%)”. Based on the analysis of the three subtypes, nurses with working years < 5 years (χ² = 12.135, P < 0.05), no children (χ² = 16.182, P < 0.01), monthly income < 6000 (χ² = 55.231, P < 0.001), poor health status (χ² = 39.658, P < 0.001), no psychological training in the past year (χ² = 56.329, P < 0.001) and suffering from workplace violence (χ² = 19.803, P < 0.001) were significantly associated with MWL. Moreover, the multivariate logistic regression analysis showed that negative coping styles (OR = 1.146, 95% CI: 1.060–1.238, P = 0.001) were accompanied by higher MWL while negatively associated with perceived social support (OR = 0.927, 95% CI: 0.900–0.955, P < 0.001).

Conclusion

Our results showed that the MWL of nurses could be classified into three subtypes. Monthly income, health status, psychological training, workplace violence, negative coping style, and perceived social support were the factors influencing MWL. Managers can employ personalised intervention strategies according to the individual characteristics of different subgroups to reduce nurses’ MWL.