Latent profiles of emotional intelligence and associated factors among clinical nurses: a cross-sectional study.

Clinical nurses in tertiary hospitals face high emotional labor, which can deplete emotional resources and lead to occupational burnout. Emotional intelligence is a crucial psychological resource for coping with such stress. This cross-sectional study aimed to use latent profile analysis to identify emotional intelligence profiles among clinical nurses and examine factors associated with profile membership.

A cross-sectional study was conducted between December 2025 and January 2026. A convenience sample of 765 clinical nurses from two tertiary Grade A hospitals in China was recruited. Data were collected using a demographic and work-related characteristics questionnaire and the Wong and Law Emotional Intelligence Scale. Latent profile analysis was used to identify distinct emotional intelligence subgroups, and multinomial logistic regression was performed to examine factors associated with profile membership.

Three distinct latent profiles of emotional intelligence were identified among the clinical nurses: "Dysregulated Low emotional intelligence" (41.7%), "Moderate-Balanced emotional intelligence" (50.8%), and "High-Balanced emotional intelligence" (7.5%). The majority of nurses exhibited an overall moderate level of emotional intelligence. Multinomial logistic regression revealed that parental overprotection or control, personality type, involvement in department management, and job satisfaction were significant factors associated with latent profile membership.

The findings highlight the significant heterogeneity of emotional intelligence within the nursing workforce. Nursing managers may adopt differentiated management strategies and develop targeted training programs tailored to specific subgroup characteristics. Targeted support for emotional intelligence may contribute to nurses' mental wellbeing and the quality of nursing care.
Mental Health
Access
Care/Management
Advocacy

Authors

Pan Pan, Wu Wu, Huang Huang, Liu Liu, Xia Xia
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