Burnout, Intrinsic Motivation, and Emotion Regulation Profiles Among Pediatric Nurses in Turkey: A Latent Profile Analysis

Abstract

Objective: This study aimed to identify latent profiles based on burnout, intrinsic motivation, and emotion regulation among pediatric nurses and to examine their associations with psychological resilience, life satisfaction, depression, anxiety, and stress. Methods: A cross-sectional study involving 563 pediatric nurses was conducted between February and May 2025 in five hospitals across four provinces in Turkey. Data were collected via the Maslach Burnout Inventory, Intrinsic Motivation Scale, Emotion Regulation Scale, Satisfaction Life Scale, Brief Resilience Scale, and DepressionAnxiety-Stress Scale. Latent profile analysis was used to analyze the latent profiles of pediatric nurses, and multinomial logistic regression was used to explore the predictors of these profiles. Results: Three profiles were identified: Balanced (52.8%), Burnt-out (8.3%), and Optimistic group (38.9%). Lower resilience (OR = 0.47) and life satisfaction (OR = 0.27) increased the likelihood of being in the burnt-out profile, whereas higher resilience (OR = 1.85), higher life satisfaction (OR = 1.46), and gender (OR = 0.60) increased the likelihood of belonging to the optimistic profile. Age, depression, anxiety, and stress were not significant predictors. Conclusion: High psychological resilience and life satisfaction were associated with a greater likelihood of pediatric nurses being classified into more adaptive profiles characterized by lower burnout, higher intrinsic motivation, and more effective emotion regulation. Female nurses were more likely to belong to the burnout profile, highlighting the importance of gender-sensitive support strategies. Practical implications: Integrating profile-based assessments of burnout, intrinsic motivation, and emotion regulation into pediatric nursing practice can guide targeted individual and institutional interventions. (c) 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.