Moral distress risk profiling in ICU nurses: A cross-sectional study

Area of Science:

• Nursing
• Healthcare Management
• Psychology

Background:

• Moral distress is prevalent among ICU nurses, contributing to burnout and reduced care quality.
• Understanding the specific factors and their interplay in predicting moral distress is crucial but poorly characterized.

Purpose of the Study:

• To identify key factors associated with moral distress in intensive care unit (ICU) nurses.
• To develop a parsimonious, exploratory risk-profiling model for moral distress in this population.

Main Methods:

• A multicentre cross-sectional survey involving 318 ICU nurses in China.
• Machine learning analysis, including LASSO regression and Gradient Boosting Machine (GBM), guided by SHapley Additive exPlanations (SHAP) for feature selection and model development.
• Validated scales for moral distress, resilience, and sleep quality were utilized.

Main Results:

• High moral distress was reported by 28.6% of participants.
• A six-factor model, including monthly night shifts, financial responsibility, psychological resilience, sleep quality, nurse-to-patient ratio, and weekly working hours, accurately predicted moral distress (AUC = 0.907).
• Night shift frequency showed a nonlinear association with moral distress risk.

Conclusions:

• Six key factors significantly predict moral distress in ICU nurses, offering potential targets for intervention.
• Interventions focusing on night shift scheduling, financial support, and enhancing resilience and sleep quality are suggested.
• External validation is necessary before clinical application of the risk model.