A Neutrosophic Framework for Nursing Education Quality Analysis Using Upside-Down Logics and Narrative Factor

Evaluating the quality of nursing education is challenging due to subjective and conflicting assessments from students, instructors, and administrators. Traditional methods, such as numerical grades, often fail to capture the uncertainty and differing perspectives in these evaluations. This study introduces a new mathematical model based on Neutrosophic Logic and Upside-Down Logics to address these issues. Neutrosophic Logic allows for the simultaneous representation of truth, falsehood, and indeterminacy, while Upside-Down Logics model how evaluations can shift between positive and negative depending on context. We also propose a Narrative Factor to quantify the impact of personal biases and cultural influences. The model includes clear equations and a practical application in a clinical simulation, demonstrating how it identifies uncertainty and bias in student evaluations. This framework offers a robust, flexible tool for improving the fairness and accuracy of nursing education assessments, with potential applications in other educational settings.