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

Abstract

 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.

DOI: 10.5281/zenodo.15691163