A computational validation for the health concept maturity levels questionnaire

This study introduces computational semantic validity as a novel complementary approach to traditional psychometric validation, providing evidence that supports both convergent and content validity for the CMLH questionnaire. This computational method demonstrates semantic alignment between expert discourse and questionnaire structure through machine learning and deep learning techniques. However, overlaps between "Programmatic" and "Need" factors indicate a need for improvement in the Concept Maturity Levels Health model. Future work will focus on enhancing these models and investigating their potential application as a complementary validation method for other psychometric tools.