A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts

This paper presents a new approach called MultiNeutrosophic Offset Structures (MN-OS) to improve students’ Mental Health Literacy (MHL). The method combines ideas from psychology, education, sociology, and computer science to provide a more personalized and accurate way of understanding and improving students’ mental health knowledge. Unlike traditional models, MN-OS allows the levels of truth (T), uncertainty (I), and falsehood (F) to go beyond normal limits (above 1 or below 0), which helps capture extreme cases like strong misconceptions or outstanding understanding. The model represents students’ knowledge as points within a Multiple space and uses new mathematical tools such as custom operators, matrices, and clustering algorithms to group students with similar MHL profiles. Based on these groups, targeted interventions can be designed to address specific needs. To test the model, a simulation was conducted with 300 students. The results showed 94% accuracy in clustering and an 87% improvement in MHL outcomes after the interventions. This demonstrates that the MNOS framework is effective, flexible, and scalable for improving mental health education in diverse student populations.