Tapping Into Awareness: Assessing Nursing Students' Water Consumption Behaviors and Sustainability Perceptions Through a Cross-Sectional Study With Machine Learning Approach.

Introduction: Investigating water consumption behaviors and perceptions of water sustainability among nursing students is crucial for effective resource management. This study employs machine learning (ML) techniques to analyze these factors in detail.

Methods: This descriptive cross-sectional study involved 182 senior nursing students from an accredited faculty in Istanbul, Turkey, during the 2023-2024 academic year. Data were collected through an online survey, including an information form, a visual analog scale toward foresight about water scarcity (VAS), and a water consumption behavior scale. Advanced ML techniques were utilized to identify intricate patterns and correlations in the students' behaviors and perceptions.

Results: The survey revealed that nursing students primarily rely on packaged water and perceive an imminent threat of water scarcity, though they exhibit limited awareness of their personal water footprint. High VAS scores indicated a strong awareness of global water crises but skepticism about local water scarcity. The ML model identified "Domestic Water Use Efficiency" as the primary factor influencing attitudes toward water sustainability, with "Water Awareness" and "Sustainable Water Ethics" also playing significant roles.

Conclusions: The study highlights the need to integrate sustainable water management education into nursing curricula and demonstrates nursing students' awareness and preparedness for sustainable practices.