Digital Storytelling: A Multilayered Data-Analysis Framework for Nursing and Health Research

In recent years, with the increased accessibility of online resources, digital storytelling (DST) has emerged as a valuable and expanding database for qualitative nursing and health researchers to explore patients' experiences, professional narratives, and community voices. While the current methodological literature offers various approaches for analyzing digital storytelling with regard to its specific content, many frameworks overlook the multilayered nature of these narratives and lack a systematic guide for navigating the complex data-analysis phase. This phase requires careful identification of the different layers of data and a structured evaluation of their significance, enabling researchers to justify analytic decisions regarding which layers of DST they chose to analyze, a choice that can inform nursing practice, education, and policy. This article proposes a multilayered data-analysis framework. The suggested framework categorizes DST along a two-axis matrix: one ranging from individual to collective, and the other from simple to stylized. To illustrate its practical application, four short hypothetical case studies situated within health and nursing contexts are presented. The discussion situates this framework within the broader sociopolitical, cultural, and ethical dimensions of health-related digital storytelling, underscoring its relevance for advancing nursing knowledge, enhancing culturally sensitive care, and contributing to patient-centered health research.