Hybrid method analysis of fire accidents in medical centers: a focus on a disastrous fire during the pandemic

Abstract

Background: Medical centers face significant fire accident risk, especially during the pandemic. This study examines the applicability of mixed-method research for analyzing fire accidents in medical centers and focuses on a disastrous fire during the pandemic as a case study. Methods: Data were collected using a triangulation approach from various sources, including accident reports, regulatory body reports, expert interviews, and laws and regulations. The data were analyzed using inductive and deductive content analysis methods. The trustworthiness of the findings was evaluated using five criteria: credibility, transferability, confirmability, dependability, and authenticity. Results: The inductive content analysis identified 42 causal factors classified into five categories and 15 subcategories. AcciMap and STAMP methods were used to selectively code hierarchical causal relationships, control loops, and feedback cycles. Findings showed that the main causal factors of fire accidents were converting a residential building to a medical center and unregulated development without considering building safety. Conclusion: The combination of content analysis, AcciMap, and STAMP methods showed that amending laws and regulations related to establishing medical centers and shortening supervision loops is the crucial resilience cycle against fire accidents in medical centers. The study demonstrated that inductive content analysis can extract accident causal factors with desirable trustworthiness. At the same time, the AcciMap and STAMP methods can be applied in deductive content analysis of causal factors based on the hierarchy of organizational structures.