Lessons Learned From Data Falsification During an Academic Course Using A Root-Cause Analysis

Abstract

Background: Fabricating data or creating fictitious datasets undermines research credibility with severe consequences. Purpose: To describe a data falsification incident that occurred during an undergraduate nursing research seminar and share the subsequent corrective measures employed at individual and class levels. Methods: The students involved in the falsification were asked to identify the incident’s factors using an Ishikawa diagram and the 5M-Model approach, presenting their findings to the class. Results: In guided meetings, students offered diverse perspectives on the incident’s causes, thoroughly examining the decision-making process behind data falsification, considering motives and emotions. Despite initial tension, the atmosphere improved as students displayed openness and honesty. Conclusions: The current case study uniquely combines educational concepts with an approach to establishing a constructive organizational culture, incorporating tools from risk management and treatment safety. Academia should study adverse events, engage students in learning, and emphasize the integration of ethical codes in academia and nursing.