Frame of reference training for content analysis with structured teams (FORT‐CAST): A framework for content analysis of open‐ended survey questions using multidisciplinary coders. Issue 4 (11th April 2022)

• Record Type: Journal Article
• Authors: Galura, Sandra J.; Horan, Kristin A.; Parchment, Joy; Penoyer, Daleen; Schlotzhauer, Ann; Dye, Kenzie; Hill, Emily
• Is Part Of: Research in nursing & health. Volume 45:Issue 4(2022)
• Page Start: 477
• Page End: 487
• Publication Date: 2022-04-11
• Subjects: frame‐of‐reference training -- open‐ended questions -- qualitative analysis; Nursing -- Research -- Periodicals; Nursing -- Periodicals; 610.7305
• DOI: 10.1002/nur.22227
• Languages: English
• ISSNs: 0160-6891

Abstract

Abstract: In the context of a global pandemic, the need for reliable analysis of qualitative data in healthcare has never been more pressing. Open‐ended questions are a feasible way for both researchers and organizational stakeholders to gain deeper insight into complex situations when timely research is needed. However, the interpretation of brief, textual responses can prove problematic. Both manual and automated/semiautomated methods of coding qualitative data have been associated with errors and costly temporal delays. Data obtained from the qualitative analysis of open‐ended questions have been questioned for lacking robust insights. The present article introduces an innovative, manual, team‐based method of analyzing responses to open‐ended survey questions. This method was developed and implemented at the outset of the COVID‐19 pandemic to understand the needs of nurses and their perceptions of organizational strategies that were implemented to address pandemic‐related challenges. This framework utilizes a dedicated project management structure, general purpose software for data collection and analysis, frame‐of‐reference training designed for an interdisciplinary team of coders, and data analysis procedures that align with qualitative content analysis procedures. In concert, these techniques empower researchd team members with varying backgrounds and disparate levels of experience to provide unique human insights to data analysis procedures, refine the coding process, and support the abstraction of meaningful themes that were used to prioritize organizational strategies and further support nurses as the pandemic progressed.