Content analysis

Content Analysis Training Framework with Structured Teams (FORT-CAST): A framework for content analysis of open-ended survey questions using multidisciplinary coders

This article was originally published here

Health Nurses Res. 2022 Apr 11. doi:10.1002/nur.22227. Online ahead of print.

ABSTRACT

In the context of a global pandemic, the need for reliable analysis of qualitative health data has never been greater. Open-ended questions are a convenient way for researchers and organizational stakeholders to deepen their understanding of complex situations when timely research is needed. However, interpreting short, textual responses can be problematic. Manual and automated/semi-automated methods of coding qualitative data have been associated with costly errors and time delays. The data obtained from the qualitative analysis of the open-ended questions has been questioned because it lacks solid information. This article presents an innovative, manual and team-based method for analyzing responses to open-ended survey questions. This method was developed and implemented at the start of the COVID-19 pandemic to understand the needs of nurses and their perceptions of the organizational strategies that were implemented to meet the challenges related to the pandemic. This framework uses a dedicated project management structure, general-purpose software for data collection and analysis, framework training designed for an interdisciplinary team of coders, and data analysis procedures that align with qualitative content analysis procedures. In concert, these techniques enable research team members from diverse backgrounds and disparate levels of experience to provide unique human insights into 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.

PMID:35411623 | DOI:10.1002/nur.22227