JOURNAL ARTICLE

How to code gerunds in constructivist grounded theory research: an example.

  • Published In: Nurse Researcher, 2024, v. 32, n. 2. P. 31 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Irwin, Kasey Ann; Donnelly, Frank; Kelly, Janet 3 of 3

Abstract

Why you should read this article: • To understand that preserving action or process when coding gerunds in constructivist grounded theory research can help discover the participants’ main concerns, which provide context for the substantive theory • To appreciate that looking for and recognising gerunds in qualitative data can be confusing for novice researchers • To benefit from an example of coding gerunds to gain deeper insights and depth to qualitative data analysis. Background: Coding for gerunds is useful in developing theory in grounded theory. However, it can be confusing for the novice researcher to recognise these words, which consider actions more abstractly. Aim: To explain how to identify, analyse and code gerunds, using the example of a constructivist grounded theory study investigating the design of operating rooms. Discussion: Coding for gerunds helped to illustrate participants’ actions and sequences in the example study and added depth to the researcher’s understanding of certain topics. Conclusion: Coding gerunds can improve the insights obtained in grounded theory studies. Implications for practice: This article may encourage nurse researchers to focus on actions to add depth to their qualitative analyses. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Nurse Researcher. 2024/06, Vol. 32, Issue 2, p31
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:1351-5578
  • DOI:10.7748/nr.2024.e1914
  • Accession Number:177802123
  • Copyright Statement:Copyright of Nurse Researcher is the property of Royal College of Nursing of the United Kingdom (The) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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