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Two sets of qualitative research reporting guidelines: An analysis of the shortfalls.

Olivia A King
Published in: Research in nursing & health (2021)
Findings from qualitative research may make valuable contributions to the evidence informing healthcare practice. Qualitative research methodologies and methods, however, are less familiar to health researchers and research consumers when compared with quantitative methods. Qualitative research reporting guidelines and their merit have been hotly debated for at least two decades. Herein I discuss two sets of qualitative research reporting guidelines endorsed by many high tiered health research journals: Consolidated criteria for reporting qualitative research and Standards for reporting qualitative research. Six aspects of the two sets of guidelines are compared. The first aspect is the focus of the guidelines. The latter five aspects are items included in the guidelines: reflexivity, participant sampling and saturation, data collection, member checking, and data analysis. Except for reflexivity, these items were selected for comparison as they include features of, or strategies to, enhance the rigor of qualitative research that are applicable within some but not all qualitative methodologies. Reflexivity, a central feature of rigor within all qualitative research, is discussed for its suboptimal representation in both sets of reporting guidelines. Without regular and critical review of reporting guidelines, efforts to promote the design, conduct, and reporting of rigorous qualitative health research to support evidence-informed practice may be undermined. Moreover, for qualitative research reporting guidelines to be useful, they must be applied appropriately and in a flexible manner by researchers and reviewers. This paper has implications for researchers, journal editors, reviewers, and research consumers.
Keyphrases
  • healthcare
  • adverse drug
  • systematic review
  • clinical practice
  • data analysis
  • primary care
  • emergency department
  • machine learning
  • deep learning