Introduction: ConQual is a method recently developed by the Joanna Briggs Institute to help health care practitioners and policy makers know to what extent confidence can be placed in the synthesized findings of qualitative systematic reviews (Joanna Briggs Institute Levels of Evidence and Grades of Recommendation Working Party, 2016; Munn, Porritt, Lockwood, Aromataris, & Pearson, 2014). A confidence score of high, moderate, low, or very low is applied to each synthesized finding. The confidence score is derived on the basis of the methodological rigor, determined as dependability, of the primary studies used for the specific synthesized finding, along with the credibility of the research findings extracted across the primary studies and aggregated to produce the synthesized finding. The ConQual score is downgraded, from a starting point of high, according to the sum of the levels of quality applied to dependability and credibility.
Objective: The purpose of this presentation is to discuss the method of derivation of ConQual scores and challenges encountered in using the method in a recently conducted qualitative systematic review. A number of questions will be raised for reflection, for example; Does the ConQual method produce confidence scores that are too stringent? Is there an alternative approach to combining the dependability and credibility levels to produce a less severe score? Does methodological rigor of the primary studies matter if the research findings of the studies are of high quality? How ought confidence scores influence recommendations for practice and policy? In addition to the confidence scores, should other considerations be taken into account in generating recommendations for practice and policy?
Conclusion: It is important that confidence in synthesized findings is a considered and reasonable assessment as scores have implications for whether health care practitioners and policy makers accept the synthesized findings and associated recommendations to guide practice and policy decision-making.