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GRADE-CERQual Methodology

GRADE-CERQual (Confidence in the Evidence from Reviews of Qualitative Research) is used to assess confidence in review findings.

Overview

GRADE-CERQual assesses how much confidence to place in findings from qualitative evidence syntheses. It evaluates four components:

  1. Methodological limitations
  2. Coherence
  3. Adequacy of data
  4. Relevance

Confidence Levels

Level Definition
High Highly likely that the review finding is a reasonable representation of the phenomenon of interest
Moderate Likely that the review finding is a reasonable representation
Low Possible that the review finding is a reasonable representation
Very Low Not clear whether the review finding is a reasonable representation

Assessment Components

1. Methodological Limitations

Concerns about the design or conduct of the primary studies:

  • Study design appropriateness
  • Data collection methods
  • Analysis rigor
  • Researcher reflexivity

2. Coherence

How well the review finding is supported by the data:

  • Consistency across studies
  • Pattern clarity
  • Explanation of variations
  • Fit between data and interpretation

3. Adequacy of Data

Richness and quantity of data supporting the finding:

  • Number of studies
  • Depth of data
  • Breadth of contexts
  • Saturation indicators

4. Relevance

Applicability of the data to the review question:

  • Context alignment
  • Population match
  • Phenomenon relevance
  • Setting appropriateness

EHDS Review Findings

High Confidence Findings

  • Data governance frameworks are essential (n=18 studies)
  • Strong methodological quality
  • High coherence across contexts

  • Rich, saturated data

  • Privacy concerns require technical solutions (n=15 studies)

  • Consistent evidence
  • Multiple country contexts
  • Adequate data depth

Moderate Confidence Findings

  • National implementation varies significantly (n=12 studies)
  • Some methodological concerns
  • Good coherence

  • Adequate relevance

  • Citizen trust depends on transparency (n=10 studies)

  • Minor limitations
  • Moderate data adequacy

Low Confidence Findings

  • Federated learning adoption faces barriers (n=8 studies)
  • Limited study count
  • Emerging evidence base
  • Technical focus

Access CERQual Data

from ehdslens import EHDSAnalyzer

analyzer = EHDSAnalyzer()
analyzer.load_default_data()

findings = analyzer.get_grade_cerqual_summary()

for f in findings:
    print(f"[{f['confidence'].upper()}]")
    print(f"  Finding: {f['finding']}")
    print(f"  Studies: n={f['studies']}")

References

Lewin S, Booth A, Glenton C, et al. Applying GRADE-CERQual to qualitative evidence synthesis findings: introduction to the series. Implementation Science. 2018;13(Suppl 1):2.