12 Critical appraisal tools for qualitative research – towards ‘fit for purpose’
Bibliographic record
Abstract
Qualitative research has an important place within evidence-based health care (EBHC), contributing to policy on patient safety and quality of care, supporting understanding of the impact of chronic illness, and explaining contextual factors surrounding the implementation of interventions. However, the question of whether, when and how to critically appraise qualitative research persists. Whilst there is consensus that we cannot - and should not – simplistically adopt existing approaches for appraising quantitative methods, it is nonetheless crucial that we develop a better understanding of how to subject qualitative evidence to robust and systematic scrutiny in order to assess its trustworthiness and credibility. Currently, most appraisal methods and tools for qualitative health research use one of two approaches: checklists or frameworks. We have previously outlined the specific issues with these approaches (Williams et al 2019). A fundamental challenge still to be addressed, however, is the lack of differentiation between different methodological approaches when appraising qualitative health research. We do this routinely when appraising quantitative research: we have specific checklists and tools to appraise randomised controlled trials, diagnostic studies, observational studies and so on. Current checklists for qualitative research typically treat the entire paradigm as a single design (illustrated by titles of tools such as ‘CASP Qualitative Checklist’, ‘JBI checklist for qualitative research’) and frameworks tend to require substantial understanding of a given methodological approach without providing guidance on how they should be applied. Given the fundamental differences in the aims and outcomes of different methodologies, such as ethnography, grounded theory, and phenomenological approaches, as well as specific aspects of the research process, such as sampling, data collection and analysis, we cannot treat qualitative research as a single approach. Rather, we must strive to recognise core commonalities relating to rigour, but considering key methodological differences. We have argued for a reconsideration of current approaches to the systematic appraisal of qualitative health research (Williams et al 2021), and propose the development of a tool or tools that allow differentiated evaluations of multiple methodological approaches rather than continuing to treat qualitative health research as a single, unified method. Here we propose a workshop for researchers interested in the appraisal of qualitative health research and invite them to develop an initial consensus regarding core aspects of a new appraisal tool that differentiates between the different qualitative research methodologies and thus provides a ‘fit for purpose’ tool, for both, educators and clinicians.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.496 | 0.741 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.039 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".