Meta-ethnography 25 years on: challenges and insights for synthesising a large number of qualitative studies
Bibliographic record
Abstract
Studies that systematically search for and synthesise qualitative research are becoming more evident in health care, and they can make an important contribution to patient care. Our team was funded to complete a meta-ethnography of patients' experience of chronic musculoskeletal pain. It has been 25 years since Noblit and Hare published their core text on meta-ethnography, and the current health research environment brings additional challenges to researchers aiming to synthesise qualitative research. Noblit and Hare propose seven stages of meta-ethnography which take the researcher from formulating a research idea to expressing the findings. These stages are not discrete but form part of an iterative research process. We aimed to build on the methods of Noblit and Hare and explore the challenges of including a large number of qualitative studies into a qualitative systematic review. These challenges hinge upon epistemological and practical issues to be considered alongside expectations about what determines high quality research. This paper describes our method and explores these challenges. Central to our method was the process of collaborative interpretation of concepts and the decision to exclude original material where we could not decipher a concept. We use excerpts from our research team's reflexive statements to illustrate the development of our methods.
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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 | 0.583 | 0.636 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.025 | 0.038 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".