Developing a reporting guideline to improve meta-ethnography in health research: the eMERGe mixed-methods study
Bibliographic record
Abstract
Background Meta-ethnography is a commonly used methodology for qualitative evidence synthesis. Research has identified that the quality of reporting of published meta-ethnographies is often poor and this has limited the utility of meta-ethnography findings to influence policy and practice. Objective To develop guidance to improve the completeness and clarity of meta-ethnography reporting. Methods/design The meta-ethnography reporting guidance (eMERGe) study followed the recommended approach for developing health research reporting guidelines and used a systematic mixed-methods approach. It comprised (1) a methodological systematic review of guidance in the conduct and reporting of meta-ethnography; (2) a review and audit of published meta-ethnographies, along with interviews with meta-ethnography end-users, to identify good practice principles; (3) a consensus workshop and two eDelphi (Version 1, Duncan E, Swinger K, University of Stirling, Stirling, UK) studies to agree guidance content; and (4) the development of the guidance table and explanatory notes. Results Results from the methodological systematic review and the audit of published meta-ethnographies revealed that more guidance was required around the reporting of all phases of meta-ethnography conduct and, in particular, the synthesis phases 4–6 (relating studies, translating studies into one another and synthesising translations). Following the guidance development process, the eMERGe reporting guidance was produced, comprising 19 items grouped into the seven phases of meta-ethnography. Limitations The finalised guidance has not yet been evaluated in practice; therefore, it is not possible at this stage to comment on its utility. However, we look forward to evaluating its uptake and usability in the future. Conclusions The eMERGe reporting guidance has been developed following a rigorous process in line with guideline development recommendations. The guidance is intended to improve the clarity and completeness of reporting of meta-ethnographies, and to facilitate use of the findings within the guidance to inform the design and delivery of services and interventions in health, social care and other fields. The eMERGe project developed a range of training materials to support use of the guidance, which is freely available at www.emergeproject.org (accessed 26 March 2018). Meta-ethnography is an evolving qualitative evidence synthesis methodology and future research should refine the guidance to accommodate future methodological developments. We will also investigate the impact of the eMERGe reporting guidance with a view to updating the guidance. Study registration This study is registered as PROSPERO CRD42015024709 for the stage 1 systematic review. Funding The National Institute for Health Research Health Services and Delivery Research programme.
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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 | 0.758 | 0.878 |
| Meta-epidemiology (narrow) | 0.007 | 0.012 |
| Meta-epidemiology (broad) | 0.011 | 0.029 |
| Bibliometrics | 0.030 | 0.028 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.014 | 0.016 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".