Improving reporting of meta‐ethnography: The <scp>eMERG</scp> e reporting guidance
Bibliographic record
Abstract
The aim of this study was to provide guidance to improve the completeness and clarity of meta‐ethnography reporting. Evidence‐based policy and practice require robust evidence syntheses which can further understanding of people's experiences and associated social processes. Meta‐ethnography is a rigorous seven‐phase qualitative evidence synthesis methodology, developed by Noblit and Hare. Meta‐ethnography is used widely in health research, but reporting is often poor quality and this discourages trust in and use of its findings. Meta‐ethnography reporting guidance is needed to improve reporting quality. The eMERG e study used a rigorous mixed‐methods design and evidence‐based methods to develop the novel reporting guidance and explanatory notes. The study, conducted from 2015 to 2017, comprised of: (1) a methodological systematic review of guidance for meta‐ethnography conduct and reporting; (2) a review and audit of published meta‐ethnographies to identify good practice principles; (3) international, multidisciplinary consensus‐building processes to agree guidance content; (4) innovative development of the guidance and explanatory notes. Recommendations and good practice for all seven phases of meta‐ethnography conduct and reporting were newly identified leading to 19 reporting criteria and accompanying detailed guidance.The bespoke eMERG e Reporting Guidance, which incorporates new methodological developments and advances the methodology, can help researchers to report the important aspects of meta‐ethnography. Use of the guidance should raise reporting quality. Better reporting could make assessments of confidence in the findings more robust and increase use of meta‐ethnography outputs to improve practice, policyand service user outcomes in health and other fields. This is the first tailored reporting guideline for meta‐ethnography. This article is being simultaneously published in the following journals: Journal of Advanced Nursing, Psycho‐oncology, Review of Education, PLoS One and BMC Medical Research Methodology .
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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 | 0.730 | 0.929 |
| Meta-epidemiology (narrow) | 0.008 | 0.013 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.040 | 0.050 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.012 | 0.017 |
| Research integrity | 0.026 | 0.019 |
| Insufficient payload (model declined to judge) | 0.035 | 0.014 |
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".