RAMESES II reporting standards for realist evaluations
Bibliographic record
Abstract
BACKGROUND: Realist evaluation is increasingly used in health services and other fields of research and evaluation. No previous standards exist for reporting realist evaluations. This standard was developed as part of the RAMESES II project. The project's aim is to produce initial reporting standards for realist evaluations. METHODS: We purposively recruited a maximum variety sample of an international group of experts in realist evaluation to our online Delphi panel. Panel members came from a variety of disciplines, sectors and policy fields. We prepared the briefing materials for our Delphi panel by summarising the most recent literature on realist evaluations to identify how and why rigour had been demonstrated and where gaps in expertise and rigour were evident. We also drew on our collective experience as realist evaluators, in training and supporting realist evaluations, and on the RAMESES email list to help us develop the briefing materials. Through discussion within the project team, we developed a list of issues related to quality that needed to be addressed when carrying out realist evaluations. These were then shared with the panel members and their feedback was sought. Once the panel members had provided their feedback on our briefing materials, we constructed a set of items for potential inclusion in the reporting standards and circulated these online to panel members. Panel members were asked to rank each potential item twice on a 7-point Likert scale, once for relevance and once for validity. They were also encouraged to provide free text comments. RESULTS: We recruited 35 panel members from 27 organisations across six countries from nine different disciplines. Within three rounds our Delphi panel was able to reach consensus on 20 items that should be included in the reporting standards for realist evaluations. The overall response rates for all items for rounds 1, 2 and 3 were 94 %, 76 % and 80 %, respectively. CONCLUSION: These reporting standards for realist evaluations have been developed by drawing on a range of sources. We hope that these standards will lead to greater consistency and rigour of reporting and make realist evaluation reports more accessible, usable and helpful to different stakeholders.
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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 | 0.673 | 0.826 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.028 | 0.018 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.015 | 0.023 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".