Reaching consensus on reporting patient and public involvement (PPI) in research: methods and lessons learned from the development of reporting guidelines
Bibliographic record
Abstract
INTRODUCTION: Patient and public involvement (PPI) is inconsistently reported in health and social care research. Improving the quality of how PPI is reported is critical in developing a higher quality evidence base to gain a better insight into the methods and impact of PPI. This paper describes the methods used to develop and gain consensus on guidelines for reporting PPI in research studies (updated version of the Guidance for Reporting Patient and Public Involvement (GRIPP2)). METHODS: There were three key stages in the development of GRIPP2: identification of key items for the guideline from systematic review evidence of the impact of PPI on health research and health services, a three-phase online Delphi survey with a diverse sample of experts in PPI to gain consensus on included items and a face-to-face consensus meeting to finalise and reach definitive agreement on GRIPP2. Challenges and lessons learnt during the development of the reporting guidelines are reported. DISCUSSION: The process of reaching consensus is vital within the development of guidelines and policy directions, although debate around how best to reach consensus is still needed. This paper discusses the critical stages of consensus development as applied to the development of consensus for GRIPP2 and discusses the benefits and challenges of consensus development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".