Building consensus in research partnerships: a scoping review of consensus methods
Bibliographic record
Abstract
Background: Research partnership approaches that engage community members within the research team (for example, integrated knowledge translation, community-based participatory research) are typically used to enhance the relevance and usefulness of research findings. However, research outcomes generated through partnered research do not de facto address the priorities of those most affected nor take inclusion or power dynamics into consideration. Consensus methods (for example, Delphi, Deliberative Dialogue) can be used to develop evidence-based solutions by addressing the groups’ needs and priorities. Limited research has examined how consensus methods are used by research partnerships. Aims and objectives: Using the PRISMA-ScR checklist as a guide, this scoping review sought to better understand the use of consensus methods in research partnerships. Methods: The search strategy involved four databases (MEDLINE, PsycINFO, EMBASE and CINAHL Plus). A total of 6,654 citations were screened, 404 were advanced for full text review, and 34 studies met eligibility criteria. Data from the 34 studies were extracted and iteratively analysed by three members of our research team. Findings: At least 11 different consensus methods were used with variations of the Delphi being most common. Issues of inclusion and power dynamics were rarely discussed. Overall, there was limited reporting of consensus methods, partnership approaches, and/or power dynamics. Discussion and conclusions: This review extends the literature by providing an overview of consensus methods that have been conducted in research partnerships and how they have been executed. We offer initial considerations for conducting and reporting on the use of consensus methods in research co-production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.376 | 0.551 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.064 | 0.064 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".