Factors to Consider During Identification and Invitation of Individuals in a Multi-stakeholder Research Partnership
Bibliographic record
Abstract
BACKGROUND: Health research teams increasingly partner with stakeholders to produce research that is relevant, accessible, and widely used. Previous work has covered stakeholder group identification. OBJECTIVE: We aimed to develop factors for health research teams to consider during identification and invitation of individual representatives in a multi-stakeholder research partnership, with the aim of forming equitable and informed teams. DESIGN: Consensus development. PARTICIPANTS: We involved 16 stakeholders from the international Multi-Stakeholder Engagement (MuSE) Consortium, including patients and the public, providers, payers of health services/purchasers, policy makers, programme managers, peer review editors, and principal investigators. APPROACH: We engaged stakeholders in factor development and as co-authors of this manuscript. Using a modified Delphi approach, we gathered stakeholder views concerning a preliminary list of 18 factors. Over two feedback rounds, using qualitative and quantitative analysis, we concentrated these into ten factors. KEY RESULTS: We present seven highly desirable factors: 'expertise or experience', 'ability and willingness to represent the stakeholder group', 'inclusivity (equity, diversity and intersectionality)', 'communication skills', 'commitment and time capacity', 'financial and non-financial relationships and activities, and conflict of interest', 'training support and funding needs'. Additionally, three factors are desirable: 'influence', 'research relevant values', 'previous stakeholder engagement'. CONCLUSIONS: We present factors for research teams to consider during identification and invitation of individual representatives in a multi-stakeholder research partnership. Policy makers and guideline developers may benefit from considering the factors in stakeholder identification and invitation. Research funders may consider stipulating consideration of the factors in funding applications. We outline how these factors can be implemented and exemplify how their use has the potential to improve the quality and relevancy of health research.
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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, not a consensus.
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".