Participatory methods for research prioritization in primary care: an analysis of the World Café approach in Ireland and the USA
Bibliographic record
Abstract
Background: There are increasing imperatives for patients and members of the public to engage as partners in identifying health research priorities. The use of participatory methods to engage stakeholders in health care in research prioritization is not commonly reported. Objective: This article analyses the use of World Cafés as a participatory method for research prioritization with marginalized communities in Ireland and the USA. Methods: The principles of purposeful and snowball sampling were followed in both settings and a diverse range of community and health care stakeholders participated (n = 63 Ireland and n = 55 USA). The principles for a classic World Café were employed but there were novel features in each setting as well. Stewart et al.'s (Patients' and clinicians' research priorities. Health Expect 2011; 14: 439-48, conceptual framework for patient engagement was adapted and used to comparatively analyse the strengths and weaknesses of the World Cafés, focusing on agenda setting, engagement with research processes, interactional features and outputs. Results: Design principles for World Cafés were found to align with high-quality patient engagement for research prioritization in both settings. They served to facilitate meaningful collaboration among stakeholder groups in research prioritization (research agenda setting) and explored research priorities (engagement with research). The café ambience, emphasis on hospitality and self-facilitation created an environment for dialogues within and across participating groups (interactional features). There was a commitment to follow-up actions with reference to possible subsequent research (outputs). Conclusions: The World Café is a valuable, participatory, flexible method that can be used with community and health care stakeholders for research prioritization with marginalized communities.
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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 | 0.103 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".