Long‐term views on chronic kidney disease research priorities among stakeholders engaged in a priority‐setting partnership: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Patients and stakeholders are increasingly engaging in health research to help address evidence-practice gaps and improve health-care delivery. We previously engaged patients, caregivers, health-care providers (HCPs) and policymakers in identifying priorities for chronic kidney disease (CKD) research. OBJECTIVE: We aimed to explore participants' views on the research priorities and prioritization process 2 years after the exercise took place. DESIGN: In this qualitative descriptive study, individual interviews were conducted and analysed using an inductive, thematic analysis approach. SETTING/PARTICIPANTS: Participants resided across Canada. We purposively sampled across stakeholder groups (CKD patients, caregivers, HCPs and policymakers) and types of engagement (wiki, workshop and/or steering committee) from the previous CKD priority-setting project. RESULTS: Across 23 interviews, participants discussed their research priorities over time, views on the prioritization process and perceived applicability of the priorities. Even though their individual priorities may have changed, participants remained in agreement overall with the previously identified priorities, and some perceived a distinction between patient and HCP priorities. They tended to balance individual priorities with their broader potential impact and viewed the prioritization process as systematic, collaborative and legitimate. However, participants acknowledged challenges to applying the priorities and emphasized the importance of communicating the project's outcomes upon its completion. CONCLUSION: Two years after engaging in CKD research prioritization, stakeholder participants remained in agreement with the previously identified priorities, which they felt reflected group deliberation and consensus. Rapport and communication were highlighted as key elements supporting effective engagement in research prioritization.
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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 | 0.057 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".