A Framework to Ensure Patient Partners Have Equal and Contributing Voices Throughout the Research Program Evaluation Process
Bibliographic record
Abstract
Purpose of program:Traditionally, peer review was a closed process conducted only by individuals working in the research field. To establish a more integrated and patient-centered approach, one of Canada’s largest kidney research networks (Can-SOLVE CKD) has created a Research Operations Committee (ROC) that includes patients as key members. The ROC represents one way for achieving meaningful patient-oriented research (POR).Source of information:Can-SOLVE CKD, a network created as part of the Canadian Institutes of Health Research (CIHR) Strategy for Patient-Oriented Research (SPOR).Methods:The ROC consists of patients, physicians, scientists, Indigenous partners, experts in research methodology, and a member of Can-SOLVE CKD’s operational team. On an annual basis, Can-SOLVE CKD’s research teams provide the ROC with a review package, which incorporates information from patient engagement check-in calls and surveys, the project’s knowledge translation plan and products, and a progress report written by the project team. The ROC evaluates the review package and provides feedback and recommendations accordingly.Key findings:The transparent nature of the process, regular feedback and review, along with an overt accountability and scoring system, has been embraced by both patients and researchers. As a result of the ROC process, the number of patient leads for each project has grown over a 3-year period and more researchers have received POR and cultural sensitivity training.Limitations:While anecdotal evidence suggests this approach is beneficial for achieving POR, formal mechanisms of evaluation are currently lacking.Implications:This ROC framework ensures patients are active contributors throughout the research process and could be adopted by other organizations to achieve a more patient-centered approach to research.
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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 | 0.781 | 0.604 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.029 | 0.050 |
| Scholarly communication | 0.054 | 0.045 |
| Open science | 0.018 | 0.052 |
| Research integrity | 0.027 | 0.040 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".