Evolving Patient-Researcher Collaboration: An Illustrative Case Study of a Patient-Led Knowledge Translation Event
Bibliographic record
Abstract
Patient engagement occurs when patients actively collaborate in health research in ways that are meaningful to them. Resources to facilitate patient engagement have been developed, but their approach is mainly toward building competencies in the early stages of forming new practices of patient engagement. This paper describes a patient-led collaboration in rheumatology, in the context of an established patient-researcher partnership. Using a case study approach, we report on a research knowledge translation event, titled eROAR2013 (Reaching Out with Arthritis Research), led by members of the Arthritis Patient Advisory Board (APAB), which is a group of volunteer advocates living with arthritis based at Arthritis Research Canada. We provide an overview of APAB's decade-long history, describe the planning and the event itself, and report on the challenges encountered, reflections and solutions pertinent for sustaining patient-researcher collaborative practices.
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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 | 0.032 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.018 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".