Patient Engagement in a Canadian Health Research Funding Institute: Implementation and Impact
Bibliographic record
Abstract
Background: Patient engagement (PE) or involvement in research is when patient partners are integrated onto teams and initiatives (not participants in research). A number of health research funding organizations have PE frameworks or rubrics but we are unaware of them applying and reporting on their own PE efforts. We describe our work at the Canadian Institutes of Health Research’s Institute of Musculoskeletal Health and Arthritis (CIHR IMHA) to implement, evaluate and understand the impact of its PE strategy.Methods: The Institute hired a PE specialist (who identifies as a patient partner) to design the strategy, its tactics, and timelines. A Patient Engagement Research Ambassador (PERA) group was convened of eight patient partners who lived with conditions represented by the Institute that meet monthly, co-created a mandate and terms of reference, and set priorities. Evaluating the PE strategy and understanding its impact was a collaboration between an external group and PERA. Results: In addition to convening PERA, the Institute produced a number of outputs (modules, video, publications, webinars, blog) to help in doing PE in research. One major output was a How-To Guide to Patient Engagement in Research entirely driven and designed by PERA. The How-To Guide is a series of free, virtual modules for different audiences used by 1,048 individuals to date. The evaluation and impacts of the PE strategy revealed positive impacts with some areas for improvement.Conclusions: Implementing a PE strategy within CIHR IMHA resulted in several PE activities and outputs with impacts within and beyond the Institute. We provide templates and outputs related to this work that may inform the efforts of other health research funding organizations. We encourage health research funders to move beyond encouraging or requiring PE in funded projects to fully ‘walk the talk’ of PE by implementing and evaluating their own PE strategies.
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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 | 0.153 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".