A roadmap to engaging patients in research: The experience of a large academic research hospital in Canada
Bibliographic record
Abstract
Recent definitions of patient engagement in research (PER) emphasize that engagement should be meaningful, active and an equal collaboration across the research continuum. The increased interest in patient engagement is predicated on the recognition by researchers of the unique experiential knowledge provided by individuals with lived experience, ethical obligations to democratize science and that patient involvement can potentially lead to improved outcomes for patients and researchers. Sunnybrook Health Sciences Center is a large academic research hospital in Toronto, Canada which aimed to create clearer pathways for patients to have a more prominent voice in the development, implementation, and dissemination of research. However, to ensure that the policies, practices and resources to support PER would be viewed as meaningful to all stakeholders (including, but not limited to, administrators, clinicians, clinician researchers, scientists, patients, family members and caregivers), a series of structured activities were undertaken to foster collective buy-in and co-create an operational implementation plan for PER. The activities consisted of a consecutive mixed methods approach of three phases of discovery: a survey, focus groups and interviews, and an in-person town hall. We describe our approach to implementation and operationalization of PER at an academic hospital based on five identified priority themes: education and training, partnerships, matching programs, policies and measures. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.045 | 0.019 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".