Facilitating and supporting the engagement of patients, families and caregivers in research: the “Ottawa model” for patient engagement in research
Bibliographic record
Abstract
BACKGROUND: Patient engagement is increasingly being recognized as a critical component of health research; however, institutional models for building infrastructure and capacity for patient engagement in research are limited. There is an opportunity to create reproducible and scalable models of patient engagement in research and share best and promising practices. MAIN BODY: In this article, we describe the development and features of the framework for the Ottawa Patient Engagement in Research Model at The Ottawa Hospital (TOH) and the Ottawa Hospital Research Institute (OHRI). Key components of the model include: a Patient and Family Engagement Program at TOH, which recruits, educates, and supports patients, families and caregivers to engage in clinical care, governance, and research; the Ottawa Methods Centre within the OHRI, which leads methodological research and provides support to investigators for patient engagement and patient-oriented research at TOH; and the Office of Patient Engagement in Research Activities, also within the OHRI, which facilitates collaborations between patients, researchers, clinicians and other stakeholders. Early success of this model can be attributed to aligned institutional priorities between TOH, OHRI and patients, the establishment of a patient engagement policy, ongoing education and support provided to patient partners and researchers, and innovative recruitment, tracking and evaluation procedures. Ongoing challenges and next steps include promoting diversity among patient partners, implementing an equitable compensation policy, engaging patients across a variety of roles and research areas, and developing resources to expand and sustain this program. CONCLUSION: This model represents a unique effort of patients, clinicians, researchers, and policymakers across disciplines and institutions to produce a harmonized strategy and infrastructure for meaningful collaboration with patients and families in health research, and capacity building in patient-oriented 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 | 0.140 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.054 |
| Scholarly communication | 0.027 | 0.017 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.008 | 0.014 |
| 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".