Establishing Effective Patient Engagement Through a Terms of Reference to Foster Inclusivity and Empowerment in Research: Example From a Healthcare Transition Quality Indicators Project
Bibliographic record
Abstract
INTRODUCTION: Patient engagement in research aims to foster meaningful partnerships, integrating patient experiences into the research process. This paper describes the development of a Terms of Reference (ToR) to support these meaningful partnerships. While engagement improves data collection and empowerment, ineffective engagement can lead to negative outcomes. A well-developed ToR promotes a structured, inclusive, and respectful process. METHODS: Using an integrated knowledge translation (iKT) approach, we established a panel of youth, caregivers, healthcare providers, and healthcare leaders/decision-makers. Through collaborative discussions, we incorporated key elements into the ToR, including values, roles, decision-making processes, and recognition of contributions. RESULTS: To promote effective engagement the ToR included sections to encourage open, transparent and vulnerable dialogue, evaluation, and accommodations for disabilities. The ToR draft was reviewed and refined by panel members for clarity. Regular reviews and updates will keep the ToR a living document and adaptable to the evolving engagement process. CONCLUSION: The implementation of our ToR is designed to foster inclusivity, mutual respect, and accountability, avoiding tokenistic partnership, enriching the experience for patients and researchers alike, and ultimately enhancing research quality.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".