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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".