Patient Engagement in Health Research: Perspectives from Patient Participants
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Over the past decade, patient engagement (PE) has emerged as an important way to help improve the relevance, quality, and impact of health research. However, there is limited consensus on how best to meaningfully engage patients in the research process. The goal of this article is to share our experiences and insights as members of a Patient Advisory Committee (PAC) on a large, multidisciplinary cancer research study that has spanned six years. We hope by sharing our reflections of the PAC experiences, we can highlight successes, challenges, and lessons learned to help guide PE in future health research. To the best of our knowledge, few publications describing PE experiences in health research teams have been written by patients, survivors, or family caregivers themselves. METHODS: A qualitative approach was used to gather reflections from members of the Patient Advisory Committee regarding their experiences in participating in a research study over six years. Each member completed an online survey and engaged in a group discussion based on the emergent themes from the survey responses. RESULTS: Our reflections about experiences as a PAC on a large, pan-Canadian research study include three overarching topics (1) what worked well; (2) areas for improvement; and (3) reflections on our overall contribution and impact. Overall, we found the experience positive and experienced personal satisfaction but there were areas where future improvements could be made. These areas include earlier engagement and training in the research process, more frequent communication between the patient committee and the research team, and on-going monitoring regarding the nature of the patient engagement. CONCLUSIONS: Engaging individuals who have experienced the types of events which are the focus of a research study can contribute to the overall relevance of the project. However, intentional efforts are necessary to ensure satisfactory involvement.
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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 | 0.050 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".