Making Agency Visible: Patient Perspectives on Engagement in Cardiovascular Research
Bibliographic record
Abstract
Objectives: To examine how cardiovascular patients perceive and enact agency in research partnerships, including decision-making, communication, health literacy, and sustained engagement.Methods: This qualitative study involved semi-structured interviews with 14 patient partners who participated in a national Masterclass on cardiovascular research. The Masterclass was designed to strengthen patient capacity for research involvement through education, mentorship, and collaborative activities. Interview transcripts were analyzed thematically, guided by the Patient Agency in Research (PAIR) framework, which conceptualizes agency as intentional, self-reflective action expressed through individual, proxy, and collective modes.Results: Participants reported increased confidence, health research knowledge, and intentionality in their roles as research partners. They described navigating power imbalances, confronting tokenism, and advocating for more inclusive and responsive research cultures. Key findings included self-directed learning, empowerment, psychological safety, and the critical role of trust, respect, and transparent communication in fostering meaningful collaboration. Despite challenges, participants emphasized the value of structured support in helping them move from passive participants to active contributors.Conclusion: Agency is a core element of meaningful and sustained patient engagement in research. However, it does not arise automatically—it must be deliberately cultivated through educational interventions, inclusive practices, and ongoing relational support.Practice implications: Programs like the Masterclass can activate and strengthen patient agency, enabling individuals with lived experience to shape research in ways that are personally meaningful and scientifically impactful. By investing in patient capacity and fostering equitable research environments, institutions can move engagement beyond tokenism and toward partnerships that reshape the purpose—and the practice—of health 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 | 0.036 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".