Questioning patient engagement: research scientists’ perceptions of the challenges of patient engagement in a cardiovascular research network
Bibliographic record
Abstract
BACKGROUND: Patient engagement in research is a dominant discourse in clinical research settings as it is seen as a move toward sustainable and equitable health care systems. In Canada, a key driver is the Strategy for Patient-Oriented Research of the Canadian Institutes of Health Research, which asserts that meaningful patient engagement can only be fostered when stakeholders understand its value. This study assessed researchers' perceptions of the meaning and value of patient engagement in research within a Canadian cardiovascular research network. In doing so, the secondary aim was to inform the development of a structured patient engagement initiative by identifying potential challenges and related mitigation strategies. METHODS: We employed a multi-method strategy involving electronic surveys and semi-structured telephone interviews with network research scientists across Canada. Interview data were analyzed using thematic and content analysis. Survey data were analyzed using descriptive statistics. RESULTS: Thirty-eight electronic surveys (response rate =33%) and 16 interviews were completed with network members. Some participants were uncertain about the meaning and value of patient engagement. While voicing guarded support, four challenges relating to patient engagement were identified from the interviews: 1) identification of representative and appropriate patients, 2) uncertainty about the scope of patients' roles given concerns about knowledge discrepancies, 3) a perceived lack of evidence of the impact of patient engagement, and 4) the need for education and culture change as a prerequisite for patient engagement. Research scientists were largely concerned that patients untrained in science and tasked with conveying an authentic patient experience and being a conduit for the voices of others might unsettle a traditional model of conducting research. CONCLUSION: Concerns about patient involvement in research were related to a lack of clarity about the meaning, process, and impact of involvement. This study highlights the need for education on the meaning of patient engagement, evidence of its impact, and guidance on practical aspects of implementation within this research community.
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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 | 0.170 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.028 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".