Abstract TP111: Attitudes and Perceptions Regarding Patient Engagement and Knowledge Translation in Stroke Research: Results From the PERSPECT Qualitative Study
Bibliographic record
Abstract
Background: Patient engagement and knowledge translation are increasingly recognized as critical components of stroke research. However, it is unclear how these components are currently perceived by interested parties. Methods: The PERSPECT (Priorities and Expectations of Researchers, Donors, Patients, and the Public Regarding the Funding and Conduct of Stroke Research) qualitative study involved in-depth, semi-structured interviews with patients, members of the general public, researchers, and donors, including board members of funding organizations and philanthropists. Participants were asked to discuss their thoughts about the role of patient and public engagement in stroke research and on the translation/dissemination of research findings. Collected data were analyzed through constant comparison and theme identification, followed by grounded theory content analysis. Results: Forty-one interviews were completed (11 with researchers and 10 for each patients, public and donors). An important theme that emerged was a need to re-evaluate and broaden conventional definitions of “expertise” in the context of selecting grant application review panels, recognizing the value of lived experience alongside that of education and professional experience. Whereas participants identified an important role for patient voices in guiding research direction and design; concerns were raised about the limited public awareness and understanding of medical research, which may hinder meaningful, advantageous involvement in the research process, as well as trust in evidence-based recommendations. In this regard, lay participants highlighted media-driven misinformation and a lack of credible lay-language resources as impeding effective dissemination of research findings. Conclusions: All four participant groups recognized the importance of knowledge end-users being engaged in research from its early stages, including decision-making about funding allocation. Impactful involvement seems however hindered by knowledge and communication gaps between the research and lay communities. Our results underscore the importance of efforts to make ongoing research and study findings more visible, accessible, and comprehensible.
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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 | 0.031 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".