Abstract TP95: Stakeholder Perspectives Regarding Conventional and Alternative Funding Paradigms for Stroke Research: Results From the Perspect Qualitative Study
Bibliographic record
Abstract
Background: Stroke research is underfunded, but there is a paucity of data on the perspectives of researchers, funders, patients and the public about current funding paradigms. Understanding their priorities and opinions is important to guide meaningful innovation. Methods: The PERSPECT (Priorities & Expectations of Researchers, Donors, Patients and the Public Regarding the Funding & Conduct of Stroke Research) study involved in-depth, semi-structured one-on-one interviews with stroke researchers, funding organization representatives/philanthropists, patients, and members of the public. Participants were sampled using three axes (age, sex, ethnicity) to ensure diversity. They were asked to discuss thoughts about the state of stroke research funding and any potential or desired alternatives to current funding models. Qualitative analyses of transcripts included constant comparison and grounded theory content analysis. The study ended when the standard of thematic saturation was attained. Results: Forty-one interviews were completed (11 researchers, 10 research funders/philanthropists, 10 patients, 10 lay citizens). Researchers, philanthropists, patients, and public participants expressed a desire for greater transparency with grant funding processes, and voiced concerns that current models fostered bias towards certain topics and researchers. Patients and donors felt that conventional processes at times seemed disconnected from their interests. Crowdfunding was identified as an alternative strategy that could facilitate democratization in terms of topics studied, exploration of new frontiers, integration of diverse methods, and capacity building for less established researchers/centers. However, participants emphasized the importance of expert review, as in current processes, in building trust in proposal quality. They noted that successful crowdfunding strategies would require innovative approaches from researchers to promote their work. Conclusions: Our findings revealed stakeholder concerns about transparency and equity with current research funding paradigms. Stakeholders recognize crowdfunding as a useful alternative approach, but incorporation of expert review will be important to engender trust.
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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 | 0.033 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".