Priorities and expectations of researchers, funders, patients and the public regarding the funding of medical research: results from the PERSPECT qualitative study
Bibliographic record
Abstract
BACKGROUND: Ideally, medical research provides crucial data about disease processes, diagnoses, prognoses, treatment targets and outcomes, and systems of care. However, medical research is costly, and funding is difficult to receive because the processes are highly competitive. There is a paucity of data on the perspectives of researchers, funders, patients and the public about current funding paradigms. This study sought to understand the priorities and opinions of each group to better guide meaningful innovation in research funding processes. METHOD: In this Priorities & Expectations of Researchers, Funders, Patients and the Public Regarding the Funding & Conduct of Stroke Research study, we conducted in-depth interviews with medical researchers, funders, patients and members of the general public to learn their opinions of the current funding process and thoughts about alternative approaches. We used both purposive and snowball sampling to recruit participants and conducted semistructured interviews. The study ended when thematic saturation was attained. Qualitative analysis followed inductive grounded theory methodology. RESULTS: 41 interviews were completed (11 researchers, 10 funders, 10 patients, 10 members of the general public; 61% female). Interviewees expressed a high interest in supporting a comprehensive evaluation of the research grant funding process while integrating funding mechanisms that are more inclusive and reduce bias in topic selection and researchers who receive funds. Participants acknowledged a gap in patient and public involvement in setting a research agenda, choosing topics to be studied and focusing on specific outcomes. Crowdfunding was identified as an alternative strategy that could facilitate research democratisation; however, participants emphasised the importance of expert review of research proposals, as in current processes to continue to support rigour and trust in research proposal quality. CONCLUSION: Our research revealed stakeholder concerns about the transparency and equity of current research funding paradigms. Suggestions to democratize research and explore alternative fundraising platforms necessitate a fundamental shift in traditional research funding processes.
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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 | 0.119 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".