Abstract TP134: Priorities and expectations of researchers, funders, patients and the public regarding equity in stroke research and funding: Results from the PERSPECT qualitative study
Bibliographic record
Abstract
Background: Considerations of equity in funding and conduct of medical research are receiving greater attention. However, perspectives of diverse stakeholder groups on this topic are poorly characterized. Our study aimed to further understand various stakeholder perspectives and associated priorities regarding perceived inequities in medical research, with a particular interest in the field of stroke. Methods: We employed a qualitative descriptive methodology embedded in an interpretive grounded theory framework. This approach involved in-depth, semi-structured interviews with researchers, funders, patients, and members of the public. Participants were asked to discuss their perspectives on the current state of equity in stroke and medical research funding. Collected data were analyzed using constant comparison, open-coding, and theme identification to generate a substantive theory. Results: We conducted 41 interviews involving 11 researchers, 10 funders, 10 patients, and 10 members of the public. Participants perceived several inequities in research participation, funding opportunities, topic prioritization, and lack of international collaborations inclusive of low- and middle-income countries (LMICs). Potential strategies to address these inequities were also identified. Through participants’ perspectives, we developed a central theory that addressing inequities in medical research and funding can promote collaborative spaces and produce greater research impact for society, regardless of demographics, socioeconomic status, and geographical residence. Conclusion: Participants perceived various inequities in the funding and conduct of medical research. However, based on the insights into potential solutions that we gained from their diverse perspectives, we are optimistic that addressing these inequities will help broaden the societal impact of stroke research and that these solutions will also result in more equitable outcomes and impact, inclusive of LMICs.
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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 | 0.041 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".