Priorities and expectations of researchers, funders, patients and the public regarding equity in medical 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 broad stakeholder perspectives and priorities regarding inequities in medical research and funding, including implications for international collaborations with low-and middle-income countries (LMICs). METHODS: Participants were recruited through purposive and snowball sampling. 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 medical research and 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 within research participation, funding opportunities, topic prioritization, and lack of international collaborations inclusive of 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. While we gained diverse perspectives from four distinct stakeholder groups, our primary limitation was that participants in our study were predominantly from Canada and the United States. CONCLUSIONS: Participants perceived various inequities in the funding and conduct of medical research. Our findings were primarily captured from participants living in Canada and the United States. However, we were able to gain insights of challenges and potential solutions through their diverse perspectives, and we are optimistic that sustaining efforts to mitigate medical research and funding inequities will help accelerate and broaden the societal impact of medical research within and across countries, including in 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 | 0.100 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".