Members of Minority and Underserved Communities Set Priorities for Health Research
Bibliographic record
Abstract
Policy Points Engaging and involving underrepresented communities when setting research priorities could make the scientific research agenda more equitable, more just, and more responsive to their needs and values. Groups and individuals from minority and underserved communities strongly prioritized child health and mental health research, often choosing to invest at the highest possible level. Groups consisting of predominantly Native American or Arab American participants invested in culture and beliefs research at the highest level, while many groups did not select it at all. The priority given to culture and beliefs research by these groups illustrates the importance of paying special attention to unique preferences, and not just commonly held views, when getting public input on spending priorities for research. CONTEXT: A major contributor to health disparities is the relative lack of resources-including resources for science-allocated to address the health problems of those with disproportionately greater needs. Engaging and involving underrepresented communities in setting research priorities could make the scientific research agenda more equitable, more just, and more responsive to their needs and values. We engaged minority and underserved communities in informed deliberations and report here their priorities for health research. METHODS: Academic-community partnerships adapted the simulation exercise CHAT for setting health research priorities. We had participants from minority and medically underserved communities (47 groups, n = 519) throughout Michigan deliberate about health research priorities, and we used surveys and CHAT software to collect the demographic characteristics and priorities selected by individuals and groups. FINDINGS: The participants ranged in age (18 to 88), included more women than men, and were overrepresented by minority groups. Nearly all the deliberating groups selected child health and mental health research (93.6% and 95.7%), and most invested at the highest level. Aging, access, promote health, healthy environment, and what causes disease were also prioritized by groups. Research on mental health and child health were high priorities for individuals both before and after group deliberations. Access was the only category more likely to be selected by individuals after group deliberation (77.0 vs 84.0%, OR = 1.63, p = .005), while improve research, health policy, and culture and beliefs were less likely to be selected after group deliberations (all, p < .001). However, the level of investment in many categories changed after the group deliberations. Participants identifying as Black/African American were less likely to prioritize mental health research, and those of Other race were more likely to prioritize culture and beliefs research. CONCLUSIONS: Minority and medically underserved communities overwhelmingly prioritized mental health and child health research in informed deliberations about spending priorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".