Evaluating community deliberations about health research priorities
Bibliographic record
Abstract
CONTEXT: Engaging underrepresented communities in health research priority setting could make the scientific agenda more equitable and more responsive to their needs. OBJECTIVE: Evaluate democratic deliberations engaging minority and underserved communities in setting health research priorities. METHODS: Participants from underrepresented communities throughout Michigan (47 groups, n = 519) engaged in structured deliberations about health research priorities in professionally facilitated groups. We evaluated some aspects of the structure, process, and outcomes of deliberations, including representation, equality of participation, participants' views of deliberations, and the impact of group deliberations on individual participants' knowledge, attitudes, and points of view. Follow-up interviews elicited richer descriptions of these and also explored later effects on deliberators. RESULTS: Deliberators (age 18-88 years) overrepresented minority groups. Participation in discussions was well distributed. Deliberators improved their knowledge about disparities, but not about health research. Participants, on average, supported using their group's decision to inform decision makers and would trust a process like this to inform funding decisions. Views of deliberations were the strongest predictor of these outcomes. Follow-up interviews revealed deliberators were particularly struck by their experience hearing and understanding other points of view, sometimes surprised at the group's ability to reach agreement, and occasionally activated to volunteer or advocate. CONCLUSIONS: Deliberations using a structured group exercise to engage minority and underserved community members in setting health research priorities met some important criteria for a fair, credible process that could inform policy. Deliberations appeared to change some opinions, improved some knowledge, and were judged by participants worth using to inform policymakers.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".