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 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 | 0.302 | 0.494 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".