Partnerships and Coalitions for Community-Based Research
Bibliographic record
Abstract
Address correspondence to Dr. Green, Office on Smoking and Health, CDC, 4770 Buford Hwy, MS K-50, Atlanta GA 30341-3717; tel. 770-488-5701; fax 770-488-5767; e-mail . WHAT HAVE SEVERAL DECADES OF HEALTH EDUCATION, PROMOTION, and engagement with community and academic partners taught us about community-based research in public health? We know that some lessons derive from specific studies,1,2 others from reviews of international research literature,3,4 and still others from guides that help practitioners apply their apparent lessons.5 This commentary blends the findings of these various studies, reviews, and guides with general principles and guidelines that have emerged from our combined experience and observa tions in academic, foundation, federal, state, and local situations in the United States, Canada, Australia, and other countries. Our comments center on community-based partnerships, coalitions, and infrastructure building, but we emphasize that horizontal commu nity coalitions and partnerships must be based on strong vertical rela tionships between local entities and their state and national counter parts or headquarter organizations. We assume that university-based researchers are often, but not necessarily or always, part of community based partnership. In order to answer our first question, we pose additional questions: Why is some partnering essential to community-based research? How much partnering is needed to facilitate the research, community planning, and execution of programs? What are the principles and components of good community partnerships, and how do they fit with the principles of participatory research and the particular demands of academic-community partnerships? What are some cautions for partnerships that become large coalitions? Finally, what lessons have the large community trials in chronic disease prevention taught us?
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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 | 0.128 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.030 | 0.035 |
| Open science | 0.005 | 0.056 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".