Partnerships and Coalitions for Community-Based Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it