Engaging Community-Based Organizations to Address Barriers in Public Health Programs: Lessons Learned From COVID-19 Vaccine Acceptance Programs in Diverse Rural Communities
Bibliographic record
Abstract
Factors such as geography, community hesitancy, the political landscape, and legislative efforts to limit public health authority have contributed to a disproportionate number of COVID-19 infections and deaths in US rural communities. Community-based organizations are trusted entities that provide social and educational services in the communities where they live and have proven to be effective public health partners in response to the COVID-19 pandemic. Recognizing the unique challenges faced by rural communities, coupled with higher rates of vaccine hesitancy, the CDC Foundation awarded grants to 21 community-based organizations serving rural communities in 7 Midwest states to support the equitable uptake and distribution of COVID-19 vaccines. In this case study, 2 grantees, the Missouri Center for Public Health Excellence and the Hmong American Center, provide case studies that document their experiences, challenges, and strategies for overcoming barriers during the implementation of COVID-19 vaccine acceptance projects in diverse rural communities. These case studies provide key lessons learned that can be applied to future public health emergency and nonemergency responses to ensure that all members of communities are served well and protected.
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.
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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".