The role of funded partnerships in working towards decreasing COVID-19 vaccination disparities, United States, March 2021—December 2022
Bibliographic record
Abstract
During the COVID-19 vaccination rollout from March 2021- December 2022, the Centers for Disease Control and Prevention funded 110 primary and 1051 subrecipient partners at the national, state, local, and community-based level to improve COVID-19 vaccination access, confidence, demand, delivery, and equity in the United States. The partners implemented evidence-based strategies among racial and ethnic minority populations, rural populations, older adults, people with disabilities, people with chronic illness, people experiencing homelessness, and other groups disproportionately impacted by COVID-19. CDC also expanded existing partnerships with healthcare professional societies and other core public health partners, as well as developed innovative partnerships with organizations new to vaccination, including museums and libraries. Partners brought COVID-19 vaccine education into farm fields, local fairs, churches, community centers, barber and beauty shops, and, when possible, partnered with local healthcare providers to administer COVID-19 vaccines. Inclusive, hyper-localized outreach through partnerships with community-based organizations, faith-based organizations, vaccination providers, and local health departments was critical to increasing COVID-19 vaccine access and building a broad network of trusted messengers that promoted vaccine confidence. Data from monthly and quarterly REDCap reports and monthly partner calls showed that through these partnerships, more than 295,000 community-level spokespersons were trained as trusted messengers and more than 2.1 million COVID-19 vaccinations were administered at new or existing vaccination sites. More than 535,035 healthcare personnel were reached through outreach strategies. Quality improvement interventions were implemented in healthcare systems, long-term care settings, and community health centers resulting in changes to the clinical workflow to incorporate COVID-19 vaccine assessments, recommendations, and administration or referrals into routine office visits. Funded partners' activities improved COVID-19 vaccine access and addressed community concerns among racial and ethnic minority groups, as well as among people with barriers to vaccination due to chronic illness or disability, older age, lower income, or other factors.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".