Community-Based Public Health Vaccination Campaign (VaccinateLA) in Los Angeles’ Black and Latino Communities: Protocol for a Participatory Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has significantly affected Los Angeles County and disproportionately impacted Black and Latino populations who experienced disparities in rates of infection, hospitalizations, morbidity, and mortality. The University of Southern California (USC), USC Keck School of Medicine, Southern California Clinical and Translational Science Institute, USC Mann School of Pharmacy and Pharmaceutical Sciences, Annenberg School for Journalism and Communication, and Children's Hospital Los Angeles will launch a collaborative public health campaign called VaccinateLA. OBJECTIVE: VaccinateLA will implement a community-based, community-partnered public health campaign that (1) delivers culturally tailored information about COVID-19 and available vaccines; and (2) addresses misinformation and disinformation, which serves as a barrier to vaccine uptake. The campaign will be targeted to communities in Los Angeles with the highest rates of COVID-19 infection and the lowest vaccination rates. Using these criteria, the campaign will be targeted to neighborhoods located in 34 zip codes in the Eastside and South Los Angeles. The primary aim of VaccinateLA will be to design and deliver an evidence-based multimedia public health campaign tailored for Black and Latino populations. A secondary aim will be to train and deploy community vaccine navigators to deliver COVID-19 education, help individuals overcome barriers to getting vaccinated (eg, transportation and challenges registering), and assist with delivering vaccinations in our targeted communities. METHODS: We will use a community-based, participatory research approach to shape VaccinateLA's public health campaign to address community members' attitudes and concerns in developing campaign content. We will conduct focus groups, establish a community advisory board, and engage local leaders and stakeholders to develop and implement a broad array of educational, multimedia, and field-based activities. RESULTS: As of February 2023, target communities have been identified. The activities will be initiated and evaluated over the course of this year-long initiative, and dissemination will occur following the completion of the project. CONCLUSIONS: Engaging the community is vital to developing culturally tailored public health messages that will resonate with intended audiences. VaccinateLA will serve as a model for how an academic institution can quickly mobilize to address a pressing public health crisis, particularly in underrepresented and underresourced communities. Our work has important implications for future public health campaigns. By leveraging community partnerships and deploying community health workers or promotores into the community, we hope to demonstrate that urban universities can successfully partner with local communities to develop and deliver a range of culturally tailored educational, multimedia, and field-based activities, which in turn may change the course of an urgent public health crisis, such as the COVID-19 pandemic. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40161.
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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 | 0.084 | 0.049 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.078 | 0.014 |
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".