Community-Based Public Health Vaccination Campaign (VaccinateLA) in Los Angeles’ Black and Latino Communities: Protocol for a Participatory Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,084 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,005 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,009 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,007 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,078 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».