Dialogic Health Education to Reduce COVID-19 Disparities and Increase Health Literacy in Community and Correctional Settings: Protocol for a Two-Pronged Health Education Program
Notice bibliographique
Résumé
BACKGROUND: COVID-19 vaccines significantly reduce rates of hospitalization and death for those infected with the SARS-CoV-2 virus. Those facing social oppression, including people of color, experience heightened risk for COVID-19 and comorbidities, but are often mistrustful of governmental agencies and initiatives, contributing to low vaccine uptake and a reluctance to access vital health care services. Dialogue-based health literacy interventions may mitigate mistrust and increase access to health services and information, subsequently increasing rates of vaccination and other behaviors that reduce COVID-19 risk. OBJECTIVE: To improve health literacy and reduce COVID-19 disparities, the Westchester County Department of Health, in partnership with two universities, community- and faith-based organizations, and the Westchester County Department of Correction, co-developed a health education program for community members, correctional officers, and incarcerated jail residents in Westchester, New York. Specific objectives are to increase preventative health behaviors, positive attitudes toward use of public health protocols, full vaccination or intentions to vaccinate, health care information understanding, health provider care access, clear communication with health care providers, and personal health care decision-making. METHODS: Grounded in dialogic learning, the program entails training community-based "trusted messengers" and correctional officers to lead health information sessions in community and correctional settings. During the grant period, the program intends for 80 community-based trusted messengers to receive training from the Department of Health and will be expected to reach a goal of 100 members (N=8000) of their communities. Correctional staff with experience delivering educational programs will be trained to facilitate sessions among 400 correctional facility residents and 600 correctional staff. RESULTS: Pre-post surveys will assess changes in health behaviors, attitudes, and perceptions. The program has been administered in the correctional facility since February 2022, with information sessions expected to cease for correctional staff and residents in June 2022 and November 2022, respectively. An initial cohort of community-based trusted messengers began training in February 2022, and information sessions have been scheduled in various virtual and community settings since March 2022. As of April 2022, the two-pronged health education program has reached 439 correctional officers, 98 jail residents, and 201 community members countywide. Program evaluation findings will be released in future publications after study implementation is complete. CONCLUSIONS: Few studies have evaluated the combined effects of training-of-trainers (ToT) and dialogical learning models on behavior and health literacy. As the first known COVID-19-specific dialogue-based health education program that applies a ToT model in the community-based, correctional, and virtual settings simultaneously, this study fills a gap in current knowledge about health literacy and health behavior in marginalized populations. Thus, this evidence-based framework can remedy COVID-19 disparities while also addressing risks for a host of health-related issues at the community level, potentially serving as a best-practice model for future health programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37713.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,019 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,005 | 0,007 |
| Intégrité de la recherche | 0,007 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,043 | 0,005 |
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 ».