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
Bibliographic record
Abstract
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.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".