Leveraging mHealth to Mitigate the Impact of COVID-19 in Black American Communities: Qualitative Analysis
Bibliographic record
Abstract
Background COVID-19 remains an ongoing public health crisis. Black Americans remain underrepresented among those vaccinated and overrepresented in both COVID-19 morbidity and mortality. Medical misinformation, specifically related to COVID-19, has exacerbated the impact of the disease in Black American communities. Communication tools and strategies to build relationships and disseminate credible and trustworthy diagnostic and preventative health information are necessary to improve outcomes and equity for historically oppressed populations. Objective As the initial phase of a larger mixed methods project to develop, pilot, and evaluate a mobile health (mHealth) intervention among a population at high risk for COVID-19 and cardiovascular comorbidities, this study sought to explore COVID-19 information behavior among Black Americans. Specifically, this study examined (1) preferences for COVID-19 education via mHealth, (2) barriers and facilitators to COVID-19 education and diagnostic testing and routine care for associated cardiovascular and respiratory comorbidities in the local community, and (3) key content for inclusion in a COVID-19 mHealth app. Methods This qualitative study used principles of community-based participatory research and information systems research to conduct 7 focus groups across 3 sites. Focus groups were audio recorded and transcribed for thematic analysis using an abductive approach. Results The study sample included 54 individuals across sites with a mean age of 50.24 (SD 11.76; range 20-71) years. Participants were primarily female (n=42, 78%) and Black (n=54, 100%) with varied education levels. Over half (n=29, 54%) of the participants were employed full-time, and nearly three-fourths (n=40, 74%) had household incomes Conclusions Increasing transparency and building trust are 2 key strategies that may improve the impact of health information messaging in Black communities. Focusing on content over context fails in the provision of critical health information and perpetuates health inequities by reinforcing systemic and structural racism. COVID-19 messaging must consider contextual information, patient needs and preferences, and patient information-seeking and information-search behaviors to establish trust and credibility, positively impact patient health outcomes, and improve health equity.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".