Development of a Theory-Based, Culturally Appropriate Message Library for Use in Interventions to Promote COVID-19 Vaccination Among African Americans: Formative Research
Bibliographic record
Abstract
BACKGROUND: Disparities in COVID-19 incidence, hospitalization, and mortality rates among African Americans suggest the need for targeted interventions. Use of targeted, theory-driven messages in behavioral and communication interventions could empower African Americans to engage in behaviors that prevent COVID-19. OBJECTIVE: To address this need, we performed a formative study that aimed to develop and design a culturally appropriate, theory-based library of messages targeting concerns around COVID-19 vaccines that could be used in behavioral and communication interventions for African Americans. METHODS: Message development occurred between January 2021 and February 2022. Initial messages were designed by a multidisciplinary team of researchers, community leaders, and community members. Kreuter's 5 strategies (ie, linguistic, peripheral, evidential, sociocultural, and constituent-involving strategies) were used to achieve cultural appropriateness. After forming a community-academic partnership, message development occurred in 4 phases: (1) adaptation of a message library using the literature, (2) review by 6 clinical and research experts for content validation, (3) input and review by a 6-member community advisory panel (CAP), and (4) message pretesting with African Americans via semistructured interviews in a qualitative study. RESULTS: Themes from the semistructured interviews among 30 African Americans were as follows: (1) community reactions to the messages, (2) community questions and information needs, (3) suggestions for additional content, and (4) suggestions to improve comprehension, relevance, and trustworthiness. Feedback from the CAP, community members, and scientific experts was used by members of the community-academic partnership to iteratively update message content to maximize cultural appropriateness. The final message library had 18 message subsets for adults and 17 message subsets for parents and caregivers of children. These subsets were placed into 3 categories: (1) vaccine development, (2) vaccine safety, and (3) vaccine effectiveness. CONCLUSIONS: We used a 4-phase, systematic process using multiple community engagement approaches to create messages for African Americans to support interventions to improve COVID-19 vaccination rates among adults and children. The newly developed messages were deemed to be culturally appropriate according to experts and members of the African American community. Future research should evaluate the impact of these messages on COVID-19 vaccination rates among African Americans.
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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 | 0.094 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".