Eliciting Opinions on Health Messaging During the COVID-19 Pandemic: Qualitative Survey Study
Bibliographic record
Abstract
BACKGROUND: Effective public health messaging has been necessary throughout the COVID-19 pandemic, but stakeholders have struggled to communicate critical information to the public, especially in different types of locations such as urban and rural areas. OBJECTIVE: This study aimed to identify opportunities to improve COVID-19 messages for community distribution in rural and urban settings and to summarize the findings to inform future messaging. METHODS: We purposively sampled by region (urban or rural) and participant type (general public or health care professional) to survey participants about their opinions on 4 COVID-19 health messages. We designed open-ended survey questions and analyzed the data using pragmatic health equity implementation science approaches. Following the qualitative analysis of the survey responses, we designed refined COVID-19 messages incorporating participant feedback and redistributed them via a short survey. RESULTS: In total, 67 participants consented and enrolled: 31 (46%) community participants from the rural Southeast Missouri Bootheel, 27 (40%) community participants from urban St Louis, and 9 (13%) health care professionals from St Louis. Overall, we found no qualitative differences between the responses of our urban and rural samples to the open-ended questions. Participants across groups wanted familiar COVID-19 protocols, personal choice in COVID-19 preventive behaviors, and clear source information. Health care professionals contextualized their suggestions within the specific needs of their patients. All groups suggested practices consistent with health-literate communications. We reached 83% (54/65) of the participants for message redistribution, and most had overwhelmingly positive responses to the refined messages. CONCLUSIONS: We suggest convenient methods for community involvement in the creation of health messages by using a brief web-based survey. We identified areas of improvement for future health messaging, such as reaffirming the preventive practices advertised early in a crisis, framing messages such that they allow for personal choice of preventive behavior, highlighting well-known source information, using plain language, and crafting messages that are applicable to the readers' circumstances.
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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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".