Impact of a Black Physician Panel Discussion on Coronavirus Disease 2019 (COVID-19) Health Education
Bibliographic record
Abstract
Background: Coronavirus disease 19 (COVID-19) has infected >26 million Americans with >400,000 deaths. Both Pfizer and Moderna vaccines against severe acute respiratory coronavirus 2 (SARS-CoV-2) have demonstrated 95% efficacy; yet there has been growing vaccination hesitancy, especially within communities of color. To achieve herd immunity and quell the spread of SARS-CoV-2, several strategies need to be deployed. This community-based demonstration project highlights the impact of a panel of black physicians’ ability to increase vaccination intent within a social media campaign targeted toward a black audience, namely a live question-and-answer (Q&A) event on SARS-CoV-2 vaccines. Methods: The social media campaign included a flyer featuring the head shots and titles of 11 black physicians. The flyer showcased a live Q&A event via Zoom video conference software. Attendees were requested to preregister with their name, e-mail address, and country of origin. Results: The live Q&A event was attended by 251 viewers. Geographic distribution was predominantly within the United States (~88%), but a few attendees were from the United Kingdom (~11%) and Canada (<1%), Puerto Rico (<1%), and Paraguay (<1%). One hundred twenty eight questions and comments were received from attendees. Audience questions were categorized, with predominant topics as follows: Vaccine Safety, Medical Mistrust, Vaccine Safety in Pregnancy, Vaccine Efficacy, and Vaccine Development. The top five poll results revealed: 31% of audience members were not planning to vaccinate or were not sure about vaccination, but after the event are now planning to vaccinate; 93% believed their knowledge of the C19 vaccines had increased; 95% believed it was important that the information was presented by Black health experts; 90% reported that they trusted the information presented; and 96% rated the session as “good or excellent”. Conclusion: Our social media project is an example of one strategy healthcare professionals can utilize to positively influence local and global communities in the mitigation of the COVID-19 pandemic. Results of this project evaluation showed that viewers responded favorably, reporting increases in vaccine acceptance and knowledge. Most respondents also affirmed the importance of having black experts involved in communicating this information. COVID-19 has disproportionately affected black communities as a result of health inequities and institutionalized racism.1 The event amplifies the importance of utilizing social-media–based interventions and increasing black healthcare representation to aid infection control. 1. Jones C. Why Racism, Not Race, Is a Risk Factor for Dying of COVID-19. Scientific American June 12, 2020. Funding: No Disclosures: None
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".