65. Vaccine Confidence, COVID19, and the Influence of Peer Networks
Bibliographic record
Abstract
Abstract Background An increased appreciation for vaccines could be expected due to COVID-19. However, surveys show a polarization in opinions with about 20% of Americans preemptively rejecting any COVID-19 vaccine, partly due to inconsistent risk communication. While Health Care Professionals (HCPs) will be heavily relied upon to encourage uptake of a COVID-19 vaccine and 70% of Americans receive their vaccine information from HCPs, 84% also rely on peer networks. Understanding that HCPs have an important, but not exclusive, influence on health decision making can signal a new approach. This study provides data on where women, the main decision-makers regarding immunization in most families access information about vaccination. Methods Through an online survey conducted in UK, Brazil, Germany, Italy and Canada from 10 to 19-March 2020, we collected data on where, and from whom, women aged 25–54 years access information about vaccination. We set 1000 respondents/country quotas to reflect regional differences with data weighted as necessary. Results 5,036 women who met inclusion criteria responded: from the UK (1,003), Brazil (1,002), Germany (1,008), Italy (1,007), and Canada (1,016). Though most likely to receive vaccination info via their HCP: in Germany, women are least likely to be influenced by HCPs, with those aged 25–34 years more likely to turn to family members or online sources; in the UK, they are more likely to find info via a health authority’s website; and in Brazil, they are more likely to see info in traditional media and on Facebook. Only 50% ranked vaccine efficacy and disease risk in the Top 5 factors influencing their vaccine decisions, alongside the opinion of an HCP, recommendation of a Public Health Authority and impact of the disease. Conclusion HCPs, families and peers are important sources of info regarding vaccination. COVID-19 is unlikely to improve vaccine confidence as the issue becomes increasingly polarized and communications more inconsistent. We can respond by investing in health promotion and harmonized communications through peer networks. Since caregivers, their families and peers have increased weight in vaccination decisions, then they should have increased weight in preventive health strategies. Disclosures Ivo Vojtek, PharmD, PhD, MSc, FRSM, RPh, GSK Vaccines (Employee, Shareholder) Vanessa Palsenbarg, MA, GSK Vaccines (Employee, Shareholder) Joe Smyser, PhD, Public Good Project (Board Member, Employee)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".