COVID-19 InfoVaccines: A WHO-supported educational project to promote COVID-19 vaccination information among professionals and the general population
Bibliographic record
Abstract
COVID-19 vaccine uptake varied across countries, in part due to vaccine hesitancy fueled by a lack of trustworthy information. To help health workers provide evidence-based answers to common questions about COVID-19 vaccines and vaccination, and thereby, assist individuals´ decisions on vaccine acceptance, COVID-19 InfoVaccines, a joint WHO-EU project, was launched in February 2021 to support COVID-19 vaccine rollout in 6 Eastern European countries. COVID-19 InfoVaccines was made available in seven languages and shared on social media networks. A total of 262,592 users accessed COVID-19 InfoVaccines.com between February 11, 2021, and January 31st, 2023. The users were most interested in: general questions; vaccine efficacy and duration of protection; vaccine safety; vaccine co-administration, and dose-interval and interchangeability; though the interest in a specific theme varied in function of the epidemiological situation. A total of 118,510 (45.1%) and 46,644 (17.7%) users scrolled up to 35% and 75% of the COVID-19 InfoVaccines webpage, respectively. The average engagement rate was 71.61%. The users accessed COVID-19 InfoVaccines from 231 countries and territories, but the majority were in Ukraine (N = 38,404; 14.6%), Spain (N = 23,327; 8.9%), and Argentina (N = 21,167; 8.1%). Older Facebook users were more interested in COVID-19 information than younger individuals (X2 p-value < .0001). Two hundred twenty-eight videos were shared on YouTube. The average Click-Through-Rate on Facebook was 7.82%, and that on YouTube was 4.4%, with 60 videos having a Click-Through-Rate >5%, falling in the range of average YouTube video Click-Through-Rate (2% – 10%). As misinformation about vaccines and vaccination spreads easily and can negatively impact health-related decisions, initiatives like COVID-19 InfoVaccines are crucial to facilitate access to reliable information.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".