Is It Time for Vaccination to “Go Viral”?
Bibliographic record
Abstract
To promote and sustain excellent vaccination coverage, while preserving the key core values of ethics, truth, transparency and trust, the vaccine community should adopt modern digital communication strategies. This article summarizes our views-as experts in multidisciplinary field of vaccinology (consisting of an anthropologist, a public health policy advisor, a vaccine industry expert, a health care journalist and a practicing physician)-which were presented at a satellite symposium held at the 33rd European Society of Paediatric Infectious Disease conference in Leipzig, Germany, in May 2015. This article aims to suggest and recommend strategies to promote vaccination awareness, and highlight proactive measures for building, maintaining and enhancing trust in vaccination through innovative communication and evidence-based interaction with the end user. We believe that converting the results of vaccine research into a successful vaccination program, and replacing misinformation with evidence-based communication, will require a multidisciplinary approach that embraces modern digital and tailored applications to reach out to all populations.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".