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 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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".