Bibliographic record
Abstract
Misinformation and disinformation ("fake news") about vaccines are contagious-travelling faster and farther than truth. The consequences are serious; leading to negative impacts on health decisions, including vaccine acceptance, and on trust in immunization advice from public health and/or healthcare professional. This article provides a brief overview of evidence-based strategies to address vaccine deniers in public, in clinical practice and in social situations. As well, a strategy to help differentiate between vaccine deniers and simple vaccine refusers in a practice or clinic is provided. Five tactics are widely used by vaccine deniers: conspiracy; fake experts; selectivity; impossible expectations; and misrepresentation and false logic. Recognizing and understanding these tactics can help protect against misinformation and science denialism propaganda. Highlighting the strong medical science consensus on the safety and effectiveness of vaccines also helps. Carefully and wisely choosing what to say and speaking up-whether you are at a dinner party, out with friends or in your medical office or clinic-is crucial. Not speaking up implies you agree with the misinformation. Having healthcare providers recognize and address misinformation using evidence-based strategies is of growing importance as the arrival of the coronavirus disease 2019 (COVID-19) vaccines is expected to further ramp up the vaccine misinformation and disinformation rhetoric. Healthcare providers must prepare themselves and act now to combat the vaccine misinformation tsunami.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".