Anti-Vaccination movement as a supracultural phenomenon
Bibliographic record
Abstract
Abstract Background Despite efforts by public health officials to counter the spread of anti-vaccine ideology, the movement continues to thrive in various countries and cultures. This study aims to answer the question of whether the anti-vaccine movement is a supracultural phenomenon that transcends national and cultural boundaries by identifying the anti-vaccine narratives in Serbia and comparing them to anti-vaccination narratives present in Germany, Brazil, India, United States of America, Australia, Canada, Nigeria and Japan. Answering this question can inform the development of targeted interventions and communication strategies to mitigate the negative impact of the anti-vaccine movement on public health. Methods For identification and understanding of the anti-vaccine narratives, qualitative research is conducted using thematic analysis via the folk model on 12 Serbian social groups on the internet chosen by the criteria of an existing anti-vaccine narrative. Systematic analysis of the topic-related literature from PubMed and Google Scholar and news articles and broadcasts provided information on present narratives in other countries. Results Comparative analysis of the acquired data has confirmed that two narratives are present in all countries. Distrust in the vaccine composition and benevolence of its components being one and another being the malevolent political agenda behind the vaccination. Conclusions This study has proved that the anti-vaccine movement is a supracultural phenomenon based on the recurring narratives in countries worldwide. The importance of this discovery for public health lies in the implication that only the unified international initiative to counter the anti-vaccine movements can hope to yield lasting results because, if confronted locally, movements can be bolstered from outside sources. Key messages • The anti-vaccine movement is a supracultural phenomenon. • The anti-vaccine movement cannot be countered locally.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".