Assessments of global drivers of vaccine hesitancy in 2014—Looking beyond safety concerns
Bibliographic record
Abstract
Vaccine hesitancy has become the focus of growing attention and concern globally despite overwhelming evidence of the value of vaccines in preventing disease and saving the lives of millions of individuals every year. Measuring vaccine hesitancy and its determinants worldwide is important in order to understand the scope of the problem and for the development of evidence-based targeted strategies to reduce hesitancy. Two indicators to assess vaccine hesitancy were developed to capture its nature and scope at the national and subnational level to collect data in 2014: 1) The top 3 reasons for not accepting vaccines according to the national schedule in the past year and whether the response was opinion- or assessment-based and 2) Whether an assessment (or measurement) of the level of confidence in vaccination had taken place at national or subnational level in the previous 5 years. The most frequently cited reasons for vaccine hesitancy globally related to (1) the risk-benefit of vaccines, (2) knowledge and awareness issues, (3) religious, cultural, gender or socio-economic factors. Major issues were fear of side effects, distrust in vaccination and lack of information on immunization or immunization services. The analysis revealed that 29% of all countries had done an assessment of the level of confidence in their country, suggesting that vaccine confidence was an issue of importance. Monitoring vaccine hesitancy is critical because of its influence on the success of immunization programs. To our knowledge, the proposed indicators provide the first global snapshot of reasons driving vaccine hesitancy and depicting its widespread nature, as well as the extent of assessments conducted by countries.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".