Mapping vaccine hesitancy—Country-specific characteristics of a global phenomenon
Bibliographic record
Abstract
The term vaccine hesitancy refers to delay in acceptance or refusal of vaccines despite the availability of vaccination services. Different factors influence vaccine hesitancy and these are context-specific, varying across time and place and with different vaccines. Factors such as complacency, convenience and confidence are involved. Acceptance of vaccines may be decreasing and several explanations for this trend have been proposed. The WHO Strategic Advisory Group of Experts (SAGE) on Immunization has recognized the global importance of vaccine hesitancy and recommended an interview study with immunization managers (IMs) to better understand the range of vaccine hesitancy determinants that are encountered in different settings. Interviews with IMs in 13 selected countries were conducted between September and December 2013 and various factors that discourage vaccine acceptance were identified. Vaccine hesitancy was not defined consistently by the IMs and most interpreted the term as meaning vaccine refusal. Although vaccine hesitancy existed in all 13 countries, some IMs considered its impact on immunization programmes to be a minor problem. The causes of vaccine hesitancy varied in the different countries and were context-specific, indicating a need to strengthen the capacity of national programmes to identify the locally relevant causal factors and to develop adapted strategies to address them.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".