A global bibliometric analysis of research productivity on vaccine hesitancy from 1974 to 2019
Bibliographic record
Abstract
Vaccine hesitancy is a phenomenon where individuals delay or refuse to take some or all vaccines. The objective of this study was to conduct a global bibliometric analysis of research productivity and identify country level indicators that could be associated with publications on vaccine hesitancy. We searched PubMed and Web of Science for publications from 1974 to 2019, and selected articles focused on behavioral and social aspects of vaccination. Data on country-level indicators were obtained from the World Bank. We used Spearman's correlation and zero-inflated negative-binomial regression models to ascertain the association between country level indicators and the number of publications. We identified 4314 articles, with 1099 eligible for inclusion. The United States of America (461 publications, 41.9%), Canada (84 publications, 7.6%) and the United Kingdom (68 publications, 6.2%) had the highest number of publications. Although various country indicators had significant correlations with vaccine hesitancy publications, only gross domestic product (GDP) and gross national income (GNI) per capita were independent positive predictors of the number of publications. When the number of publications were standardized by GDP, the Gambia, Somalia and Malawi ranked highest in decreasing order. The United States, Canada and United Kingdom ranked highest (in that order) when standardized by current health expenditure. Overall, high-income countries were more productive in vaccine hesitancy research than low-and-middle-income countries. There is a need for more investment in research on vaccine hesitancy in low-and-middle-income 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 | 0.015 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.205 | 0.238 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".