Bibliometric analysis of global scientific literature on vaccine hesitancy in peer-reviewed journals (1990–2019)
Bibliographic record
Abstract
BACKGROUND: Vaccine hesitancy is a growing threat to national and global health security. The current study was undertaken to provide insights into the global scientific literature on vaccine hesitancy in peer-reviewed journals. METHOD: The current study was a descriptive bibliometric study. A validated search strategy on vaccine hesitancy was implemented using SciVerse Scopus. Bibliometric indicators such as (1) annual growth of publications, (2) key players, (3) research themes, (4) pathogens/diseases encountered, (5) top-cited documents, and (6) annual growth of publications stratified by world region and by age category were presented. The study period was from 1990 to 2019. RESULTS: Search strategy found 2791 documents. The h-index of the retrieved literature was 89. The leading journal was Vaccine (369; 13.2%%) followed by Human Vaccines and Immunotherapeutics (129; 4.6%). Authors from the USA led with 1125 (40.3%) followed by authors from Italy (234; 8.4%) and the UK (204; 7.3%). The US CDC led with 140 (5.0%) documents followed by Emory University (USA) (81, 2.9%). The list of active authors included six from the USA while the remaining were from France, Australia, and Canada. Research themes in the retrieved literature focused on influenza, human papillomavirus, and the role of parents in immunization of their children. The region of the Americas and the European region had the greatest share of publications and showed steep growth of publications lately. Vaccine hesitancy research on adolescents was most noticed after 2007 while that on children was present all the time but escalated lately. Titles/abstracts analysis indicated that 621(22.3%) documents focused on influenza, 392 (14.0%) focused on Human papillomavirus, and 292 (10.5%) focused on measles. The top-cited documents in literature published after 2015 focused on the definition and strategies to overcome vaccine hesitancy. CONCLUSION: The last decade witnessed a noticeable increase in the number of publications. Influenza vaccine and parental concerns about the human papillomavirus vaccine were the main focus of the retrieved literature. Information on vaccine hesitancy needs to be collected from all countries to build a better coalition against the anti-vaccination groups. Re-building trust in vaccines requires targeting parents by providing adequate information on the vaccines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.029 | 0.440 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".