Mapping Eight Decades of Vaccination Social Science: Bibliometric Analysis of Global Research Trends
Bibliographic record
Abstract
Background: Despite growing recognition of vaccination social science as essential to immunization strategies, the field’s evolution, geographic distribution, and research patterns remain poorly characterized. This study provides the first comprehensive mapping of the social science literature on vaccination over eight decades. Methods: We conducted a bibliometric analysis of peer-reviewed publications indexed in PubMed from their inception, using a systematic search strategy that combined vaccination and social science terms. Publications were analyzed using the Bibliometrix R package (version 5.0) to examine temporal trends, author productivity, institutional contributions, geographic distribution, and thematic evolution globally. Results: We retrieved 8005 eligible publications. Analysis highlighted three chronological research phases: sporadic early work (1945–1980, n = 85), sustained growth (1981–2019, n = 2743), and unprecedented expansion since the COVID-19 era (2020–2024, n = 4563). Annual publications reached a peak in 2022 (n = 1686). Research spans 146 countries but remains concentrated in high-income countries, with the United States (n = 10,230), China (n = 3796), and Canada (n = 2288) leading production. The top 20 institutions were from the United States (n = 8), United Kingdom (n = 4), and Canada (n = 3), with a few institutions from African countries. International collaboration was moderate (19.44%). Thematic analysis revealed a clear evolution from biological science (1963–1999) to socio-behavioural science, with an emphasis on vaccine hesitancy, trust, communication, and health equity (2015–2024). Conclusions: Vaccination social science has grown steadily over the decades, with a sharp rise in research during the COVID-19 pandemic. Most studies were from high-income countries, underscoring the need for enhanced social science capacity in low- and middle-income countries. As the focus of immunization efforts shifts toward issues like vaccine hesitancy and trust, broader collaboration and inclusion will be key to improving vaccine uptake worldwide.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.132 | 0.208 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".