Mapping Research Trends of Universal Health Coverage From 1990 to 2019: Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: Universal health coverage (UHC) is one of many ambitious, health-related, sustainable development goals. Sharing various experiences of achieving UHC, in terms of challenges, pitfalls, and future prospects, can help policy and decision-makers reduce the likelihood of committing errors. As such, scholarly articles and technical reports are of paramount importance in shedding light on the determinants that make it possible to achieve UHC. OBJECTIVE: The purpose of this study is to conduct a comprehensive analysis of UHC-related scientific literature from 1990 to 2019. METHODS: We carried out a bibliometric analysis of papers related to UHC published from January 1990 to September 2019 and indexed in Scopus via VOSviewer (version 1.6.13; CWTS). Relevant information was extracted: the number of papers published, the 20 authors with the highest number of publications in the field of UHC, the 20 journals with the highest number of publications related to UHC, the 20 most active funding sources for UHC-related research, the 20 institutes and research centers that have produced the highest number of UHC-related research papers, the 20 countries that contributed the most to the research field of UHC, the 20 most cited papers, and the latest available impact factors of journals in 2018 that included the UHC-related items under investigation. RESULTS: In our analysis, 7224 articles were included. The publication trend was increasing, showing high interest in the scientific community. Most researchers were from the United States, the United Kingdom, and Canada, with Thailand being a notable exception. The Lancet accounted for 3.95% of published UHC-related research. Among the top 20 funding sources, the World Health Organization (WHO), the Bill and Melinda Gates Foundation, and the National Institutes of Health (NIH) accounted for 1.41%, 1.34%, and 1.02% of published UHC-related research, respectively. The highest number of citations was found for articles published in The Lancet, the American Journal of Psychiatry, and the Journal of the American Medical Association (JAMA). The top keywords were "health insurance," "insurance," "healthcare policy," "healthcare delivery," "economics," "priority," "healthcare cost," "organization and management," "health services accessibility," "reform," "public health," and "health policy." CONCLUSIONS: The findings of our study showed an increasing scholarly interest in UHC and related issues. However, most research concentrated in middle- and high-income regions and countries. Therefore, research in low-income countries should be promoted and supported, as this could enable a better understanding of the determinants of the barriers and obstacles to UHC achievement and improve global health.
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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 | 0.012 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.226 | 0.287 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".