Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Artificial intelligence in public health: promises, challenges, and an agenda for policy makers and public health institutions
202519 citationsДимитра Пантели, Natasha Azzopardi‐Muscat et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Josep Figueras
Since
Specialization
Citations
This map shows the geographic impact of Josep Figueras's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Josep Figueras with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Josep Figueras more than expected).
This network shows the impact of papers produced by Josep Figueras. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Josep Figueras. The network helps show where Josep Figueras may publish in the future.
Co-authorship network of co-authors of Josep Figueras
This figure shows the co-authorship network connecting the top 25 collaborators of Josep Figueras.
A scholar is included among the top collaborators of Josep Figueras based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Josep Figueras. Josep Figueras is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Williams, Gemma, et al.. (2020). Translating evidence into policy during the COVID-19 pandemic: bridging science and policy (and politics). (Special Issue: COVID-19 health system response.). 26(2). 29–33.3 indexed citations
5.
Cylus, Jonathan, et al.. (2020). Living longer, but in better or worse health? [Internet].1 indexed citations
6.
Smith, Peter, Anna Sagan, Luigi Siciliani, et al.. (2020). Building on value-based health care.1 indexed citations
7.
Maresso, Anna, Philipa Mladovsky, Sarah Thomson, et al.. (2015). Economic crisis, health systems and health in Europe. Country experiences..31 indexed citations
Lavis, John N., Govin Permanand, Josep Figueras, et al.. (2013). How can knowledge brokering be advanced in a country’s health system?.1 indexed citations
Wismar, Matthias, et al.. (2007). HIA speeding up the decision-making process: the reconstruction of route 73 in Sweden.. 161–175.1 indexed citations
16.
Robinson, Ray, Josep Figueras, & Elke Jakubowski. (2005). Purchasing to improve health systems performance. London School of Economics and Political Science Research Online (London School of Economics and Political Science).156 indexed citations
Saltman, Richard B., et al.. (1998). Critical Challenges For Health Care Reform In Europe. DigitalGeorgetown (Georgetown University Library).131 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.