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Global investments in pandemic preparedness and COVID-19 : development assistance and domestic spending on health between 1990 and 2026
Bibliographic record
VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersStanford Cardiovascular Institute, School of Medicine, Stanford UniversityDivision of Human Resource DevelopmentNational Heart, Lung, and Blood InstituteFaculty of Medicine and Health, University of SydneyDipartimento di Medicina e Chirurgia, Università degli Studi di Milano-BicoccaLee Kong Chian School of Medicine, Nanyang Technological UniversityCare and Public Health Research Institute, Universiteit MaastrichtNational Health and Medical Research CouncilSamsungUniwersytet OpolskiFarhangian UniversityGeorge Institute for Global HealthMoscow Institute of Physics and TechnologyUniversitas Syiah KualaUniversiti Sultan Zainal AbidinAlborz University of Medical SciencesGonabad University of Medical SciencesQazvin University of Medical SciencesMasarykova UniverzitaIstituto Auxologico ItalianoUniversity of ReadingMekelle UniversityUniversity of PeradeniyaDire Dawa UniversityTarbiat Modares UniversityUniversity of GondarUniversitatea din BucureștiUrmia UniversityZagazig UniversityUniversitas IndonesiaXiamen UniversityUniversidad de ChileAkademiska SjukhusetJimma UniversityManipal College of Pharmaceutical Sciences, Manipal Academy of Higher EducationFlinders UniversityUniversiteit UtrechtNankai UniversityUniversidade Federal de Minas GeraisAhvaz Jundishapur University of Medical SciencesUniversity of Veterinary and Animal SciencesChinese University of Hong KongHaramaya UniversityInyuvesi Yakwazulu-NataliWestern Sydney UniversityTartu ÜlikoolGeorge Mason UniversityUppsala UniversitetKerman University of Medical SciencesUniversiti Putra MalaysiaU.S. Department of Veterans AffairsUniversity of CreteUniversiti MalayaKing Abdulaziz UniversityMadda Walabu UniversityInstitució Catalana de Recerca i Estudis AvançatsUniversity of the Western CapeRajshahi UniversityFundación Valle del LiliUniversitat de ValènciaHelsingin YliopistoDelhi Technological UniversityDirectorate for Biological SciencesMenofia UniversityBushehr University of Medical SciencesIslamic Azad UniversityGachon UniversityCentro de Investigación Biomédica en Red de Salud MentalUniversidad ICESIUniversity of New South WalesPublic Health EnglandUniversiteit MaastrichtAin Shams UniversityWuhan UniversityRijksuniversiteit GroningenAmity UniversityUniversity of LeedsNational Research University Higher School of EconomicsSüleyman Demirel ÜniversitesiHamadan University of Medical SciencesPirogov Russian National Research Medical UniversitySaveetha Dental CollegeKorea UniversityKasturba Medical College, ManipalEwha Womans UniversityKrishna Institute Of Medical Sciences Deemed To Be UniversityCentral University of KeralaNational Center of Neurology and PsychiatryTribhuvan UniversityMonash UniversitySouth Eastern Sydney Local Health DistrictYazd UniversityBill and Melinda Gates FoundationInstituto de Salud Carlos IIIUniversità degli Studi di MilanoUniversity of South CarolinaBournemouth UniversityKermanshah University of Medical SciencesVanderbilt UniversityLondon School of Economics and Political ScienceCase Western Reserve UniversityRice UniversityJazan UniversityPublic Health WalesTulane UniversityBundesministerium für GesundheitNanyang Technological UniversityUniversidade da Beira InteriorUniversity of GeorgiaJohns Hopkins UniversityJahrom University of Medical SciencesLondon South Bank UniversityDebre Tabor UniversityUniversity Of Nigeria NsukkaUniversità di BolognaJackson State UniversityAhmadu Bello UniversityJordan University of Science and TechnologyMcMaster UniversityRajarata University of Sri LankaYale UniversityKyung Hee UniversityUniversidad de ConcepciónUniversidad de AntioquiaUniversità degli Studi di Napoli Federico IIUniversity of Ottawa
KeywordsPreparednessPandemicGovernment (linguistics)Context (archaeology)Global healthInvestment (military)Health care
DOInot available
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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.
metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.145
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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.
Opus teacher head0.102
GPT teacher head0.436
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation status
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.
The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical
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".
Quick stats
Citations0
Published2023
Admission routes1
Has abstractno