{"id":"W4401761072","doi":"10.1186/s12992-024-01066-4","title":"Financing pandemic prevention, preparedness and response: lessons learned and perspectives for future","year":2024,"lang":"en","type":"review","venue":"Globalization and Health","topic":"Viral Infections and Outbreaks Research","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mastercard Foundation","keywords":"Preparedness; Public health; Pandemic; Global health; Innovative financing; Business; Equity (law); Finance; Economic growth; Resilience (materials science); Political science; Health care; Coronavirus disease 2019 (COVID-19); Medicine; Economics; Infectious disease (medical specialty); Nursing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0148905,0.0008720841,0.0007533341,0.002091402,0.0021754,0.01021114,0.002409054,0.009227762,0.01050848],"category_scores_gemma":[0.02520562,0.0003756217,0.0009877265,0.003085987,0.00586075,0.01728368,0.005697171,0.009924693,0.001112614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008478097,"about_ca_system_score_gemma":0.03527358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00795945,"about_ca_topic_score_gemma":0.008772414,"domain_scores_codex":[0.9953138,0.002519128,0.0001762521,0.000256249,0.0006336579,0.001100923],"domain_scores_gemma":[0.9729338,0.01751683,0.001359682,0.0007117877,0.003324695,0.004153248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00012995,0.0002196122,0.002017112,0.00467957,0.00008269119,0.0007336217,0.003283667,0.003976746,0.0003446962,0.4861879,0.2083143,0.2900301],"study_design_scores_gemma":[0.00006721422,0.0001718939,0.002236063,0.02172656,0.00006155407,0.0004850712,0.01930716,0.00271576,0.0005498994,0.4666688,0.4859045,0.000105553],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.00386715,0.1920246,0.005251991,0.7814541,0.003599724,0.00005835593,0.0002033662,0.00005771386,0.0134829],"genre_scores_gemma":[0.2384392,0.6170462,0.02505101,0.1010801,0.009991683,0.0004371669,0.0004550991,0.00009244285,0.007407036],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.0148905,"threshold_uncertainty_score":0.07874942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2598463722970512,"score_gpt":0.5394568395072049,"score_spread":0.2796104672101538,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}