{"id":"W4402901695","doi":"10.1038/s41598-024-69212-x","title":"Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski; Natural Sciences and Engineering Research Council of Canada; University of Victoria","funders":"Government of Canada; Canadian Institutes of Health Research; Western Canada Research Grid; National Research Council Canada; Compute Canada","keywords":"Pandemic; Outbreak; Coronavirus disease 2019 (COVID-19); Health care; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Data science; Computer science; Medicine; Virology; Political science; Disease; Infectious disease (medical specialty); Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01220054,0.001340036,0.0009370258,0.005433866,0.001071159,0.00345602,0.001672097,0.001236212,0.00223965],"category_scores_gemma":[0.0210636,0.0003829284,0.001283799,0.00385121,0.001357519,0.002362297,0.002215842,0.001364383,0.0003266578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003171932,"about_ca_system_score_gemma":0.003674204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0118099,"about_ca_topic_score_gemma":0.008393044,"domain_scores_codex":[0.9924108,0.005403171,0.0004145575,0.0005717198,0.0009142781,0.0002855822],"domain_scores_gemma":[0.9900653,0.006382428,0.000977368,0.0003341855,0.001784487,0.0004561091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001570103,0.0003368435,0.02732093,0.0003305955,0.0004547323,0.0002815401,0.001063395,0.7879045,0.001605888,0.1139484,0.002045421,0.0645507],"study_design_scores_gemma":[0.000008419159,0.0001130605,0.003146841,0.0000497707,0.00002942507,0.00003366299,0.0005101824,0.9576352,0.0002393084,0.03700376,0.001194018,0.00003643668],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06263028,0.0006351451,0.9262444,0.001011924,0.00007057887,0.000553422,0.000603759,0.0001475825,0.008102908],"genre_scores_gemma":[0.6750399,0.0004491063,0.3212233,0.0001511364,0.00006556539,0.000943746,0.0005969362,0.00003290302,0.001497317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01220054,"threshold_uncertainty_score":0.06452334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3435877766725841,"score_gpt":0.5586850678832589,"score_spread":0.2150972912106748,"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."}}