{"id":"W6939027693","doi":"10.60692/nywfb-ytn12","title":"Measuring the Protective Effect of Health Insurance Coverage on Out-of-Pocket Expenditures During the COVID-19 Pandemic in the Peruvian Population","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Pandemic; Poverty; Population; Health insurance; Disease control; Coronavirus disease 2019 (COVID-19); National health insurance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001605071,0.0002750459,0.0002324937,0.0006528852,0.0002709882,0.000498626,0.0003064423,0.0003874833,0.001694938],"category_scores_gemma":[0.006025808,0.0001511535,0.0006037272,0.0008036122,0.000254005,0.0004724271,0.0007405783,0.0004881162,0.0001551168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003831469,"about_ca_system_score_gemma":0.0004752441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01144309,"about_ca_topic_score_gemma":0.01291336,"domain_scores_codex":[0.999062,0.0005597704,0.00005694383,0.0001144875,0.00007539264,0.000131493],"domain_scores_gemma":[0.99743,0.0007227815,0.001389645,0.0001363374,0.0001919112,0.0001292968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000177606,0.0001083106,0.9896415,0.00007524731,0.0001760275,0.00003701844,0.0004842731,0.0001897794,0.0002243952,0.00009408184,0.0002059272,0.008585702],"study_design_scores_gemma":[0.000004899277,0.0002644263,0.9983842,0.00002688006,0.00006516521,0.00003585015,0.0003638248,0.0003208854,0.00007822787,0.00004980368,0.0004021654,0.000003638213],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973366,0.0005361462,0.000256345,0.0002468867,0.000006593635,0.00002603241,0.0008080189,0.000004983444,0.0007785195],"genre_scores_gemma":[0.9983803,0.000305469,0.0003296136,0.00007186332,0.00001442069,0.0000550617,0.0006182315,0.000001627055,0.0002234207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01144309,"threshold_uncertainty_score":0.02275294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04816989076704757,"score_gpt":0.2447296212084278,"score_spread":0.1965597304413802,"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."}}