{"id":"W3174372545","doi":"10.3386/w28972","title":"Women Left Behind: Gender Disparities in Utilization of Government Health Insurance in India","year":2021,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Global Maternal and Child Health","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Baylor University; York University; Columbia Population Research Center; University of Minnesota","keywords":"Government (linguistics); Business; Health insurance; Economic growth; Gender disparity; Demographic economics; Economics; Health care","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":[0.0002524615,0.0001296233,0.0001674248,0.001132173,0.0007392502,0.0008376145,0.0004440621,0.000216624,0.00265387],"category_scores_gemma":[0.0007580529,0.0001285374,0.0002861875,0.001655269,0.0005273711,0.0004739424,0.001023851,0.0006777631,0.0002852119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001054624,"about_ca_system_score_gemma":0.00145218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1096362,"about_ca_topic_score_gemma":0.1706816,"domain_scores_codex":[0.9995764,0.00005521758,0.00002417056,0.00004517636,0.0000722335,0.0002267607],"domain_scores_gemma":[0.9994838,0.00008912307,0.0001784411,0.00003197821,0.0000582439,0.0001582852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005829689,0.00007550792,0.9861569,0.0000271257,0.00003197698,0.0002081217,0.002741145,0.00007361444,0.0001471529,0.0009933289,0.001946408,0.007540344],"study_design_scores_gemma":[0.000002657656,0.00002856451,0.9947667,0.00002984075,0.00001769011,0.0001371958,0.003593507,0.0001048993,0.00006771728,0.0001891451,0.001054901,0.000007133899],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928017,0.0004603451,0.00004150272,0.0009193492,0.00001924461,0.000007793565,0.001220769,0.000005196508,0.004524263],"genre_scores_gemma":[0.998714,0.0002245442,0.00002461897,0.0001240157,0.00001037518,0.000004905542,0.0003024588,0.000001849823,0.0005933591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1096362,"threshold_uncertainty_score":0.2179961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3212027712957581,"score_gpt":0.5114329045789504,"score_spread":0.1902301332831923,"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."}}