{"id":"W3154560214","doi":"","title":"Physician Bias and Racial Disparities in Health: Evidence from VeteransA Pensions","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pension; Endogeneity; Disability pension; Population; Medicine; Racial differences; Demographic economics; Survey of Income and Program Participation; Health and Retirement Study; Demography; Gerontology; Actuarial science; Economics; Political science; Ethnic group; Environmental health; Sociology; Law","routes":{"ca_aff":true,"ca_fund":false,"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":[0.007850045,0.0002047446,0.000403772,0.001679106,0.0009032902,0.0008349196,0.000788545,0.0009092353,0.005171984],"category_scores_gemma":[0.0389577,0.0002168883,0.0008566615,0.002437753,0.001210669,0.0006517473,0.001333424,0.0007140726,0.0002843552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003944528,"about_ca_system_score_gemma":0.0007185829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02573143,"about_ca_topic_score_gemma":0.02909618,"domain_scores_codex":[0.9952506,0.00270087,0.0003218841,0.0006112818,0.0006928227,0.0004225079],"domain_scores_gemma":[0.9486686,0.02704815,0.01652555,0.004001048,0.002182961,0.001573669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002753868,0.00006879354,0.9873675,0.0001215343,0.0005758328,0.0000605959,0.0008609073,0.00004676883,0.00004000816,0.0007699605,0.0004449968,0.009367702],"study_design_scores_gemma":[0.00003105349,0.0001246509,0.9959965,0.0001635676,0.0004192715,0.00009462352,0.0007487368,0.0001294341,0.00006747036,0.0005110131,0.001707197,0.000006502365],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803892,0.01146021,0.0004229496,0.002377266,0.00006969756,0.00001880504,0.0007212357,0.000003658842,0.004537029],"genre_scores_gemma":[0.996855,0.002199949,0.0001044167,0.0002693544,0.00009166123,0.000005754327,0.0002085917,0.000002473834,0.0002627615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02573143,"threshold_uncertainty_score":0.05116332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1803649128624223,"score_gpt":0.4146684536500502,"score_spread":0.2343035407876279,"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."}}