{"id":"W2310391462","doi":"10.1353/hpu.2016.0013","title":"Reducing Medical School Admissions Disparities in an Era of Legal Restrictions: Adjusting for Applicant Socioeconomic Disadvantage","year":2016,"lang":"en","type":"article","venue":"Journal of Health Care for the Poor and Underserved","topic":"Medical Education and Admissions","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Family Medicine","funders":"","keywords":"Disadvantage; Socioeconomic status; Medical school; Health equity; Environmental health; Demographic economics; Medicine; Psychology; Actuarial science; Political science; Medical education; Business; Law; Economics; Health care","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":[0.005947476,0.0004298259,0.000763476,0.0007059153,0.001216883,0.001566924,0.001558225,0.001058254,0.005524031],"category_scores_gemma":[0.02970172,0.0003686765,0.001842013,0.001200321,0.0007901219,0.001802066,0.002136849,0.001849364,0.0002466929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002674313,"about_ca_system_score_gemma":0.007083361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1131766,"about_ca_topic_score_gemma":0.1187857,"domain_scores_codex":[0.996819,0.001915473,0.0001124284,0.0003597252,0.0001651266,0.0006281323],"domain_scores_gemma":[0.9924292,0.004353243,0.001245159,0.0006252168,0.0004965227,0.0008505944],"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":[0.001037632,0.001522623,0.4209167,0.0001286867,0.0006386921,0.0002929056,0.0007685401,0.5319598,0.0004101168,0.01249415,0.004829489,0.02500064],"study_design_scores_gemma":[0.000973282,0.002029068,0.1483039,0.000123809,0.0006374752,0.00009335059,0.002598785,0.8193259,0.0006970993,0.01882429,0.006280246,0.0001128856],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915854,0.00008245734,0.002826137,0.001551577,0.00005705806,0.0001259743,0.0007227036,0.00004930749,0.002999461],"genre_scores_gemma":[0.9962111,0.00004539263,0.00236154,0.0002890816,0.00001796498,0.00009143174,0.0003367343,0.00001162573,0.0006352062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1131766,"threshold_uncertainty_score":0.2250356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04733231171881698,"score_gpt":0.3905228992233523,"score_spread":0.3431905875045353,"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."}}