{"id":"W4328095925","doi":"10.32388/9smv1e.7","title":"Building a Digital Republic to Reduce Health Disparities and Improve Population Health in the United States","year":2023,"lang":"en","type":"preprint","venue":"Qeios","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Population; Health equity; Health care; Business; Medicaid; Welfare; Government (linguistics); Public economics; Economic growth; Public relations; Political science; Economics; Medicine; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002793782,0.0001988218,0.0004320984,0.0003479474,0.0007447138,0.0006539946,0.0003735895,0.0001516614,0.000005005969],"category_scores_gemma":[0.0009080354,0.0001671458,0.00005815356,0.0006276652,0.00008893693,0.0002042587,0.0002986041,0.0005319956,0.000004428433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006091942,"about_ca_system_score_gemma":0.0008534063,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4850805,"about_ca_topic_score_gemma":0.05708515,"domain_scores_codex":[0.9971126,0.0005073043,0.0006121581,0.0004719619,0.0004375894,0.0008584311],"domain_scores_gemma":[0.9984653,0.0005219,0.0002723286,0.0003260884,0.0000607653,0.0003536194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00008695701,0.0002204363,0.2256994,0.004840758,0.00006582854,0.00001462527,0.2423689,0.001417741,3.436371e-7,0.258456,0.1333945,0.1334345],"study_design_scores_gemma":[0.0003272232,0.0001387725,0.7416139,0.001212656,0.000005958391,0.000001467768,0.04311552,0.0004964584,3.678585e-7,0.05747789,0.1550968,0.000513027],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.4941118,0.000661025,0.0001483433,0.5018509,0.0008705852,0.001787763,0.0002828577,0.0001822373,0.0001044229],"genre_scores_gemma":[0.9592543,0.003476448,0.0004582925,0.0347545,0.0004860052,0.0002165236,0.0006040517,0.00004074226,0.0007091023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5159145,"threshold_uncertainty_score":0.9601206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06901516493330752,"score_gpt":0.4053600339361729,"score_spread":0.3363448690028654,"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."}}