{"id":"W4292386346","doi":"10.32388/9smv1e.6","title":"Building a digital republic to reduce health disparities and improve population health in the United States","year":2022,"lang":"en","type":"preprint","venue":"Qeios","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Medicaid; Health equity; Government (linguistics); Population; Economic growth; Business; Health care; Health policy; Political science; Public relations; Medicine; Environmental health; Economics","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":[0.005194854,0.000366374,0.0002922531,0.001038218,0.002623112,0.004515861,0.0005340104,0.001854142,0.0096301],"category_scores_gemma":[0.0107928,0.0001554745,0.0005860955,0.0007631065,0.002784893,0.005185591,0.002510588,0.002925125,0.0013953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001492436,"about_ca_system_score_gemma":0.003340085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002549524,"about_ca_topic_score_gemma":0.003774802,"domain_scores_codex":[0.9980989,0.00086272,0.00009982396,0.000160325,0.0006344443,0.0001437586],"domain_scores_gemma":[0.997093,0.001533201,0.0001527393,0.0003228446,0.0005564839,0.0003417736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003210247,0.00006124115,0.001201799,0.0001744624,0.00002504383,0.00006391545,0.001254482,0.0002773752,0.0003027038,0.3172532,0.5235797,0.155774],"study_design_scores_gemma":[0.0000196124,0.00002087888,0.0008636792,0.0002376662,0.0000244908,0.00003811507,0.0005895655,0.0003853826,0.0005569365,0.0505406,0.9467081,0.00001494328],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01063988,0.02654681,0.02085688,0.6290439,0.1323573,0.0001313617,0.000444137,0.0007528905,0.1792268],"genre_scores_gemma":[0.2977178,0.1072874,0.0594005,0.1681491,0.1240172,0.0003978004,0.0007922447,0.0009267771,0.2413111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0096301,"threshold_uncertainty_score":0.03221595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04596884319460037,"score_gpt":0.3927620222807241,"score_spread":0.3467931790861237,"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."}}