{"id":"W4381611827","doi":"10.3233/shti230384","title":"Humanizing Big Data and Detailing Social Determinants of Health via Information Visualizations","year":2023,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Homewood Research Institute; University of Victoria","funders":"","keywords":"Ethnic group; Visualization; Macro; Health care; Coronavirus disease 2019 (COVID-19); Health equity; Population; Big data; Pandemic; Population health; Persona; Data science; Sociology; Computer science; Medicine; Human–computer interaction; Economic growth; Artificial intelligence; Disease; Data mining; Economics; Anthropology","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":[0.005372007,0.0008326053,0.0003788132,0.005688258,0.001324769,0.005836261,0.0008899668,0.0006357751,0.01378728],"category_scores_gemma":[0.01745784,0.0003050643,0.0008847737,0.004981398,0.001150266,0.004699557,0.004456261,0.001186289,0.00106821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009303272,"about_ca_system_score_gemma":0.002042793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005188123,"about_ca_topic_score_gemma":0.009130161,"domain_scores_codex":[0.9977419,0.00147437,0.000150285,0.0001777175,0.0003617343,0.00009401116],"domain_scores_gemma":[0.9844109,0.01169626,0.0007795596,0.001757961,0.001033081,0.0003222818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005409135,0.0003016885,0.03952113,0.003232443,0.0002056564,0.0008133154,0.08648217,0.01532607,0.005952906,0.2929695,0.100242,0.4544121],"study_design_scores_gemma":[0.0001425883,0.0001866211,0.0224193,0.002221329,0.0001461767,0.0005198004,0.04471621,0.05139033,0.007800268,0.2632505,0.6069858,0.0002210587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1596816,0.002518257,0.6539179,0.02414506,0.001110048,0.001547292,0.0396832,0.01806894,0.09932767],"genre_scores_gemma":[0.429627,0.002330657,0.5508304,0.0006788934,0.0002656322,0.001282539,0.008458991,0.001187822,0.005338062],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01378728,"threshold_uncertainty_score":0.04612303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2436838404847257,"score_gpt":0.4909262858760622,"score_spread":0.2472424453913366,"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."}}