{"id":"W3044231266","doi":"10.2196/18084","title":"Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Robert Wood Johnson Foundation","keywords":"Health care; Predictive analytics; Predictive modelling; Social determinants of health; Process (computing); Digital health; Data science; Business; Psychology; Computer science; Economics; Economic growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1399338,0.001446316,0.002088024,0.005284431,0.002104694,0.01260766,0.004241223,0.003292488,0.002270989],"category_scores_gemma":[0.2359322,0.001273311,0.002477986,0.01022391,0.004872804,0.02024525,0.007046672,0.01380224,0.0008716725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003317847,"about_ca_system_score_gemma":0.01201473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02852456,"about_ca_topic_score_gemma":0.0446477,"domain_scores_codex":[0.9525522,0.03577102,0.002465348,0.002597133,0.0058258,0.0007884483],"domain_scores_gemma":[0.5680863,0.38283,0.01016803,0.01540987,0.02104244,0.002463282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002334001,0.0005284693,0.4198787,0.002868284,0.001422652,0.000433801,0.005910997,0.01884134,0.000359732,0.1492859,0.03509687,0.3651398],"study_design_scores_gemma":[0.00008323327,0.0003528379,0.04278633,0.01103051,0.0008077841,0.0005935788,0.01081778,0.2198284,0.001097002,0.6068589,0.1053845,0.0003591751],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.07634774,0.07795592,0.315478,0.5033174,0.003086841,0.0006912035,0.004725037,0.0007809299,0.01761704],"genre_scores_gemma":[0.6134729,0.06551009,0.2779777,0.02698739,0.007599547,0.001332875,0.004615666,0.0003892383,0.00211451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1399338,"threshold_uncertainty_score":0.7400494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6205259132512873,"score_gpt":0.5180967221802941,"score_spread":0.1024291910709931,"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."}}