{"id":"W3033070441","doi":"10.2196/18780","title":"Medical Insurance Information Systems in China: Mixed Methods Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Informatization; China; Business; Status quo; Information system; Computer science; Political science","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":[0.01741411,0.0008106048,0.002165731,0.00688335,0.002843019,0.002968439,0.001770812,0.001245512,0.004535507],"category_scores_gemma":[0.02166785,0.000882661,0.002581259,0.009831411,0.001036049,0.003176834,0.0021559,0.001170562,0.0003436207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008362111,"about_ca_system_score_gemma":0.0144559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04055293,"about_ca_topic_score_gemma":0.05292224,"domain_scores_codex":[0.986991,0.005448493,0.002649755,0.00132649,0.002405734,0.001178462],"domain_scores_gemma":[0.9766649,0.01314393,0.004297011,0.000890513,0.004057952,0.0009457335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.000488267,0.002215978,0.7944711,0.02124743,0.002904504,0.001080233,0.05291005,0.0005698102,0.0003847231,0.004737903,0.003603314,0.1153867],"study_design_scores_gemma":[0.0005190826,0.002974161,0.8332075,0.01210474,0.004252769,0.0005689693,0.1187603,0.003762924,0.000821469,0.002635762,0.02012761,0.0002646874],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9518309,0.02978499,0.00334693,0.001801959,0.0001013856,0.005736025,0.003149365,0.00003480435,0.004213499],"genre_scores_gemma":[0.9668359,0.0108909,0.007350432,0.001854129,0.0001109045,0.009835872,0.001793561,0.0000210915,0.001307078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04055293,"threshold_uncertainty_score":0.09209573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03559234592310894,"score_gpt":0.3297426039212558,"score_spread":0.2941502579981469,"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."}}