{"id":"W2151467249","doi":"10.5539/gjhs.v7n6p146","title":"Asymmetric Information in Iranian’s Health Insurance Market: Testing of Adverse Selection and Moral Hazard","year":2015,"lang":"en","type":"article","venue":"Global Journal of Health Science","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Iran University of Medical Sciences","keywords":"Adverse selection; Moral hazard; Information asymmetry; Actuarial science; Hazard; Morale hazard; Selection (genetic algorithm); Health hazard; Business; Psychology; Environmental health; Medicine; Economics; Insurance policy; Casualty insurance; Auto insurance risk selection; Computer science; Microeconomics; Incentive; Chemistry; Finance","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":[0.02301981,0.0005200556,0.001167474,0.001962416,0.0008990682,0.002351265,0.001482149,0.001639907,0.008175164],"category_scores_gemma":[0.07484778,0.0003128806,0.002053745,0.001081307,0.003133942,0.003121713,0.001682258,0.002425685,0.0002061489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001682878,"about_ca_system_score_gemma":0.002182747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003758121,"about_ca_topic_score_gemma":0.001133907,"domain_scores_codex":[0.9866928,0.00749103,0.0006862545,0.001588039,0.001982269,0.001559618],"domain_scores_gemma":[0.8013285,0.1565641,0.02998022,0.005246311,0.004418366,0.002462551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002094271,0.001405524,0.8425735,0.000449807,0.001224691,0.002114773,0.003674763,0.03356395,0.0007506137,0.07496112,0.002004347,0.03518266],"study_design_scores_gemma":[0.0005076102,0.002284328,0.3452342,0.0001705079,0.0006960303,0.001133783,0.006494606,0.5570036,0.001294843,0.08341688,0.001599299,0.000164369],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818068,0.0003152231,0.01282046,0.001012462,0.00004069054,0.0002619664,0.0002072853,0.00001606394,0.00351901],"genre_scores_gemma":[0.9986191,0.00007679053,0.0008925073,0.00004319604,0.00003951499,0.00004318693,0.00005732371,0.000001711585,0.000226633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02301981,"threshold_uncertainty_score":0.1217418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05990038709545915,"score_gpt":0.2924924109935548,"score_spread":0.2325920238980957,"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."}}