{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00648748,0.0000821696,0.0004336691,0.0006013441,0.0001082222,0.00002324129,0.0001728358,0.00004340752,0.000001059872],"category_scores_gemma":[0.0003831978,0.00007138395,0.00002785994,0.00226246,0.00008376317,0.001281301,0.00002591006,0.0001573042,0.000004438443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107217,"about_ca_system_score_gemma":0.001302045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004444803,"about_ca_topic_score_gemma":0.000130532,"domain_scores_codex":[0.9977087,0.00003056317,0.001607032,0.0001156475,0.0001778354,0.0003602382],"domain_scores_gemma":[0.9977063,0.000009375041,0.001618022,0.00007862079,0.0002612836,0.0003264137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001336801,0.00001711954,0.9274219,0.0001654256,0.000001657598,6.239317e-7,0.0002102813,0.0004396868,3.488853e-7,0.0007870545,0.0001269769,0.07081553],"study_design_scores_gemma":[0.0005679455,0.0004260326,0.9927154,0.0001442562,1.349994e-7,0.0001884569,0.0002366946,0.003563949,0.000001597137,0.001455245,0.0006368014,0.00006346983],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915161,0.003365931,0.0009211069,0.0009746595,0.0007492524,0.0001956115,0.00003206425,0.000007135344,0.002238113],"genre_scores_gemma":[0.9955155,0.0003583288,0.003811929,0.0002741692,0.00003352499,8.735708e-7,2.899494e-7,0.000002446021,0.000002912426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07075205,"threshold_uncertainty_score":0.6719242,"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."}}