{"id":"W2895761109","doi":"10.1515/bejeap-2018-0067","title":"Public Health Insurance and Prescription Medications for Mental Illness","year":2018,"lang":"en","type":"article","venue":"The B E Journal of Economic Analysis & Policy","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicaid; Receipt; Medical prescription; Mental illness; Public health insurance; Mental health; Quarter (Canadian coin); Medicine; Psychiatry; Scope (computer science); Health insurance; Family medicine; Health care; Business; Nursing; Economics; Economic growth; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001168097,0.0002930989,0.0005296743,0.00108529,0.000620393,0.002035825,0.0007608863,0.0009834962,0.01725193],"category_scores_gemma":[0.00882951,0.0003188435,0.001113826,0.001900939,0.0006772552,0.001200487,0.001591528,0.002453138,0.000839946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002232208,"about_ca_system_score_gemma":0.002237588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08533492,"about_ca_topic_score_gemma":0.06631115,"domain_scores_codex":[0.9988369,0.0002976313,0.00007286769,0.0001287389,0.0002673245,0.0003964915],"domain_scores_gemma":[0.9887611,0.005237372,0.004465804,0.0002489042,0.0004072042,0.0008794537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000144729,0.0003606395,0.9772908,0.0000845352,0.0004358267,0.0001302296,0.0001384289,0.008125791,0.00008431153,0.004005963,0.004633063,0.004565669],"study_design_scores_gemma":[0.0000616835,0.0002570205,0.9583002,0.0002195201,0.0004662796,0.0001606305,0.001741543,0.02566475,0.0002983924,0.003692125,0.009099023,0.00003871842],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732386,0.001314255,0.0008132553,0.00545809,0.00009270336,0.00006183838,0.009552201,0.00003859876,0.009430531],"genre_scores_gemma":[0.9922802,0.0005715735,0.0001840786,0.0002754055,0.00009504065,0.00003184049,0.003769041,0.000006147886,0.002786778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08533492,"threshold_uncertainty_score":0.1696764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09986332699148082,"score_gpt":0.3335719135851081,"score_spread":0.2337085865936273,"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."}}