{"id":"W2341671533","doi":"10.1016/j.healthpol.2016.04.007","title":"Examining regional variation in health care spending in British Columbia, Canada","year":2016,"lang":"en","type":"article","venue":"Health Policy","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital; University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Metropolitan area; Per capita; Context (archaeology); Health care; Population; Demographic economics; Regional variation; Population health; Geography; Business; Economic growth; Demography; Economics; Environmental health; Medicine; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00107177,0.00008784234,0.0003969443,0.0003335432,0.0001433391,0.00005583243,0.0001394777,0.00006254861,0.0001001443],"category_scores_gemma":[0.0002190946,0.0001472709,0.00002179127,0.0004772746,0.00001711521,0.0001429988,0.00004801532,0.0001370818,0.00002955207],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005767975,"about_ca_system_score_gemma":0.002196376,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9997157,"about_ca_topic_score_gemma":0.9996514,"domain_scores_codex":[0.9975516,0.00009800445,0.001060192,0.0004094677,0.00006479632,0.0008159609],"domain_scores_gemma":[0.9990118,0.00008521566,0.0004180393,0.0002194485,0.00001299176,0.00025252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007353821,0.00007431382,0.6011851,0.001122929,0.00001189017,0.00003095828,0.006667824,0.00003483783,4.231709e-7,0.2420323,0.0187563,0.1300757],"study_design_scores_gemma":[0.0006295345,0.0000535952,0.7266667,0.0002507662,1.075723e-7,0.000003843414,0.0003041927,0.00002288055,4.631775e-8,0.002826157,0.2691154,0.0001267841],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5681156,0.005647502,0.000729354,0.412513,0.0009978757,0.001710159,0.0007864133,0.00007945521,0.009420614],"genre_scores_gemma":[0.9775888,0.001248021,0.0002291285,0.01967393,0.0002829699,0.00005134922,0.00001467284,0.00002170337,0.0008894068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4094732,"threshold_uncertainty_score":0.9980487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08488889136272423,"score_gpt":0.3061789710288966,"score_spread":0.2212900796661723,"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."}}