{"id":"W2160246577","doi":"10.1016/s0168-8510(03)00047-2","title":"Income inequality and mortality: time series evidence from Canada","year":2003,"lang":"en","type":"article","venue":"Health Policy","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Toronto","funders":"","keywords":"Economics; Economic inequality; Inequality; Unemployment; Econometrics; Distributed lag; Gini coefficient; Demographic economics; Income distribution; Population; Population health; Demography; Macroeconomics; Economic growth; Mathematics; Health care; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003038544,0.0006052439,0.001146807,0.003838911,0.003272664,0.002306677,0.001928225,0.001136943,0.00432027],"category_scores_gemma":[0.01190445,0.0004437469,0.00144427,0.01803571,0.001256646,0.0008336508,0.001544763,0.001924248,0.0002788082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03046913,"about_ca_system_score_gemma":0.06140065,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9978637,"about_ca_topic_score_gemma":0.998376,"domain_scores_codex":[0.9975629,0.000380584,0.0002010439,0.0002550494,0.0007385358,0.0008618999],"domain_scores_gemma":[0.9837192,0.003115786,0.003476749,0.0007701392,0.00638312,0.002535011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008826304,0.0002443154,0.9663603,0.0002871092,0.000984328,0.00023689,0.001046035,0.001159091,0.00007191872,0.001645483,0.01310264,0.0139792],"study_design_scores_gemma":[0.0001109825,0.00005731823,0.9907747,0.0002236679,0.0007563023,0.00004303512,0.001553336,0.0005562888,0.00008877579,0.0003015118,0.005493175,0.00004085184],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9099773,0.02556285,0.0005423375,0.009454138,0.0001895694,0.0001213771,0.04515367,0.00003946784,0.008959274],"genre_scores_gemma":[0.9624832,0.01496663,0.0005426378,0.000990181,0.0001167164,0.00004748334,0.01725957,0.00002187584,0.003571685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03046913,"threshold_uncertainty_score":0.2210701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08114074145866369,"score_gpt":0.4338414781583936,"score_spread":0.3527007366997299,"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."}}