{"id":"W2550241561","doi":"10.1177/1054137316678549","title":"Epidemiological Transition and Population Health: Understanding Social Determinants of Health in China","year":2016,"lang":"en","type":"article","venue":"Illness Crisis & Loss","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"University of British Columbia","keywords":"Epidemiology; Epidemiological transition; Health promotion; Social determinants of health; Context (archaeology); Public health; Disease; Population health; Population; China; Social epidemiology; Medicine; Environmental health; Global health; Economic growth; Political science; Geography; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001661973,0.0002931103,0.0003800155,0.002747202,0.001091442,0.001545274,0.0005642233,0.0005585356,0.00100701],"category_scores_gemma":[0.00288384,0.00017377,0.0004780773,0.002125969,0.001797022,0.002620429,0.002079593,0.0008819049,0.00003440373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003264812,"about_ca_system_score_gemma":0.004552955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0612734,"about_ca_topic_score_gemma":0.05812306,"domain_scores_codex":[0.999355,0.0002552343,0.00004534075,0.0001012416,0.00007879834,0.0001643596],"domain_scores_gemma":[0.9987144,0.0004198083,0.0003734112,0.0001001125,0.0001128612,0.0002794144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002993253,0.000134377,0.8846628,0.0001386627,0.0001306903,0.0004415482,0.009591573,0.002397095,0.0002662707,0.07374351,0.0008970165,0.02756647],"study_design_scores_gemma":[0.000004999594,0.00006681687,0.943313,0.0001468865,0.00004912842,0.00009415988,0.007583108,0.007740758,0.0000864128,0.03742407,0.003467784,0.0000228415],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653567,0.004502456,0.004403824,0.01847822,0.00009759295,0.00005455783,0.0002876037,0.00001934657,0.006799696],"genre_scores_gemma":[0.9981328,0.001068415,0.0003336768,0.0001856306,0.00004890549,0.0000154115,0.00005561774,0.000001027914,0.0001584975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0612734,"threshold_uncertainty_score":0.1218335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1003769342079544,"score_gpt":0.4114742259319649,"score_spread":0.3110972917240105,"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."}}