{"id":"W2944030928","doi":"10.1007/s10903-019-00898-2","title":"Asian-White Health Inequalities in Canada: Intersections with Immigration","year":2019,"lang":"en","type":"article","venue":"Journal of Immigrant and Minority Health","topic":"Migration, Health and Trauma","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Heart and Stroke Foundation of Canada","keywords":"Mental health; Public health; Immigration; Ethnic group; Health equity; Inequality; Race and health; Self-rated health; Medicine; Population; Logistic regression; Cross-sectional study; Environmental health; Gerontology; Demography; Geography; Political science; Psychiatry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001542251,0.0003659499,0.0006978918,0.00337738,0.01281505,0.005660746,0.002287894,0.00123708,0.01032944],"category_scores_gemma":[0.004611988,0.0003451644,0.001036846,0.008437796,0.003026161,0.001767896,0.005195957,0.002874101,0.0002657099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04755864,"about_ca_system_score_gemma":0.1097707,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936686,"about_ca_topic_score_gemma":0.9979197,"domain_scores_codex":[0.9969495,0.0002477107,0.0001135591,0.0002100514,0.0003787182,0.002100431],"domain_scores_gemma":[0.9942029,0.0004860401,0.0007545312,0.0001444579,0.001778171,0.00263397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002273541,0.0002059291,0.9486223,0.00009921877,0.000132954,0.0002712088,0.01567081,0.0001133353,0.0001628952,0.007373229,0.00587726,0.02124351],"study_design_scores_gemma":[0.00001458258,0.00003100659,0.9438753,0.0002707862,0.00007022182,0.00007583027,0.04940968,0.0002061452,0.00006934099,0.001237164,0.004699446,0.00004060106],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9595714,0.003889662,0.0002074661,0.01547404,0.0001297124,0.00006211496,0.002207039,0.00002036664,0.01843813],"genre_scores_gemma":[0.9955621,0.00126266,0.0001208571,0.00103426,0.00003163251,0.00001726615,0.0003187646,0.00001080785,0.001641649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04755864,"threshold_uncertainty_score":0.3450637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01656023236662784,"score_gpt":0.2862816452714241,"score_spread":0.2697214129047963,"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."}}