{"id":"W2343414943","doi":"10.1080/13557858.2016.1179725","title":"South Asian-White health inequalities in Canada: intersections with gender and immigrant status","year":2016,"lang":"en","type":"article","venue":"Ethnicity and Health","topic":"Racial and Ethnic Identity Research","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge; University of British Columbia","funders":"Canadian Institutes of Health Research; Social Sciences and Humanities Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Immigration; Demography; Health equity; Odds; Intersectionality; Race and health; Inequality; Medicine; Logistic regression; Gerontology; Socioeconomic status; Gender studies; Geography; Sociology; Public health; Population","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":[0.001706923,0.0003474354,0.0006122082,0.002469889,0.004587623,0.001902628,0.001246343,0.0003486769,0.004387591],"category_scores_gemma":[0.004552048,0.0002509838,0.001067717,0.005664886,0.001723333,0.0007450197,0.003684762,0.0009236337,0.0001232904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0227068,"about_ca_system_score_gemma":0.0425845,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9894197,"about_ca_topic_score_gemma":0.9936723,"domain_scores_codex":[0.9985021,0.0001776667,0.00005308676,0.0002581978,0.0003032621,0.0007056653],"domain_scores_gemma":[0.9976541,0.0004042464,0.0005322077,0.0001810486,0.0006677812,0.0005605484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007435038,0.00002343768,0.9869592,0.00003465579,0.00009116281,0.00004871206,0.002004739,0.0002370339,0.00007580249,0.002373394,0.0009320203,0.007145486],"study_design_scores_gemma":[0.000007057155,0.00001342279,0.9906171,0.0000842485,0.0000786555,0.00003261547,0.0045373,0.001436218,0.00008113357,0.001306732,0.001790837,0.00001464564],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842494,0.00101968,0.0008211488,0.001666967,0.00002048408,0.00004637533,0.003561836,0.00001823552,0.008595674],"genre_scores_gemma":[0.9979079,0.0002454631,0.0002767647,0.00008454123,0.000005286362,0.00001809328,0.000857252,0.000004152761,0.0006005955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0227068,"threshold_uncertainty_score":0.1647502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1181745682124904,"score_gpt":0.3781581294538753,"score_spread":0.2599835612413849,"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."}}