{"id":"W3029043819","doi":"10.1080/17441692.2020.1771396","title":"Unmet healthcare needs among migrants without medical insurance in Montreal, Canada","year":2020,"lang":"en","type":"article","venue":"Global Public Health","topic":"Migration, Health and Trauma","field":"Psychology","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Health care; Government (linguistics); Medicine; Medical prescription; Immigration; Population; Pandemic; Family medicine; Environmental health; Nursing; Economic growth; Coronavirus disease 2019 (COVID-19); Political science; Disease","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001035741,0.0002757293,0.0006119955,0.0001011643,0.0001919001,0.00003778716,0.0004760251,0.0002708842,0.0006337826],"category_scores_gemma":[0.0004019926,0.0002718523,0.00005372475,0.001735232,0.00008931877,0.0001603388,0.00004506026,0.0006107115,0.0000943585],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007938144,"about_ca_system_score_gemma":0.008303232,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9878703,"about_ca_topic_score_gemma":0.9977964,"domain_scores_codex":[0.9951138,0.0008051536,0.001024053,0.0005750118,0.0009356987,0.001546316],"domain_scores_gemma":[0.9955361,0.00007421453,0.0002587926,0.000402666,0.0001157873,0.003612462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006027902,0.00009937496,0.8246571,0.0001513559,0.00001657715,0.00005786624,0.004780544,0.00000193915,1.531854e-8,0.002623456,0.1244183,0.04313315],"study_design_scores_gemma":[0.001570808,0.0001797836,0.934056,0.00004510542,0.000001248372,0.00002509363,0.002647893,0.0001719874,4.878009e-8,0.00008396311,0.06103035,0.0001877365],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7414709,0.002135023,0.0001063618,0.2501991,0.0007265797,0.0005798772,0.0004166723,0.00010234,0.004263158],"genre_scores_gemma":[0.9130685,0.0001548438,0.00003821504,0.08617112,0.0002963021,0.00006000151,0.0001297609,0.00001855045,0.00006276017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1715976,"threshold_uncertainty_score":0.9999734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03567103522473361,"score_gpt":0.3286696203412213,"score_spread":0.2929985851164877,"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."}}