{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005035388,0.0003309181,0.0003726393,0.001262767,0.004792423,0.001410491,0.0008976386,0.000482769,0.003168975],"category_scores_gemma":[0.001576053,0.0002492589,0.0004369858,0.001827053,0.0009236597,0.000528357,0.00121976,0.000782747,0.0001638124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02155763,"about_ca_system_score_gemma":0.02157363,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9857953,"about_ca_topic_score_gemma":0.9956836,"domain_scores_codex":[0.9994091,0.0000577602,0.00002777911,0.00005513452,0.0001171714,0.0003332049],"domain_scores_gemma":[0.998702,0.00005523277,0.0002779423,0.00002034262,0.0003646204,0.0005796733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001551618,0.0001213054,0.9642062,0.0001159387,0.00007764847,0.0005353863,0.008153163,0.00009776374,0.0003984884,0.0002960713,0.006736393,0.01910646],"study_design_scores_gemma":[0.00001059416,0.00007064844,0.9822588,0.000109727,0.00001830946,0.0001492296,0.01457498,0.0000983737,0.00004847762,0.00003798095,0.002594523,0.00002824098],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943855,0.00081499,0.00003798798,0.001361819,0.00003757668,0.00004450416,0.001196537,0.000008834243,0.002112403],"genre_scores_gemma":[0.996546,0.0007545144,0.0001005908,0.0004466664,0.00001481414,0.00002384144,0.0004118301,0.000004994142,0.001696759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02155763,"threshold_uncertainty_score":0.1564123,"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."}}