{"id":"W2132720252","doi":"10.1080/19475683.2011.625975","title":"Analysing spatial accessibility to health care: a case study of access by different immigrant groups to primary care physicians in Toronto","year":2011,"lang":"en","type":"article","venue":"Annals of GIS","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Census; Geography; Ethnic group; Metropolitan area; Context (archaeology); Immigration; Health care; Mainland China; Mainland; Socioeconomics; Population; Public health; Medicine; China; Environmental health; Economic growth; Sociology; Nursing","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":[],"consensus_categories":[],"category_scores_codex":[0.0004958283,0.0001432877,0.0006176418,0.00007735504,0.0002267441,0.0000335412,0.0004145484,0.00005255685,0.00004912652],"category_scores_gemma":[0.00006457431,0.0001303451,0.0001037335,0.0003162542,0.00005152521,0.0002902904,0.000163274,0.00007580058,4.911718e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004326575,"about_ca_system_score_gemma":0.0002214721,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.964146,"about_ca_topic_score_gemma":0.9678327,"domain_scores_codex":[0.9977151,0.0003936721,0.0006209699,0.0003365059,0.0004383521,0.0004953654],"domain_scores_gemma":[0.9988567,0.00007383029,0.0002231505,0.0003355659,0.0002253277,0.0002853867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00009891174,0.0006752289,0.3502438,0.0003874109,0.00002484752,0.00002053248,0.5906042,0.000002598591,0.000006386342,0.00002432427,0.0004151005,0.0574966],"study_design_scores_gemma":[0.0002134137,0.0003950287,0.6262985,0.00008732682,0.00001048656,1.569458e-7,0.3726185,0.000001083149,0.0001428072,0.000009073081,0.0001143992,0.0001091175],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958669,0.0009646531,0.00004513403,0.001144755,0.000142766,0.0009862609,0.00009269969,0.00001439977,0.0007424477],"genre_scores_gemma":[0.9970816,0.00009078986,0.00003149062,0.002668084,0.00006192441,0.00003859333,0.000008165119,0.000009669244,0.00000971902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2760547,"threshold_uncertainty_score":0.5315319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1092194092076474,"score_gpt":0.4394989765963758,"score_spread":0.3302795673887284,"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."}}