{"id":"W2887156066","doi":"10.17269/s41997-018-0110-1","title":"Geographic disparities in accessing community pharmacies among vulnerable populations in the Greater Toronto Area","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Geography; Catchment area; Vulnerability (computing); Index (typography); Pharmacy; Socioeconomics; Geographic information system; Population; Location; Environmental health; Medicine; Cartography; Drainage basin","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.004427991,0.00008962065,0.0002432226,0.0003261654,0.0005062427,0.0001488522,0.0002259172,0.00004211482,0.0003417031],"category_scores_gemma":[0.0006203947,0.00006094951,0.00005401199,0.0003206316,0.0002102691,0.0009525408,0.00001128637,0.0009561476,0.000001191211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004734657,"about_ca_system_score_gemma":0.001336257,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3528601,"about_ca_topic_score_gemma":0.7610184,"domain_scores_codex":[0.9976355,0.001137948,0.0004774079,0.00006079857,0.0001923695,0.000495985],"domain_scores_gemma":[0.9984785,0.0002252939,0.0002014175,0.0001631906,0.0001336541,0.0007979253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001003657,0.00004732165,0.9898482,0.00003674587,0.00001189568,0.00002762647,0.005768832,0.000002466197,0.000001168402,0.0002013373,0.0009245804,0.003119822],"study_design_scores_gemma":[0.0007090988,0.000174462,0.9796519,0.000118914,0.00001201911,0.00008590323,0.004773461,0.0002632604,0.000001240069,0.0001739736,0.01397878,0.00005699862],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9651795,0.0009854789,0.00004425838,0.03063029,0.0002873435,0.0001745061,0.000007502934,0.00000330407,0.002687789],"genre_scores_gemma":[0.992318,0.00008964339,0.0000719375,0.007319708,0.0001367034,0.000003299652,0.000007635378,0.000007339132,0.00004570338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4081583,"threshold_uncertainty_score":0.6514493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4758131757955417,"score_gpt":0.4677645071400694,"score_spread":0.008048668655472269,"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."}}