{"id":"W3089243136","doi":"10.1101/2020.09.23.20200147","title":"Identifying gaps in COVID-19 health equity data reporting in Canada using a scorecard approach","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Balanced scorecard; Equity (law); Geography; Jurisdiction; Health care; Health equity; Population; Ethnic group; Coronavirus disease 2019 (COVID-19); Medicine; Demography; Business; Actuarial science; Environmental health; Political science; Economic growth; Economics; Sociology","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":[0.00982523,0.0003398212,0.001761833,0.0004627997,0.0001311739,0.0001444888,0.001155414,0.0002692644,0.00001881242],"category_scores_gemma":[0.002499522,0.0003593248,0.000105088,0.0006203668,0.00003255889,0.0002116812,0.002873975,0.001423102,0.000007483821],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.00990608,"about_ca_system_score_gemma":0.01315292,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9961777,"about_ca_topic_score_gemma":0.9535906,"domain_scores_codex":[0.9919868,0.0001481758,0.0051272,0.001720072,0.0001736389,0.0008440926],"domain_scores_gemma":[0.9928308,0.00003099,0.004953158,0.001593068,0.0000228429,0.0005691385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007238511,0.00002354611,0.9845489,0.006620103,0.00003166898,0.0002302774,0.001465851,0.00449826,0.000002148127,0.0005076901,0.0001211832,0.001943194],"study_design_scores_gemma":[0.001542558,0.00003629988,0.7156038,0.003478955,0.000007374876,0.0001436348,0.003718284,0.2455151,0.000005690333,0.01680566,0.01103063,0.002111966],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9715664,0.009445583,0.008922334,0.003557048,0.002629459,0.001409234,0.0008824634,0.00004828824,0.001539177],"genre_scores_gemma":[0.9951321,0.0006476351,0.002246119,0.001202653,0.0002576758,0.00004136032,0.0003981347,0.00005673437,0.00001753626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.268945,"threshold_uncertainty_score":0.9998859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5315080151436686,"score_gpt":0.4165154793664753,"score_spread":0.1149925357771934,"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."}}