{"id":"W3081995743","doi":"10.36834/cmej.70401","title":"“COVID-19 as the equalizer”: Evolving discourses of COVID-19 and implications for medical education.","year":2020,"lang":"en","type":"article","venue":"Canadian Medical Education Journal","topic":"Child and Adolescent Health","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Computational biology; Computer science; Biology; Medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002277871,0.0001511114,0.0002692402,0.0001827353,0.002319834,0.00003736113,0.0005451955,0.0002792297,0.02499088],"category_scores_gemma":[0.194428,0.0001108541,0.00008780823,0.0003390683,0.0003305999,0.0001225634,0.00004694616,0.001224229,0.00002753839],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008682691,"about_ca_system_score_gemma":0.520327,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01584633,"about_ca_topic_score_gemma":0.0274442,"domain_scores_codex":[0.997101,0.0006063446,0.000886901,0.0002739742,0.0005941623,0.0005375952],"domain_scores_gemma":[0.9633198,0.001452287,0.0004417695,0.0002666286,0.0004259665,0.03409348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001677643,0.0001049901,0.01035166,0.0005303696,0.00002362744,0.000001357292,0.01535094,0.00000109015,0.000002109653,0.05260049,0.9036663,0.01735032],"study_design_scores_gemma":[0.0008070584,0.00005510848,0.006770082,0.0003743399,0.00004114156,0.0001602461,0.02996118,0.0001356874,2.822472e-7,0.01005297,0.9515026,0.0001392765],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.007301907,0.003319802,0.0018548,0.9838461,0.001598147,0.0008789626,0.0000706877,0.00002270162,0.001106847],"genre_scores_gemma":[0.3364363,0.0007153542,0.0002310143,0.6586183,0.003483009,0.00005743118,0.00006111482,0.00002179385,0.0003756904],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.5194588,"threshold_uncertainty_score":0.998979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06974636190868909,"score_gpt":0.4895513166665008,"score_spread":0.4198049547578117,"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."}}