{"id":"W2967861689","doi":"10.4300/jgme-d-19-00051","title":"Using Gamification to Understand Accreditation in Postgraduate Medical Education","year":2019,"lang":"en","type":"article","venue":"Journal of Graduate Medical Education","topic":"Innovations in Medical Education","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Royal College of Physicians and Surgeons of Canada","funders":"","keywords":"Accreditation; Construct (python library); Context (archaeology); Engineering ethics; Perspective (graphical); Comprehension; Medical education; Psychology; Public relations; Political science; Computer science; Medicine; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003437392,0.0002408471,0.0005332322,0.001593126,0.0000712715,0.00004829125,0.000359126,0.0003698429,0.001041769],"category_scores_gemma":[0.01546898,0.0002154422,0.0001304238,0.002031339,0.0001130653,0.0004876851,0.00004152867,0.001095046,0.0001214584],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001366787,"about_ca_system_score_gemma":0.02241221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001172414,"about_ca_topic_score_gemma":0.00002038315,"domain_scores_codex":[0.9941988,0.0002125594,0.001670154,0.0003524192,0.003204978,0.0003610981],"domain_scores_gemma":[0.9960618,0.0001254744,0.0007391536,0.0004110028,0.002029066,0.0006334776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00100833,0.00935221,0.06853465,0.0009160379,0.0001647421,0.0000384163,0.008870284,0.0001192703,0.003557083,0.01011377,0.1213055,0.7760197],"study_design_scores_gemma":[0.01511109,0.00426494,0.7526041,0.02508538,0.0008648809,0.008465504,0.06676873,0.05375709,0.002362797,0.02166103,0.04689919,0.002155274],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9128551,0.0002213031,0.003960719,0.07527079,0.00596259,0.0007466907,7.252649e-7,0.0000215501,0.0009604815],"genre_scores_gemma":[0.972902,0.0001823883,0.01185729,0.01305962,0.001497283,0.00001858992,0.00009063666,0.00004498782,0.0003472137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7738644,"threshold_uncertainty_score":0.9998714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0684536881705877,"score_gpt":0.420257185407976,"score_spread":0.3518034972373882,"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."}}