{"id":"W4404355437","doi":"10.36834/cmej.79440","title":"The productivity paradox in postgraduate medical education: improving asynchronous learning","year":2024,"lang":"en","type":"article","venue":"Canadian Medical Education Journal","topic":"Innovations in Medical Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Productivity; Asynchronous communication; Asynchronous learning; Computer science; Medical education; Data science; Psychology; Mathematics education; Medicine; Computer network; Economics; Synchronous learning; Teaching method; Cooperative learning; Economic growth","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01948997,0.0004301101,0.0005534864,0.001606863,0.001968647,0.008201292,0.002088708,0.003779649,0.02212844],"category_scores_gemma":[0.1230725,0.0002369624,0.0006196535,0.003605381,0.002514917,0.01066499,0.006298109,0.003655891,0.002690029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007496302,"about_ca_system_score_gemma":0.01591457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01026309,"about_ca_topic_score_gemma":0.008814791,"domain_scores_codex":[0.9845818,0.006039902,0.0007952368,0.0009307569,0.006393507,0.001258975],"domain_scores_gemma":[0.8609027,0.09575867,0.01023877,0.003858119,0.01808368,0.011158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008793336,0.0005048439,0.01618509,0.003374285,0.000128175,0.0003748622,0.00276294,0.002932972,0.001600392,0.1453368,0.172989,0.6529312],"study_design_scores_gemma":[0.001283616,0.001215442,0.0801046,0.005324856,0.0003233274,0.001147967,0.006861628,0.01007798,0.003606314,0.4494464,0.4402679,0.0003400514],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.08280101,0.06999997,0.03488837,0.6803567,0.01299712,0.0001837846,0.001301495,0.0005231093,0.1169485],"genre_scores_gemma":[0.8808791,0.03818672,0.01248178,0.03930265,0.01193953,0.0002891422,0.0005952057,0.0002249062,0.01610099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02212844,"threshold_uncertainty_score":0.1030741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007119258723929108,"score_gpt":0.3106964990550142,"score_spread":0.3035772403310851,"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."}}