{"id":"W3109777503","doi":"10.1017/cem.2020.471","title":"Breaking down the silos in simulation-based education: Exploring, refining, and standardizing","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Simulation-Based Education in Healthcare","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Queen's University","funders":"","keywords":"Information silo; Refining (metallurgy); Medicine; Action (physics); Content (measure theory); Engineering; Mechanical engineering; Metallurgy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1172521,0.001150831,0.0009765979,0.004953708,0.006484118,0.025427,0.003386344,0.002527306,0.003567418],"category_scores_gemma":[0.1585139,0.001016166,0.00122205,0.003044904,0.01981111,0.02115818,0.01742577,0.008141126,0.001221104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01101889,"about_ca_system_score_gemma":0.03422853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009192139,"about_ca_topic_score_gemma":0.01453119,"domain_scores_codex":[0.9066359,0.06731518,0.005460512,0.003485808,0.01403902,0.003063645],"domain_scores_gemma":[0.8462337,0.09068381,0.009313933,0.02196568,0.02559127,0.006211539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003219121,0.0005206265,0.02028536,0.001880159,0.0002343885,0.0001493816,0.1007502,0.01451003,0.002960394,0.3989651,0.01197323,0.4474493],"study_design_scores_gemma":[0.000127456,0.0008546843,0.01309456,0.01234081,0.0002882244,0.0002690991,0.08884428,0.02820479,0.008389762,0.6183433,0.2289041,0.0003388695],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1694408,0.01005437,0.583776,0.1197183,0.002635575,0.001347427,0.0002804578,0.002481797,0.1102653],"genre_scores_gemma":[0.7807615,0.004431998,0.2030511,0.005276856,0.0003401757,0.0007003357,0.0002613432,0.0007151934,0.00446151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1172521,"threshold_uncertainty_score":0.620096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2954710153746135,"score_gpt":0.4257099985118304,"score_spread":0.1302389831372169,"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."}}