{"id":"W7018433016","doi":"","title":"From diagnosis to discernment: fostering clinical judgement in high fidelity simulations","year":2012,"lang":"en","type":"other","venue":"Arca (British Columbia Electronic Library Network)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Clinical judgement; Competence (human resources); Fidelity; Clinical Practice; Context (archaeology); Clinical judgment","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02473158,0.0004663199,0.0002871217,0.001430778,0.006101621,0.008246453,0.002049375,0.001510711,0.004338149],"category_scores_gemma":[0.1236865,0.0003942938,0.0004287965,0.0005770532,0.0079686,0.00328366,0.01145814,0.003095215,0.0007880579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01206674,"about_ca_system_score_gemma":0.04573332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07441201,"about_ca_topic_score_gemma":0.1910625,"domain_scores_codex":[0.9707633,0.02223312,0.0007305184,0.0007900068,0.00407264,0.001410423],"domain_scores_gemma":[0.9240509,0.05097179,0.004245962,0.004749535,0.009004712,0.006977184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004387006,0.001334165,0.03909759,0.0007542738,0.00006446474,0.001943203,0.4244317,0.006485881,0.004957014,0.04049769,0.02720705,0.4527882],"study_design_scores_gemma":[0.0003605952,0.001157286,0.06197827,0.005483512,0.0001850506,0.002815927,0.4232072,0.04015714,0.01895427,0.1827356,0.2622477,0.0007174028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6182628,0.001125593,0.09465295,0.05020883,0.0005729524,0.001243653,0.0001338587,0.0006509057,0.2331484],"genre_scores_gemma":[0.9434116,0.0004552606,0.04969569,0.001123934,0.00004031057,0.0001522347,0.0000505711,0.00005379899,0.005016538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07441201,"threshold_uncertainty_score":0.1479578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967210215567958,"score_gpt":0.2630274435162519,"score_spread":0.2433553413605723,"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."}}