{"id":"W4387700511","doi":"10.36834/cmej.78045","title":"Lost in translation: the case for embedding newcomer care in medical education","year":2023,"lang":"en","type":"article","venue":"Canadian Medical Education Journal","topic":"Global Health Workforce Issues","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Embedding; Translation (biology); Computer science; Artificial intelligence; Biology; Genetics","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":[0.1741171,0.001285504,0.001917761,0.002978545,0.02963627,0.04742811,0.01152871,0.04904409,0.03338957],"category_scores_gemma":[0.3273555,0.002128839,0.002360306,0.003519142,0.1188377,0.08109909,0.07585374,0.06403722,0.01212476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01792191,"about_ca_system_score_gemma":0.06958748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01104844,"about_ca_topic_score_gemma":0.0112651,"domain_scores_codex":[0.7615584,0.1569364,0.01389674,0.01137549,0.0370121,0.01922087],"domain_scores_gemma":[0.607365,0.2468163,0.01875118,0.05663144,0.02995451,0.04048171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002030889,0.0002080135,0.001814555,0.001239276,0.00006276652,0.004596754,0.1565953,0.0003229229,0.0005819824,0.5564753,0.1946385,0.08326142],"study_design_scores_gemma":[0.000122071,0.000181467,0.0008103318,0.005093258,0.0000617359,0.004524484,0.0941595,0.0006064535,0.0006236176,0.2974139,0.5962005,0.0002026375],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.005175627,0.005608855,0.008551247,0.9324979,0.004941442,0.00007810556,0.00004038256,0.000183717,0.04292272],"genre_scores_gemma":[0.3930165,0.009353503,0.02525926,0.5014682,0.009387101,0.0008920384,0.0001558181,0.001864222,0.05860339],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.1741171,"threshold_uncertainty_score":0.92083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05646316544705317,"score_gpt":0.4936792686149397,"score_spread":0.4372161031678865,"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."}}