{"id":"W4396531030","doi":"10.36834/cmej.76441","title":"Selection of international medical graduates into postgraduate training positions in Canada. Who applies? Who is selected?","year":2024,"lang":"en","type":"article","venue":"Canadian Medical Education Journal","topic":"Global Health Workforce Issues","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Workforce; Selection (genetic algorithm); Medical education; Training (meteorology); Personnel selection; Medicine; Computer science; Family medicine; Political science; Statistics; Artificial intelligence; Geography; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001428542,0.0001921974,0.0003495416,0.0007529607,0.0005553411,0.00003971652,0.0004665617,0.0003629912,0.03837724],"category_scores_gemma":[0.006194652,0.0001870175,0.00005555206,0.001350701,0.0001023871,0.0002047229,0.00002977085,0.002525092,0.00005834481],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.005860757,"about_ca_system_score_gemma":0.3444148,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9516541,"about_ca_topic_score_gemma":0.9955564,"domain_scores_codex":[0.9957061,0.0003945674,0.001235294,0.000295843,0.001550362,0.0008178779],"domain_scores_gemma":[0.9927626,0.0003718838,0.0002024803,0.0001219845,0.0008273915,0.005713616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002053582,0.00004885082,0.04028876,0.0002601326,0.00008232785,0.0001080116,0.01604753,0.000007092759,0.000007930894,0.005439708,0.8517516,0.08593751],"study_design_scores_gemma":[0.0008387014,0.00009200333,0.04485889,0.01267553,0.00004655992,0.0007662092,0.0289858,0.01453387,0.00002641554,0.004058894,0.8926525,0.0004646907],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.45582,0.002313164,0.0001399539,0.5203735,0.009676201,0.0006098222,0.00007601368,0.00005737304,0.01093404],"genre_scores_gemma":[0.9630414,0.001167937,0.0003071431,0.03219384,0.001740577,0.00007437849,0.00009191173,0.00003788075,0.001344986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5072213,"threshold_uncertainty_score":0.9997761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02094166568133783,"score_gpt":0.3937817829266897,"score_spread":0.3728401172453519,"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."}}