{"id":"W2181761912","doi":"10.25011/cim.v36i4.19950","title":"Strength in Numbers: Growth of Canadian Clinician Investigator Training in the 21st Century","year":2013,"lang":"en","type":"article","venue":"Clinical and investigative medicine","topic":"Health and Medical Research Impacts","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto; Ontario Medical Association; Western University","funders":"Air Force Materiel Command; Canadian Institutes of Health Research; Association of Faculties of Medicine of Canada","keywords":"Attrition; Workforce; Demographics; Medicine; Medical education; Training (meteorology); Work (physics); Family medicine; Grant funding; Baseline (sea); Political science; Geography; Demography","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02162551,0.0004091618,0.0005524194,0.002703509,0.004559307,0.004731415,0.00351998,0.001666017,0.01423453],"category_scores_gemma":[0.05020891,0.0004473297,0.000443151,0.004395311,0.004313351,0.001873614,0.004859123,0.003055678,0.001362988],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05346751,"about_ca_system_score_gemma":0.211429,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.835292,"about_ca_topic_score_gemma":0.8876534,"domain_scores_codex":[0.980876,0.002805771,0.0004857399,0.001513767,0.009706343,0.004612476],"domain_scores_gemma":[0.9110166,0.006597899,0.007279671,0.003397241,0.02917347,0.04253514],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004267649,0.0002075404,0.2626637,0.001421276,0.0001114922,0.0003945575,0.01169495,0.0008399922,0.001661098,0.03951175,0.2504205,0.4306463],"study_design_scores_gemma":[0.00008850582,0.0001932196,0.5322836,0.0008071088,0.00003953276,0.000391843,0.006689348,0.0006817499,0.000532449,0.003520398,0.4546943,0.00007780123],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3291572,0.02721527,0.006374743,0.4599717,0.006227388,0.0005881236,0.005111349,0.001117272,0.164237],"genre_scores_gemma":[0.9147577,0.01324992,0.008094091,0.02755157,0.001280935,0.0002369598,0.001517426,0.0001843104,0.03312703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9783745,"threshold_uncertainty_score":0.3879358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4090192064507953,"score_gpt":0.4403633427107374,"score_spread":0.03134413625994209,"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."}}