{"id":"W4394763394","doi":"10.1097/acm.0000000000005700","title":"Bibliometric Networks for Researchers in Health Professions Education","year":2024,"lang":"en","type":"article","venue":"Academic Medicine","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Network for Innovation in Education; Université de Montréal","funders":"","keywords":"Multidisciplinary approach; Social network analysis; Bibliometrics; Field (mathematics); Set (abstract data type); Health science; Network analysis; Data science; Computer science; Library science; Medical education; Sociology; Medicine; Social science; World Wide Web; Engineering; Social media","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.008427724,0.001006135,0.001058689,0.03226575,0.001843919,0.005664767,0.001634769,0.001290827,0.02467332],"category_scores_gemma":[0.08434156,0.0006526551,0.001667532,0.04059216,0.0007937162,0.0061986,0.004067927,0.001450989,0.006810263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004158816,"about_ca_system_score_gemma":0.003413332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005477165,"about_ca_topic_score_gemma":0.006553397,"domain_scores_codex":[0.9908047,0.004166103,0.001364807,0.0007931487,0.002614744,0.0002564907],"domain_scores_gemma":[0.9755182,0.01444097,0.003757336,0.00291314,0.002840721,0.0005295849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002529808,0.0002459071,0.02868365,0.003170197,0.000608477,0.0003742447,0.002593624,0.02426177,0.001286746,0.2544988,0.1521621,0.5318614],"study_design_scores_gemma":[0.00008073833,0.0001228143,0.03405926,0.001551434,0.0002849597,0.00104068,0.001330958,0.1044221,0.001949927,0.3035354,0.5514732,0.0001484857],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05027784,0.007962437,0.6443403,0.01170555,0.001149498,0.002760897,0.1046989,0.02682035,0.1502842],"genre_scores_gemma":[0.1852958,0.005741373,0.73779,0.0004014719,0.0006222972,0.005126261,0.05113868,0.001759181,0.012125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9677343,"threshold_uncertainty_score":0.08254045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8012703957469408,"score_gpt":0.7019638239757008,"score_spread":0.09930657177124003,"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."}}