{"id":"W4291824829","doi":"10.3389/feduc.2022.978796","title":"Pre-licensure medical students’ knowledge and views on interprofessional learning: A qualitative concept analysis based on real-world data","year":2022,"lang":"en","type":"article","venue":"Frontiers in Education","topic":"Interprofessional Education and Collaboration","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Stiftung Suzanne und Hans Biäsch zur Förderung der Angewandten Psychologie; Saskatoon City Hospital Foundation; University of Bern","keywords":"Debriefing; Interprofessional education; Identifier; Computer science; Code (set theory); Medical education; Qualitative research; Component (thermodynamics); Psychology; Medicine; Health care; Programming language; Sociology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004125336,0.0002141937,0.0004298615,0.00118995,0.001038721,0.00002415319,0.0007855615,0.0001209454,0.002631663],"category_scores_gemma":[0.000925007,0.0001977262,0.00005536567,0.002195094,0.0001097514,0.0001538707,0.0004200345,0.001717382,0.00003586042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001098038,"about_ca_system_score_gemma":0.005233476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006670695,"about_ca_topic_score_gemma":0.002548373,"domain_scores_codex":[0.9904719,0.00668293,0.0007388538,0.0006795503,0.001114757,0.0003120028],"domain_scores_gemma":[0.9973247,0.001165559,0.0004920488,0.0006002189,0.0002167352,0.0002007522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001496802,0.001951067,0.4396034,0.00006321201,0.0001394933,9.707632e-7,0.1113492,0.0002625032,0.000002077262,0.002822964,0.4251161,0.0171921],"study_design_scores_gemma":[0.001461121,0.0004093471,0.4311156,0.0006465334,0.0001596452,2.915381e-7,0.3530961,0.03284847,0.000002083811,0.0006862394,0.1791787,0.0003959429],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9410924,0.0006174953,0.002571413,0.02123463,0.01901361,0.002476689,0.0002204147,0.0001222316,0.01265116],"genre_scores_gemma":[0.9602314,0.00003733096,0.00136025,0.005458735,0.0003483856,0.001265348,0.003191479,0.00002985672,0.0280772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2459375,"threshold_uncertainty_score":0.99828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06128326628304538,"score_gpt":0.538031277041354,"score_spread":0.4767480107583086,"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."}}