{"id":"W4400695801","doi":"10.3389/feduc.2024.1369230","title":"Identifying factors influencing program selection in health sciences by underrepresented minority students—a scoping review","year":2024,"lang":"en","type":"article","venue":"Frontiers in Education","topic":"Medical Education and Admissions","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Underrepresented Minority; Selection (genetic algorithm); Computer science; Medical education; Psychology; Mathematics education; Knowledge management; Medicine; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008727954,0.0001054211,0.0002457824,0.0004325717,0.0001033528,0.00007989522,0.0001047369,0.00005402658,0.0001912426],"category_scores_gemma":[0.001152934,0.00009356455,0.00003969575,0.001546511,0.00006022181,0.0002259047,0.00001913296,0.0003039503,0.000004309186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006716101,"about_ca_system_score_gemma":0.004350002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000711236,"about_ca_topic_score_gemma":0.00006304529,"domain_scores_codex":[0.9983863,0.0001336636,0.0004790286,0.0003505079,0.0004044399,0.0002460669],"domain_scores_gemma":[0.9994323,0.00004057467,0.00008388494,0.00009148592,0.00003757634,0.0003141794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004787822,0.001027806,0.7109519,0.01417439,0.00001535993,8.433651e-7,0.002407143,0.000002934262,0.00007813026,0.00004315232,0.1425581,0.1287355],"study_design_scores_gemma":[0.0008646256,0.0003936493,0.4693536,0.4671707,0.000100491,0.00002919686,0.03183604,0.002464636,0.0003400324,0.0007778926,0.02610248,0.0005666748],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6824844,0.2793929,0.001915636,0.02193746,0.007276817,0.005185523,0.000002119114,0.0003073825,0.001497749],"genre_scores_gemma":[0.9366629,0.03724555,0.01540193,0.006505024,0.0003710839,0.000659661,0.0002078293,0.000034982,0.002911055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4529963,"threshold_uncertainty_score":0.7716718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05294262161445941,"score_gpt":0.4829466503586679,"score_spread":0.4300040287442085,"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."}}