{"id":"W4409074624","doi":"10.3389/frvir.2025.1389318","title":"Designing diversity: ethical virtual agents for effective dermatological training","year":2025,"lang":"en","type":"article","venue":"Frontiers in Virtual Reality","topic":"Body Image and Dysmorphia Studies","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pediatric Oncology Group","funders":"Westfälische Wilhelms-Universität Münster; Universität des Saarlandes; Bundesministerium für Bildung und Forschung","keywords":"Diversity (politics); Training (meteorology); Engineering ethics; Computer science; Psychology; Medical education; Human–computer interaction; Medicine; Engineering; Sociology; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001277627,0.0002608772,0.0006448541,0.0002217387,0.0005320142,0.00003157201,0.0003495064,0.0005834858,0.00003869308],"category_scores_gemma":[0.001149916,0.000251805,0.0001901458,0.0003684425,0.0004382298,0.00009326095,0.0004254068,0.0008016072,0.00001361353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001866637,"about_ca_system_score_gemma":0.00005788718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001287934,"about_ca_topic_score_gemma":0.00003305919,"domain_scores_codex":[0.9974526,0.000670622,0.0003968089,0.0006465961,0.0002173055,0.0006160385],"domain_scores_gemma":[0.9983273,0.001108392,0.00009535439,0.0002929097,0.00007885831,0.00009722649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.002436466,0.0004154393,0.1987681,0.00005877678,0.0009049074,0.0002119569,0.02448429,0.00007825415,0.0000545419,0.07534534,0.5229343,0.1743076],"study_design_scores_gemma":[0.01685821,0.001739254,0.7282321,0.0003998722,0.0006837955,0.00002851496,0.09187399,0.002906486,0.0006348052,0.08440086,0.07046058,0.001781516],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07639775,0.0003276496,0.8968788,0.002533547,0.003883518,0.001194823,0.00007349738,0.000162607,0.01854782],"genre_scores_gemma":[0.9925527,0.00001598921,0.002937012,0.002974084,0.00008859841,0.0002706115,0.00002449295,0.0000140519,0.001122452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.916155,"threshold_uncertainty_score":0.9999934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06821014597774129,"score_gpt":0.3642420578900166,"score_spread":0.2960319119122753,"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."}}