{"id":"W2738894580","doi":"10.1177/1460458217719562","title":"A low-fidelity serious game for medical-based cultural competence education","year":2017,"lang":"en","type":"article","venue":"Health Informatics Journal","topic":"Simulation-Based Education in Healthcare","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pace; Cultural competence; Usability; Health care; Competence (human resources); Psychology; Serious game; Medical education; Cultural diversity; Fidelity; Medicine; Computer science; Multimedia; Pedagogy; Social psychology; Sociology; Human–computer interaction; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001126187,0.001240155,0.0004084142,0.0005218999,0.0003736049,0.0009308938,0.001419761,0.0007686485,0.007895757],"category_scores_gemma":[0.004020175,0.0002788008,0.0006194037,0.0001243063,0.0004293818,0.0009230979,0.001913101,0.0006813087,0.0009105938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003938456,"about_ca_system_score_gemma":0.0006186471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001209618,"about_ca_topic_score_gemma":0.001969637,"domain_scores_codex":[0.9994492,0.0002741821,0.00004607418,0.0000657092,0.00009231191,0.00007246045],"domain_scores_gemma":[0.9985437,0.0008864873,0.00005512357,0.00007089342,0.00009815992,0.0003456037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009746736,0.01344351,0.02350177,0.00488321,0.0003981997,0.00445237,0.01465854,0.03869136,0.2127646,0.0171259,0.04904636,0.6112874],"study_design_scores_gemma":[0.005666518,0.0361917,0.09885583,0.002126162,0.00058557,0.01256197,0.006008547,0.409736,0.07976943,0.02870876,0.3187099,0.001079642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.586715,0.0005724232,0.3755758,0.001044245,0.0003880896,0.005511569,0.001239862,0.007989454,0.02096345],"genre_scores_gemma":[0.7191268,0.0003377968,0.2646494,0.0004874068,0.00004232073,0.003062428,0.001235692,0.0002020358,0.01085607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007895757,"threshold_uncertainty_score":0.02641398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05812984396441771,"score_gpt":0.457930561111922,"score_spread":0.3998007171475043,"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."}}