{"id":"W3025882023","doi":"10.3233/shti190141","title":"Educational Electronic Health Records at the University of Victoria: Challenges, Recommendations and Lessons Learned","year":2019,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Graduation (instrument); Work (physics); Context (archaeology); Health informatics; Informatics; Medical education; Health records; Open source; Health professionals; Knowledge management; Engineering management; Computer science; Engineering ethics; Medicine; Engineering; Nursing; Health care; Political science; Public health; Software","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":[],"consensus_categories":[],"category_scores_codex":[0.03687979,0.0007101467,0.0008539692,0.002012174,0.006674174,0.0144346,0.00505695,0.01030202,0.006877199],"category_scores_gemma":[0.0425552,0.0006418824,0.0009267362,0.003011832,0.003969392,0.01238355,0.007074392,0.009921324,0.002140853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01070286,"about_ca_system_score_gemma":0.09511287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1233426,"about_ca_topic_score_gemma":0.2184718,"domain_scores_codex":[0.9708137,0.01435246,0.001988634,0.001573208,0.005877342,0.005394697],"domain_scores_gemma":[0.9004056,0.03221083,0.003090615,0.002316818,0.02746146,0.0345147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002365374,0.001398186,0.02058055,0.009778984,0.000086511,0.003306949,0.02686448,0.002776348,0.001533467,0.02613685,0.2508181,0.6564831],"study_design_scores_gemma":[0.0001341298,0.0008892377,0.02865944,0.0368278,0.000141449,0.002630569,0.3443282,0.005959087,0.002116527,0.02680533,0.5510969,0.0004113445],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02013193,0.03282457,0.002724334,0.9347379,0.00181714,0.000139657,0.0001541903,0.0001829953,0.007287292],"genre_scores_gemma":[0.5152926,0.2379865,0.09553479,0.1108761,0.002417174,0.00085415,0.0009249888,0.0002960571,0.03581758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1233426,"threshold_uncertainty_score":0.2452493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1087896398687327,"score_gpt":0.452264569352518,"score_spread":0.3434749294837853,"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."}}