{"id":"W4402035424","doi":"10.32920/26882503","title":"Improving Healthcare Data Usability for Clinicians and Patients","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Nursing Diagnosis and Documentation","field":"Nursing","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Toronto Metropolitan University; Canadian Institute for Health Information; Ontario Ministry of Labour","funders":"","keywords":"Usability; Health care; Computer science; Medicine; Human–computer interaction; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005344762,0.0002068778,0.0002932718,0.00006819497,0.0001198001,0.0002979908,0.0002745705,0.0002388542,0.00001316046],"category_scores_gemma":[0.0003455001,0.0001920392,0.00006967589,0.00004449532,0.00005289411,0.0001160342,0.001036992,0.00037829,0.000008211774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001693957,"about_ca_system_score_gemma":0.00005604617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00238633,"about_ca_topic_score_gemma":0.0007717568,"domain_scores_codex":[0.9980024,0.00006920096,0.0004835955,0.001031385,0.0001715603,0.0002418433],"domain_scores_gemma":[0.9984188,0.0002965113,0.000165465,0.0009140642,0.0001168006,0.00008836926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001782844,0.0002636473,0.05229339,0.004688194,0.00003495595,2.9213e-7,0.0007266431,0.000004163613,0.00001181219,0.0003129734,0.0496575,0.8918281],"study_design_scores_gemma":[0.008121516,0.00536953,0.2400827,0.009102661,0.003306699,0.000004048683,0.003988616,0.1448651,0.002443648,0.512594,0.06536965,0.004751735],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9187914,0.002378946,0.001085999,0.0407774,0.02681383,0.005067315,0.004095469,0.0004881377,0.0005015333],"genre_scores_gemma":[0.9870241,0.0000284936,0.007882551,0.001370379,0.0004917039,0.0001116227,0.002959005,0.00005653688,0.00007565437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8870764,"threshold_uncertainty_score":0.7831131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08111131650041353,"score_gpt":0.4166335162759749,"score_spread":0.3355221997755614,"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."}}