{"id":"W3024295830","doi":"","title":"The Big Data Revolution: Opportunities for Chief Nurse Executives","year":2017,"lang":"en","type":"article","venue":"ElectronicHealthcare","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Health informatics; Transformational leadership; Informatics; eHealth; Nursing; Health Administration Informatics; Health care; Medicine; Big data; Public relations; Medical education; Political science; Public health; Computer science","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","sts"],"consensus_categories":[],"category_scores_codex":[0.005527303,0.0003736485,0.0005870228,0.00007159646,0.03160411,0.0002185541,0.003206093,0.0004350654,0.00002138514],"category_scores_gemma":[0.002429082,0.000280199,0.0001145324,0.0001048148,0.0003458337,0.0004280458,0.0004272031,0.001817928,0.00008643426],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001622208,"about_ca_system_score_gemma":0.012747,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002690829,"about_ca_topic_score_gemma":0.03712436,"domain_scores_codex":[0.9930279,0.00138092,0.001213941,0.0008871059,0.0004707442,0.003019365],"domain_scores_gemma":[0.9904973,0.001781678,0.001367715,0.005284064,0.000607489,0.0004617606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006307932,0.00007684089,0.01081819,0.001986706,0.0001731591,0.000009188492,0.001793659,3.257641e-7,0.00006816884,0.106457,0.4968358,0.3811502],"study_design_scores_gemma":[0.001117224,0.0004456112,0.004902669,0.0003822946,0.00003235934,0.00001045762,0.006784809,0.0004043715,0.00001682898,0.003503669,0.9821005,0.0002992057],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04501294,0.1186123,0.00328897,0.6967883,0.0268156,0.02138472,0.001511374,0.001251381,0.0853344],"genre_scores_gemma":[0.8839628,0.01344333,0.0002572609,0.003772271,0.01125698,0.003830141,0.0006243899,0.0002323875,0.08262043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8389499,"threshold_uncertainty_score":0.999965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4224647239800451,"score_gpt":0.5049728308621955,"score_spread":0.08250810688215032,"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."}}