{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03807056,0.0006678187,0.0005587655,0.00135615,0.006510336,0.01605708,0.002024416,0.005351644,0.01139768],"category_scores_gemma":[0.03563077,0.0005034136,0.0006873154,0.00193142,0.00572897,0.01818323,0.0144144,0.008315236,0.002293268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003819262,"about_ca_system_score_gemma":0.02963665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002403765,"about_ca_topic_score_gemma":0.006767488,"domain_scores_codex":[0.9868445,0.006751444,0.0004703423,0.0008025627,0.002372252,0.002758934],"domain_scores_gemma":[0.9338166,0.02223923,0.002852554,0.002987411,0.006512198,0.03159196],"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":[0.0002129822,0.0006377003,0.01004144,0.0006658129,0.00003141425,0.001211949,0.0173502,0.0003440725,0.0005934991,0.1405102,0.5306024,0.2977983],"study_design_scores_gemma":[0.0001019411,0.0003326829,0.004933329,0.001016355,0.00001739748,0.0005190874,0.03601899,0.0008141531,0.0004866635,0.07926808,0.8764223,0.00006903894],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.01690981,0.0120953,0.00300281,0.9293553,0.005494455,0.00007210961,0.00009769829,0.0001152582,0.03285738],"genre_scores_gemma":[0.4928602,0.05680116,0.01904361,0.3346782,0.02472056,0.0005911815,0.0005833959,0.0001946062,0.07052703],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.03807056,"threshold_uncertainty_score":0.2013388,"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."}}