{"id":"W2111153318","doi":"","title":"Transforming Healthcare through Better Use of Data: A Canadian Context","year":2012,"lang":"en","type":"article","venue":"ElectronicHealthcare","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data governance; Standardization; Leverage (statistics); Health care; Competitive advantage; Analytics; Data sharing; Context (archaeology); Big data; Business; Data science; Knowledge management; Marketing; Computer science; Data quality; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009314931,0.0006470978,0.0008625786,0.003985683,0.03605507,0.02171347,0.003334494,0.005817022,0.008174956],"category_scores_gemma":[0.0167927,0.0006968121,0.001073859,0.0132496,0.01768902,0.006804553,0.008154818,0.009769962,0.0005962547],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.261534,"about_ca_system_score_gemma":0.4267646,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9971774,"about_ca_topic_score_gemma":0.9979178,"domain_scores_codex":[0.9839951,0.002968503,0.000543604,0.00125353,0.005990739,0.005248658],"domain_scores_gemma":[0.9749441,0.00509006,0.0008320723,0.0006448728,0.009230263,0.00925865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002026649,0.0001778827,0.01457614,0.0007086065,0.0001132521,0.003990535,0.06509535,0.001304154,0.0008301394,0.6405954,0.221281,0.05112499],"study_design_scores_gemma":[0.00006141514,0.00006177229,0.01552933,0.001093424,0.00006716736,0.0009600121,0.07090115,0.0008772532,0.000321124,0.02716161,0.8826325,0.0003333098],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.05461609,0.026308,0.001915238,0.6956582,0.00212641,0.0001616232,0.001916945,0.00009418745,0.2172033],"genre_scores_gemma":[0.7320775,0.06174764,0.009181669,0.1412616,0.0007849645,0.0001533853,0.001324513,0.0002341522,0.0532345],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.738466,"threshold_uncertainty_score":0.8565159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1778800959350638,"score_gpt":0.325930273645493,"score_spread":0.1480501777104292,"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."}}