{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008970001,0.0002336339,0.0005603736,0.0002736909,0.0002186977,0.00003436497,0.000487681,0.0002104306,0.0001392766],"category_scores_gemma":[0.00009197215,0.0002796394,0.00009419657,0.0003935487,0.00005277151,0.001608686,0.00007437203,0.000468297,0.0001638192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008954178,"about_ca_system_score_gemma":0.0005810207,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7642426,"about_ca_topic_score_gemma":0.5707136,"domain_scores_codex":[0.9967098,0.00007581476,0.0009501361,0.0005039963,0.00007876159,0.001681523],"domain_scores_gemma":[0.9979712,0.00007438173,0.0002993823,0.00103315,0.00006365165,0.0005582664],"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":[0.00002107319,0.00006302071,0.04196434,0.0005073457,0.000084427,0.000002709701,0.004240731,0.000001075653,0.000004180618,0.9057416,0.005093797,0.04227564],"study_design_scores_gemma":[0.0002950666,0.0001218417,0.003765798,0.00004258601,0.000007745985,0.000006972071,0.0002746721,0.00007828188,0.0001029827,0.004940578,0.9900827,0.0002807979],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2830201,0.1681499,0.008122294,0.5133059,0.002597791,0.004552806,0.008165596,0.0003012771,0.01178433],"genre_scores_gemma":[0.9811552,0.001306337,0.0009536087,0.01567303,0.0003304199,0.00004981919,0.0002705633,0.00005027344,0.0002107854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9849889,"threshold_uncertainty_score":0.9999655,"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."}}