{"id":"W2403397093","doi":"10.3233/978-1-58603-979-0-161","title":"Capturing Pan-Canadian Primary Health Care Indicator Data Using Multiple Approaches for Data Collection","year":2009,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Health Information","funders":"","keywords":"Data collection; Computer science; Primary care; Primary health care; Data mining; Data science; Health care; Medicine; Statistics; Family medicine; Political science; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08677022,0.002919666,0.002664349,0.0241307,0.007651473,0.009887125,0.004932363,0.00116355,0.002587929],"category_scores_gemma":[0.1450923,0.001880016,0.003450189,0.05499117,0.002087659,0.003569975,0.009970213,0.002788154,0.0005914467],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0531549,"about_ca_system_score_gemma":0.1000398,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8908244,"about_ca_topic_score_gemma":0.9263716,"domain_scores_codex":[0.8588925,0.05261309,0.01825291,0.009365492,0.05617619,0.004699831],"domain_scores_gemma":[0.9083944,0.02295755,0.008354628,0.01040809,0.04832963,0.0015557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003568996,0.0003896357,0.1921179,0.009085162,0.003347155,0.0004235003,0.03544706,0.01329636,0.003183282,0.03664631,0.02992403,0.6757827],"study_design_scores_gemma":[0.0003764827,0.0005250811,0.6457399,0.006382256,0.003264933,0.0004795302,0.04292334,0.06888643,0.008769172,0.04043382,0.181028,0.00119108],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.113219,0.006946231,0.7400144,0.006895239,0.0005373903,0.03958067,0.0376408,0.002548145,0.05261821],"genre_scores_gemma":[0.147979,0.002113918,0.8144872,0.0006279991,0.0000744544,0.02087897,0.01087469,0.0001879102,0.002775911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9468451,"threshold_uncertainty_score":0.4588903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3267414868155717,"score_gpt":0.4764769634375118,"score_spread":0.1497354766219401,"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."}}