{"id":"W2895262614","doi":"10.23889/ijpds.v3i3.437","title":"Challenges Associated with Cross-Jurisdictional Analyses using Administrative Health Data and Primary Care Electronic Medical Records in Canada","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Calgary; University of Manitoba; Alberta Health Services; University of British Columbia; Manitoba Health","funders":"","keywords":"Custodians; Jurisdiction; Business; Data quality; Health care; Data access; Population health; Population; Medicine; Environmental health; Political science; Computer science; Geography; Database; Marketing","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07872761,0.0005975916,0.001007279,0.0092368,0.01834945,0.01444931,0.005404505,0.001503585,0.001332323],"category_scores_gemma":[0.1391839,0.001324572,0.0009781474,0.0206042,0.009200246,0.002841438,0.009878759,0.002304553,0.0001648453],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.1897042,"about_ca_system_score_gemma":0.4034259,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9971436,"about_ca_topic_score_gemma":0.9977605,"domain_scores_codex":[0.8911247,0.03702404,0.009421428,0.009338686,0.03969168,0.01339949],"domain_scores_gemma":[0.7991694,0.08311166,0.01354314,0.01297675,0.08077791,0.01042116],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004354189,0.0002299098,0.4926953,0.002053097,0.001001824,0.003098965,0.1614372,0.0102348,0.002449085,0.1008909,0.0252464,0.2002272],"study_design_scores_gemma":[0.0000866421,0.0001377485,0.7282824,0.002955757,0.0004618927,0.0007866348,0.1368162,0.01096043,0.001857591,0.01293186,0.1042735,0.0004493231],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7866781,0.01560603,0.03303078,0.08614263,0.0005857767,0.002826466,0.008567909,0.0005685271,0.06599383],"genre_scores_gemma":[0.9594754,0.002741497,0.02660424,0.005528649,0.00006622692,0.0005977285,0.001439739,0.0001387797,0.003407845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9212724,"threshold_uncertainty_score":0.9398283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3867880874018707,"score_gpt":0.4891489664041175,"score_spread":0.1023608790022469,"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."}}