{"id":"W2889867277","doi":"10.23889/ijpds.v3i4.952","title":"Measuring equity in per capita primary care investment in Ontario: Challenges for data linkage and analysis","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Institute for Clinical Evaluative Sciences","funders":"","keywords":"Capitation; Payment; Equity (law); Incentive; Business; Proxy (statistics); Actuarial science; Per capita; Primary care; Family medicine; Medicine; Finance; Economics; Environmental health","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":[0.07327932,0.0006547643,0.001367132,0.005813281,0.004466412,0.00733367,0.004473279,0.001054381,0.001917569],"category_scores_gemma":[0.1598397,0.0009264617,0.0009898518,0.02753478,0.002811022,0.002458787,0.006269739,0.001109938,0.0002419983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09178854,"about_ca_system_score_gemma":0.1083889,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9606664,"about_ca_topic_score_gemma":0.9564718,"domain_scores_codex":[0.9078687,0.03902543,0.01035869,0.005331971,0.03311319,0.004302053],"domain_scores_gemma":[0.8310018,0.0553566,0.03335932,0.01457412,0.06161873,0.004089465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001987794,0.00009272391,0.853804,0.001832082,0.0008441791,0.0001088086,0.006940856,0.002571969,0.0001910087,0.008620757,0.02090665,0.1038882],"study_design_scores_gemma":[0.00009297275,0.0001015272,0.9442138,0.001770454,0.0003055386,0.00007074653,0.004549159,0.0072125,0.0003474129,0.004483137,0.03677605,0.00007658881],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6953471,0.02410954,0.05708938,0.0584707,0.0005418335,0.007093297,0.07363504,0.0006048508,0.08310822],"genre_scores_gemma":[0.9394059,0.004153712,0.03460445,0.002734525,0.0002238133,0.004670952,0.01101801,0.00009420864,0.003094394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09178854,"threshold_uncertainty_score":0.6659756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4499955626573608,"score_gpt":0.5362889808120167,"score_spread":0.08629341815465585,"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."}}