{"id":"W4382982623","doi":"10.3390/ijerph20136265","title":"A Spatio-Temporal Analysis of OECD Member Countries’ Health Care Systems: Effects of Data Missingness and Geographically and Temporally Weighted Regression on Inference","year":2023,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Missing data; Health care; Econometrics; Proxy (statistics); Imputation (statistics); Causal inference; Ordinary least squares; Per capita; Inference; Statistical inference; Statistics; Actuarial science; Computer science; Economics; Environmental health; Medicine; Mathematics; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002635827,0.0001062251,0.0005753502,0.001714177,0.0001183626,0.0001108283,0.0003786804,0.00005961593,0.00007632615],"category_scores_gemma":[0.0002073381,0.00009028782,0.00005318358,0.0004556652,0.0002517887,0.0003890261,0.000278444,0.0002145801,0.000002476638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001101329,"about_ca_system_score_gemma":0.000174659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00258329,"about_ca_topic_score_gemma":0.0002818589,"domain_scores_codex":[0.997922,0.0001734275,0.0009087111,0.000291722,0.0004571459,0.0002469946],"domain_scores_gemma":[0.9979361,0.0004896672,0.0009006653,0.0002421724,0.0001016385,0.0003297585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001819838,0.0002464266,0.9720665,0.000347268,0.001025632,0.00002147923,0.001012997,0.00001079253,0.00001319772,0.002612142,0.0004917327,0.02196988],"study_design_scores_gemma":[0.001542881,0.001698862,0.9614832,0.0004179811,0.00003991128,0.00001402101,0.001400402,0.009108979,0.00001257637,0.0009140256,0.02317742,0.0001897465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822959,0.01044091,0.0002786552,0.004580945,0.00009632218,0.0001574667,0.00210456,0.000003328734,0.00004190325],"genre_scores_gemma":[0.9778469,0.02105231,0.000148367,0.0001050983,0.00004391835,0.000002465811,0.0007703342,0.000007158807,0.00002348018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02268569,"threshold_uncertainty_score":0.3905178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1111509085493074,"score_gpt":0.3793365407276782,"score_spread":0.2681856321783708,"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."}}