{"id":"W3124998720","doi":"","title":"Population-Level Intervention and Information Collection in Dynamic Healthcare Policy","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Markov decision process; Partially observable Markov decision process; Population; Health care; Monotonic function; Actuarial science; Intervention (counseling); Markov process; Computer science; Econometrics; Medicine; Mathematics; Economics; Statistics; Environmental health","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01798013,0.001649386,0.003821169,0.00177444,0.001048478,0.003093034,0.003569493,0.004387368,0.0107624],"category_scores_gemma":[0.04880294,0.001878789,0.001780069,0.002070095,0.004221902,0.005195423,0.002650402,0.004367248,0.0006265828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00648124,"about_ca_system_score_gemma":0.006936001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006653884,"about_ca_topic_score_gemma":0.004327272,"domain_scores_codex":[0.9860078,0.01004345,0.0003841367,0.001814039,0.0008486993,0.000901936],"domain_scores_gemma":[0.9616624,0.03344711,0.002662201,0.000916349,0.0007131013,0.0005989972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001225315,0.0001553775,0.0008710292,0.0002038504,0.000112334,0.0001366092,0.0001522358,0.4811554,0.0003543845,0.5046165,0.0008676756,0.01125208],"study_design_scores_gemma":[0.0001470576,0.0001835816,0.0004085122,0.00006444322,0.00004953011,0.00004630613,0.00005818934,0.5180396,0.0002872542,0.4783422,0.002329181,0.00004409561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01111124,0.0004121849,0.9791307,0.003569555,0.00007376485,0.0003502792,0.0003360227,0.0001127723,0.004903521],"genre_scores_gemma":[0.6775624,0.001610567,0.3088835,0.001235974,0.0003221193,0.002205245,0.0004018371,0.00008639895,0.007691997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01798013,"threshold_uncertainty_score":0.09508914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0840389501912132,"score_gpt":0.4337004996604655,"score_spread":0.3496615494692523,"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."}}