{"id":"W1972015562","doi":"10.1023/b:hcan.0000005496.74131.a0","title":"Resource Allocation in Health Care: Health Economics and Beyond","year":2003,"lang":"en","type":"article","venue":"Health Care Analysis","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Canadian Health Services Research Foundation","keywords":"Health informatics; Health care; Macro; Resource allocation; Health administration; Health economics; Health services research; Health care rationing; Public relations; Health policy; Scarcity; Service (business); Business; Knowledge management; Economics; Computer science; Political science; Marketing; Management; Economic growth","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002097158,0.0002358764,0.001094161,0.001312132,0.000363579,0.00006762732,0.0001538079,0.0001053185,0.00004392382],"category_scores_gemma":[0.00006502473,0.0003088019,0.0001661159,0.001288861,0.00004110304,0.0001394862,0.00004712413,0.0002349278,0.00004597399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002112781,"about_ca_system_score_gemma":0.0006440411,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05191663,"about_ca_topic_score_gemma":0.05730137,"domain_scores_codex":[0.9963662,0.0002641733,0.001670517,0.0007997875,0.00005754249,0.0008417911],"domain_scores_gemma":[0.9979036,0.00005857518,0.0008845173,0.0006026613,0.00004011764,0.0005104741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001636331,0.000107578,0.0209677,0.001546986,0.0003483902,0.000002175675,0.04016382,0.002958887,2.163677e-8,0.8320596,0.005310805,0.09651774],"study_design_scores_gemma":[0.0006517166,0.000259823,0.01316334,0.000025049,0.00001671683,0.000001481908,0.009865152,0.001025016,8.640795e-7,0.001372903,0.9732912,0.0003267867],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.05512697,0.4171833,0.01323417,0.458299,0.0008243137,0.003871311,0.001110559,0.0002404054,0.05010997],"genre_scores_gemma":[0.9288977,0.02201482,0.002301702,0.04604894,0.00007105206,0.0000711706,0.0003085449,0.00003824765,0.0002478517],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9679803,"threshold_uncertainty_score":0.9999364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02678470800011233,"score_gpt":0.2863562587359678,"score_spread":0.2595715507358554,"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."}}