{"id":"W2101445798","doi":"10.1186/1472-6963-10-170","title":"The impact of different benefit packages of Medical Financial Assistance Scheme on health service utilization of poor population in Rural China","year":2010,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto; Department for International Development","keywords":"Reimbursement; Medicine; Nursing research; Health administration; Health informatics; Payment; Public health; Service (business); Environmental health; Population; Rural area; Health services research; China; Health care; Business; Nursing; Finance; Economic growth; Marketing; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.00656677,0.000154934,0.0007459352,0.0004391261,0.0002332696,0.00002234978,0.0005599146,0.0002601143,0.00005672428],"category_scores_gemma":[0.0001771704,0.0001022368,0.0001144515,0.00100009,0.00007078463,0.0001208614,0.0001050723,0.0006717842,0.000007292606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003216336,"about_ca_system_score_gemma":0.0007196919,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2201484,"about_ca_topic_score_gemma":0.1217672,"domain_scores_codex":[0.9964135,0.0002140253,0.001868742,0.0002978662,0.0005357562,0.0006700957],"domain_scores_gemma":[0.9977279,0.0001694541,0.001069544,0.0005139993,0.0002251037,0.0002940265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001949022,0.0003106974,0.9338968,0.004746526,0.00001143986,2.164552e-7,0.00178821,0.00003967491,0.00002396739,0.04150261,0.00001508225,0.01746982],"study_design_scores_gemma":[0.0005602561,0.0004827589,0.984009,0.0007001534,1.502017e-7,7.814771e-7,0.0004072371,0.01046773,0.00002678308,0.003177521,0.00009384593,0.00007382558],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943408,0.001401599,0.00003326816,0.002875169,0.0002526973,0.0007811308,0.0001867239,0.000007850948,0.0001207162],"genre_scores_gemma":[0.9980272,0.001556803,0.00006404649,0.0001030805,0.0001082254,0.00003037354,0.00006528319,0.00001985481,0.00002511285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09838119,"threshold_uncertainty_score":0.8942583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07134967962549872,"score_gpt":0.4113548047538799,"score_spread":0.3400051251283812,"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."}}