{"id":"W3123891191","doi":"","title":"The importance of individual heterogeneity in the decomposition of measures of socioeconomic inequality in health: An approach based on quantile regression","year":2002,"lang":"en","type":"preprint","venue":"Repositori digital de la UPF (Universitat Pompeu Fabra)","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quantile regression; Econometrics; Inequality; Economics; Quantile; Regression; Health equity; Socioeconomic status; Regression analysis; Economic inequality; Mathematics; Statistics; Medicine; Health care; Environmental health; Population; Economic growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003351494,0.0002052124,0.0005774462,0.0001836202,0.0003340977,0.000136355,0.000919467,0.000346621,0.000003361038],"category_scores_gemma":[0.000193731,0.000163975,0.0002284947,0.0002290719,0.0004494103,0.0003356146,0.0001597189,0.0005110233,3.084251e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005457811,"about_ca_system_score_gemma":0.0007118707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005649999,"about_ca_topic_score_gemma":0.001683285,"domain_scores_codex":[0.9959363,0.001745541,0.0008112884,0.0003700581,0.0007329248,0.0004038764],"domain_scores_gemma":[0.9969241,0.001239802,0.001073885,0.0005294615,0.0001144393,0.0001182814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002377117,0.001043365,0.9382134,0.001098715,0.00005610624,0.00001353287,0.03344502,0.002784474,0.00001315888,0.01871798,0.0003451719,0.004031338],"study_design_scores_gemma":[0.0009027438,0.0002842573,0.9358054,0.0008442875,0.0000290371,0.000002755966,0.05517505,0.003126778,0.0001798727,0.002529185,0.000756658,0.0003639358],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920624,0.0005335443,0.0001236017,0.001790229,0.0002435219,0.0005511509,0.0001809105,0.00001636426,0.004498297],"genre_scores_gemma":[0.9992285,0.0001523479,0.0002872619,0.0001697463,0.00006464477,0.000009104027,0.00006243589,0.00001193365,0.00001406141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02173003,"threshold_uncertainty_score":0.8541147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05579267314855251,"score_gpt":0.3540502365049659,"score_spread":0.2982575633564133,"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."}}