{"id":"W4298127463","doi":"10.1016/j.ssmph.2022.101238","title":"Time availability as a mediator between socioeconomic status and health","year":2022,"lang":"en","type":"article","venue":"SSM - Population Health","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Neurosciences Foundation; University of California Berkeley; Paul and Daisy Soros Fellowships for New Americans; National Science Foundation","keywords":"Scarcity; Mediation; Socioeconomic status; Sample (material); Time allocation; Poverty; Set (abstract data type); Logistic regression; Psychology; Environmental health; Survey data collection; Demographic economics; Medicine; Economics; Statistics; Sociology; Economic growth; Population; Computer science; Mathematics","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004262433,0.0001083265,0.0003756671,0.00008469685,0.002847542,0.00004234499,0.0001173645,0.00005428103,0.002646693],"category_scores_gemma":[0.0001164443,0.0001252468,0.00005009927,0.0001391996,0.0001078096,0.0001762508,0.00007600538,0.0002587647,0.0001207598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002319379,"about_ca_system_score_gemma":0.002723925,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1711343,"about_ca_topic_score_gemma":0.01015071,"domain_scores_codex":[0.9968311,0.0009870694,0.0006052303,0.0003243921,0.000387915,0.000864273],"domain_scores_gemma":[0.9984226,0.0002378579,0.0003384123,0.0001740486,0.00002286496,0.0008042564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000652623,0.00002192593,0.9541632,0.0001077453,0.000005456624,1.466568e-7,0.01690865,0.000008785933,1.341919e-8,0.01032844,0.009450088,0.008999026],"study_design_scores_gemma":[0.0002953363,0.0001056745,0.883571,0.00000608865,0.000002191158,2.537857e-7,0.006732993,0.00003341346,3.516474e-8,0.004840337,0.1043044,0.0001082885],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9034609,0.00108818,0.000003024581,0.09355436,0.0003764539,0.0007417097,0.0001352565,0.0001146988,0.0005254157],"genre_scores_gemma":[0.9803213,0.0004934091,0.0001218375,0.01754536,0.0003472778,0.00004442882,0.0002004616,0.00001608103,0.0009098614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1609836,"threshold_uncertainty_score":0.9984506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04667829509136511,"score_gpt":0.394420811801967,"score_spread":0.3477425167106019,"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."}}