{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001863371,0.000475441,0.0004036435,0.001014722,0.001057734,0.001635818,0.0005717068,0.0004337727,0.01023276],"category_scores_gemma":[0.007781726,0.0002294562,0.001000787,0.001010956,0.0008552179,0.001508807,0.002467737,0.000865087,0.000257766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009015459,"about_ca_system_score_gemma":0.002132947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02982976,"about_ca_topic_score_gemma":0.03290183,"domain_scores_codex":[0.9988917,0.0005451108,0.0000556922,0.0001710624,0.0001556201,0.000180784],"domain_scores_gemma":[0.9941193,0.003393363,0.001237421,0.0003585172,0.000247762,0.0006435714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001699949,0.0002324109,0.978738,0.0001102567,0.0004619794,0.0001782959,0.001643197,0.000533846,0.0003214058,0.006794889,0.0005004993,0.01031509],"study_design_scores_gemma":[0.00002614052,0.0002401516,0.9846532,0.0001415099,0.0005471288,0.0001351259,0.003718087,0.002205622,0.0004784433,0.005155677,0.002667045,0.00003193756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833409,0.0009382857,0.00293916,0.001552166,0.000044639,0.00006550086,0.001193086,0.00002025037,0.009905986],"genre_scores_gemma":[0.9984302,0.0001589988,0.0005820928,0.00005007883,0.00001221216,0.00002630386,0.0001752598,0.000002079282,0.0005627685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02982976,"threshold_uncertainty_score":0.05931228,"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."}}