{"id":"W3110008023","doi":"10.2196/19061","title":"Predictors of Internet Use Among Older Adults With Diabetes in South Korea: Survey Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"The Internet; Gerontology; Logistic regression; Digital divide; Odds; Medicine; Odds ratio; Internet access; Educational attainment; Information and Communications Technology; Sample (material); Psychology; Demography; Economic growth; Computer science; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0005234975,0.0001884381,0.000226728,0.0008114672,0.0002935152,0.0005023039,0.0002431631,0.0002925634,0.001474926],"category_scores_gemma":[0.001283825,0.0002428855,0.0003843243,0.001258398,0.0001132139,0.0004765556,0.0005127645,0.0005192069,0.0002722867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003226745,"about_ca_system_score_gemma":0.0003898052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01137016,"about_ca_topic_score_gemma":0.01872288,"domain_scores_codex":[0.9997013,0.00005198166,0.00009568088,0.00005789701,0.00004632767,0.00004671415],"domain_scores_gemma":[0.9990079,0.0001027269,0.000523013,0.00004599572,0.0001409813,0.000179328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001310695,0.00003746465,0.9987697,0.00002017732,0.0000172242,0.00002171404,0.0001032054,0.000007486265,0.00004490435,0.000004148451,0.0001118262,0.0008490585],"study_design_scores_gemma":[0.000002973703,0.00003061042,0.9992211,0.00001207068,0.00001343637,0.0000587102,0.0004368221,0.00004422546,0.00001315889,0.000003224802,0.000161896,0.0000018664],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973971,0.0002001342,0.00004111916,0.00008905005,0.000003782918,0.00004120883,0.001733397,0.000002499836,0.0004917023],"genre_scores_gemma":[0.9981259,0.0002618699,0.0001131383,0.0001002838,0.000004795519,0.00005079704,0.001123214,0.000001256589,0.0002188598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01137016,"threshold_uncertainty_score":0.02260792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01763944750085181,"score_gpt":0.268280888011626,"score_spread":0.2506414405107742,"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."}}