{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009114365,0.0001645651,0.0003828733,0.0001376144,0.00004651039,0.00004021004,0.0006525243,0.0002928362,0.00009168284],"category_scores_gemma":[0.002070054,0.0001258415,0.00003477613,0.0008115192,0.0008574577,0.0005484496,0.0001973643,0.0005212995,0.00001612992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004124911,"about_ca_system_score_gemma":0.0002060996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001220834,"about_ca_topic_score_gemma":0.01673492,"domain_scores_codex":[0.9973346,0.0002197189,0.0007459044,0.0001474686,0.001151914,0.0004003814],"domain_scores_gemma":[0.9986662,0.0003348724,0.0002837731,0.0002193351,0.0001308608,0.0003649199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002534418,0.0001448202,0.6900743,0.00005152115,0.00002981505,0.000005042633,0.3087389,0.000001000111,3.481079e-8,0.00001630381,0.000539688,0.000373228],"study_design_scores_gemma":[0.001755601,0.0003505294,0.9048344,0.0003033029,0.00001056466,1.063158e-7,0.09070963,0.001781399,0.00001105678,0.000002347293,0.00009928344,0.0001417172],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981392,0.000005314158,0.00004338655,0.0002025266,0.00007712204,0.001080474,0.000022906,0.0001853752,0.0002437088],"genre_scores_gemma":[0.9994605,0.000003989902,0.0001080914,0.0002697619,0.00003773497,0.00006655014,0.00001764056,0.00001270701,0.00002303292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2180292,"threshold_uncertainty_score":0.9338481,"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."}}