{"id":"W4205404706","doi":"10.2196/27220","title":"Use of Health Information Technology by Adults With Diabetes in the United States: Cross-sectional Analysis of National Health Interview Survey Data (2016-2018)","year":2022,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"eHealth; Medicine; National Health Interview Survey; Diabetes mellitus; Health Information National Trends Survey; Family medicine; Health care; Gerontology; Obesity; Type 2 diabetes; Ethnic group; Community health; Cross-sectional study; Health information; Public health; Environmental health; Nursing; Internal medicine; Population; Endocrinology","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":[0.002049418,0.0002576431,0.0003676411,0.001629076,0.0002809233,0.0006592372,0.0004640584,0.0004906576,0.001085086],"category_scores_gemma":[0.005163712,0.0003863675,0.0009122704,0.003503043,0.0002004445,0.0007357537,0.0007381478,0.0007583817,0.0003159371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006609897,"about_ca_system_score_gemma":0.0005635829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03522224,"about_ca_topic_score_gemma":0.03524727,"domain_scores_codex":[0.998195,0.0005216021,0.0004262875,0.0003104154,0.0004050782,0.0001417164],"domain_scores_gemma":[0.9943347,0.00101007,0.003264108,0.0003418181,0.0006949691,0.0003543888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002391257,0.00002331729,0.9981703,0.00003441986,0.0001268737,0.000009688994,0.00007031649,0.00004829266,0.00002526266,0.00001548767,0.0005950236,0.00085706],"study_design_scores_gemma":[0.000005903496,0.00002566674,0.9989715,0.0000251101,0.00005468922,0.00004990192,0.0002113758,0.0001463174,0.00002827545,0.00001165271,0.0004651171,0.000004481688],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9552326,0.001000152,0.0003990629,0.0003165514,0.00003023833,0.00008104795,0.04152548,0.00002678293,0.001387986],"genre_scores_gemma":[0.977919,0.0007501537,0.0005739441,0.0002337921,0.00002493547,0.0001816167,0.01996625,0.000006820384,0.0003434852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03522224,"threshold_uncertainty_score":0.07003444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.114756740485684,"score_gpt":0.4334084780935808,"score_spread":0.3186517376078968,"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."}}