{"id":"W4376225713","doi":"10.2196/44501","title":"Assessing Disparities in Video-Telehealth Use and eHealth Literacy Among Hospitalized Patients: Cross-sectional Observational Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; University of Chicago; Nutrition Obesity Research Center, University of North Carolina; Chicago Center for Diabetes Translation Research; Georgia Clinical and Translational Science Alliance; National Institute on Aging","keywords":"Telehealth; eHealth; Observational study; Telemedicine; Medicine; Logistic regression; Digital divide; Health literacy; Cross-sectional study; Reimbursement; Videoconferencing; Odds; Health care; Literacy; Ethnic group; Family medicine; The Internet; Psychology; Multimedia; Internal medicine; World Wide Web; Computer science","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":[0.002176614,0.0002044851,0.0005101449,0.0008594049,0.0005902968,0.0007207721,0.0005968383,0.0006422042,0.001629754],"category_scores_gemma":[0.005019678,0.000354825,0.0006278663,0.001529754,0.0003337328,0.0008540945,0.0007957963,0.0009783016,0.0002151567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005692043,"about_ca_system_score_gemma":0.0007895156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008454569,"about_ca_topic_score_gemma":0.01034424,"domain_scores_codex":[0.9985363,0.0004741437,0.0002689874,0.0002314157,0.0002840174,0.0002050212],"domain_scores_gemma":[0.9956578,0.00103676,0.002117103,0.0002542261,0.0003824397,0.0005516767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004307145,0.0001173913,0.9992577,0.00001097066,0.00003196696,0.0000112628,0.00009438529,0.000008789195,0.00003115066,0.000009025039,0.00005594384,0.0003283593],"study_design_scores_gemma":[0.00001743638,0.0002421902,0.9987444,0.00001580445,0.00002978075,0.00005710489,0.0005846904,0.0001705699,0.00003092325,0.0000115184,0.00009172835,0.000003986512],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991118,0.00007780407,0.00007157803,0.00004135131,0.00000277968,0.00003599567,0.0004625618,0.000001811172,0.0001942518],"genre_scores_gemma":[0.9992293,0.00005830086,0.0001200849,0.00006211275,0.000006048756,0.00004394514,0.0004443761,0.000001016855,0.00003474722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008454569,"threshold_uncertainty_score":0.01681072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2460852334247749,"score_gpt":0.5482776324817495,"score_spread":0.3021923990569746,"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."}}