{"id":"W4282002242","doi":"10.2196/37591","title":"Synchronous Teleconsultation and Monitoring Service Targeting COVID-19: Leveraging Insights for Postpandemic Health Care","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Ministerio de Economía y Competitividad; Universidade Federal de Minas Gerais; Ministério da Educação; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Usability; Likert scale; System usability scale; Health care; Telemedicine; Coronavirus disease 2019 (COVID-19); Chatbot; Service (business); Pandemic; Nursing; Medicine; Medical education; Medical emergency; Psychology; World Wide Web; Business; Web usability; Computer science; Political 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":[0.004800501,0.0002649465,0.0001746104,0.0007759328,0.0008019728,0.001444911,0.0004931413,0.0003514622,0.001829685],"category_scores_gemma":[0.0127976,0.000121017,0.000303067,0.0004634603,0.0005731731,0.001077858,0.001682959,0.0004417078,0.0002124516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060231,"about_ca_system_score_gemma":0.002605941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002708226,"about_ca_topic_score_gemma":0.009675776,"domain_scores_codex":[0.9968219,0.00238631,0.0001000832,0.0001627755,0.0003014363,0.0002275133],"domain_scores_gemma":[0.9909157,0.006416333,0.0007999543,0.0003114483,0.0007650906,0.0007914105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005890544,0.00128984,0.2364965,0.002157823,0.0000654371,0.002096353,0.1315328,0.0006490245,0.01505097,0.001937981,0.007885546,0.6002488],"study_design_scores_gemma":[0.0001912103,0.007414281,0.5624363,0.003848103,0.0003727102,0.003125645,0.2887614,0.01227586,0.0114847,0.004140148,0.1057653,0.0001844449],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739963,0.0006797472,0.009531146,0.003861312,0.00005875536,0.0006332169,0.0002114845,0.0002006914,0.01082734],"genre_scores_gemma":[0.9822105,0.0005192455,0.01567924,0.0005718693,0.00003094109,0.0001877067,0.0001042115,0.00002029083,0.0006761089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004800501,"threshold_uncertainty_score":0.02538776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03261710231403159,"score_gpt":0.3785908756534912,"score_spread":0.3459737733394596,"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."}}