{"id":"W3166785002","doi":"10.1016/j.chb.2021.106882","title":"Something good out of something bad: eHealth and telemedicine in the Post-COVID era","year":2021,"lang":"en","type":"article","venue":"Computers in Human Behavior","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Telemedicine; eHealth; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Internet privacy; Psychology; Computer science; Health care; Virology; Medicine; Political science; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015235,0.0001786075,0.0005161764,0.0004644696,0.0001201073,0.00001925036,0.0001499979,0.00009267998,0.00007340252],"category_scores_gemma":[0.0001666172,0.000143371,0.00005180285,0.0004271088,0.0001150832,0.0001101155,0.00009012588,0.000688476,0.000001096085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001286104,"about_ca_system_score_gemma":0.0002698182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005608888,"about_ca_topic_score_gemma":0.0006181501,"domain_scores_codex":[0.9976723,0.0002588153,0.0008765842,0.0003578985,0.0004485932,0.0003857881],"domain_scores_gemma":[0.9987596,0.000408699,0.0002047899,0.0003645183,0.0001167588,0.0001455943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004646376,0.0005880207,0.8663142,0.0006971762,0.00001823655,0.0007337923,0.03300941,0.00000271376,0.0079337,0.001666037,0.0005340092,0.08845621],"study_design_scores_gemma":[0.005074642,0.0009022661,0.9854413,0.0004667128,0.0001133556,0.0001698362,0.0058036,0.00006093389,0.0006783407,0.0002193656,0.0009270406,0.0001426213],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905202,0.0005015573,0.0006882228,0.006902814,0.0003889619,0.0008073151,0.000006680155,0.00002819915,0.0001559887],"genre_scores_gemma":[0.9855447,0.00007510271,0.006472949,0.007451826,0.0002356552,0.00005259231,0.0001226944,0.00002089005,0.00002352383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1191271,"threshold_uncertainty_score":0.5846502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05547101330421738,"score_gpt":0.3928413508999379,"score_spread":0.3373703375957205,"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."}}