{"id":"W4226082716","doi":"10.2196/35032","title":"An Electronic Data Capture Tool for Data Collection During Public Health Emergencies: Development and Usability Study","year":2022,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Disaster Response and Management","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biomedical Advanced Research and Development Authority; Baylor University; Washington University in St. Louis; University of Southern California","keywords":"Usability; Electronic data capture; Computer science; Automatic identification and data capture; Vendor; Data collection; System usability scale; Preparedness; Electronic data; Data science; Heuristic evaluation; Database; Human–computer interaction","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.003254046,0.0001877249,0.0002632601,0.0001940122,0.004640397,0.00004875629,0.001081336,0.00003975561,0.0006036276],"category_scores_gemma":[0.0001050082,0.0001771925,0.00001696496,0.0002956044,0.0000224181,0.0005672833,0.002383028,0.0004242816,0.000003803414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008646992,"about_ca_system_score_gemma":0.0009356894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005389646,"about_ca_topic_score_gemma":0.007259101,"domain_scores_codex":[0.9964107,0.0009961027,0.0005836341,0.0008580788,0.0003975775,0.000753918],"domain_scores_gemma":[0.9975988,0.000114452,0.0002302111,0.001850884,0.00004507152,0.0001605615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004140277,0.004043177,0.5795789,0.001130621,0.0003986864,0.000003092956,0.3227186,0.000005735071,0.0001779736,0.0009292466,0.08697146,0.003628509],"study_design_scores_gemma":[0.001279103,0.0006236984,0.3741397,0.000009063104,0.000017665,3.649109e-7,0.1670505,0.0001278362,0.00000173195,0.00004236889,0.4564545,0.0002535227],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932965,0.00005005155,0.0002374186,0.000458658,0.0002631713,0.005246291,0.0002482948,0.0001253087,0.00007430109],"genre_scores_gemma":[0.993052,0.000004895409,0.00007104219,0.0002783423,0.00005070753,0.001444078,0.002612535,0.00003034009,0.002456065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.369483,"threshold_uncertainty_score":0.9966554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2536454957563695,"score_gpt":0.4730160859713058,"score_spread":0.2193705902149363,"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."}}