{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06821311,0.0009603167,0.0007054257,0.002354571,0.001077227,0.002252985,0.001787523,0.001203789,0.00114505],"category_scores_gemma":[0.09682208,0.0007965788,0.001087981,0.001448604,0.0009827797,0.003061479,0.001958824,0.001115219,0.0003918647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278304,"about_ca_system_score_gemma":0.003015245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001058664,"about_ca_topic_score_gemma":0.001485955,"domain_scores_codex":[0.9672628,0.02243371,0.004039421,0.00132061,0.003995501,0.0009479894],"domain_scores_gemma":[0.8903669,0.07723257,0.00279184,0.005279615,0.02231833,0.0020107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","study_design_scores_codex":[0.002197894,0.0164112,0.1788401,0.005479961,0.0004107567,0.002832913,0.06910205,0.00465014,0.0318714,0.001880564,0.00879118,0.6775319],"study_design_scores_gemma":[0.0037648,0.08739816,0.525268,0.008858214,0.001556487,0.00903365,0.1044505,0.0585949,0.08116799,0.002538451,0.1161557,0.001213185],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8948481,0.0003481419,0.07977247,0.0006767788,0.0001195558,0.01996674,0.0005166588,0.000822373,0.002929148],"genre_scores_gemma":[0.6634212,0.000618443,0.3167513,0.000510199,0.00006816303,0.01619758,0.001076075,0.0002232719,0.001133814],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.06821311,"threshold_uncertainty_score":0.3607497,"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."}}