{"id":"W3212049263","doi":"10.1093/eurpub/ckab164.488","title":"What makes COVID-19 dashboards actionable? Lessons learned from international and country-specific studies of COVID-19 dashboards and with dashboard developers in the WHO European Region","year":2021,"lang":"en","type":"article","venue":"European Journal of Public Health","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preparedness; Pandemic; Coronavirus disease 2019 (COVID-19); Dashboard; Knowledge management; Data science; Business; Computer science; Process management; Political science; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02260566,0.000363665,0.001030805,0.0002947681,0.000496798,0.0003510098,0.0006488572,0.00004971599,0.00005008434],"category_scores_gemma":[0.02266136,0.0002325318,0.0001028738,0.0005963661,0.0007438893,0.0006502127,0.0004676867,0.000759634,0.000002735108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008746152,"about_ca_system_score_gemma":0.001625836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001068254,"about_ca_topic_score_gemma":0.0005879916,"domain_scores_codex":[0.9846392,0.0115802,0.001643776,0.0006306294,0.0009585582,0.0005476429],"domain_scores_gemma":[0.9909695,0.005787901,0.001493009,0.0004453295,0.0006412107,0.0006630122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001120365,0.001730377,0.09175465,0.003041245,0.003754557,0.008508353,0.2339338,0.0008330147,0.000175495,0.05548969,0.351,0.2486585],"study_design_scores_gemma":[0.003506357,0.0005155429,0.06293141,0.0006065664,0.000057602,0.0006782715,0.09968347,0.00004002938,0.000003148768,0.005197659,0.8264047,0.0003752479],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.268298,0.01863566,0.03293786,0.6764205,0.0007435923,0.0005234448,0.00009331117,0.00008505842,0.002262632],"genre_scores_gemma":[0.7965177,0.1503459,0.0107415,0.04085907,0.0008000665,0.00001081995,0.00004376447,0.0001220298,0.0005591392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6355614,"threshold_uncertainty_score":0.9855712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5682024300004578,"score_gpt":0.4762800974892485,"score_spread":0.09192233251120924,"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."}}