{"id":"W2761296717","doi":"10.2196/publichealth.7540","title":"Participatory Disease Surveillance: Engaging Communities Directly in Reporting, Monitoring, and Responding to Health Threats","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":159,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Disease surveillance; Citizen journalism; Citizen science; Participatory sensing; Participatory GIS; Disease; Best practice; Public health surveillance; Business; Public health; Public relations; Medicine; Knowledge management; Data science; Computer science; Political science; World Wide Web; Biology","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":[0.124507,0.001309455,0.0009553286,0.00516503,0.006516519,0.007121327,0.004187568,0.002849931,0.007281614],"category_scores_gemma":[0.1111428,0.0008736008,0.001367512,0.003635831,0.01117884,0.007770339,0.02263029,0.004049958,0.001234136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004024123,"about_ca_system_score_gemma":0.02258854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004554317,"about_ca_topic_score_gemma":0.005589162,"domain_scores_codex":[0.7914457,0.1879306,0.004317523,0.006885018,0.007266042,0.002155082],"domain_scores_gemma":[0.804052,0.1493832,0.01387489,0.01731439,0.008983767,0.006391689],"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":[0.0004427292,0.0009124387,0.02901203,0.009843757,0.0004734306,0.0008577341,0.16268,0.003399714,0.00583706,0.08229251,0.04655109,0.6576976],"study_design_scores_gemma":[0.0004016424,0.001693007,0.01565191,0.01465592,0.0003575011,0.001148167,0.08960991,0.007392659,0.008358677,0.2152569,0.6451086,0.0003650936],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0793198,0.0116917,0.6423949,0.09699008,0.003155593,0.01427712,0.002808613,0.001813576,0.1475486],"genre_scores_gemma":[0.5687585,0.007037228,0.3904054,0.01045412,0.0009673214,0.01324456,0.001219614,0.0002358263,0.007677449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.124507,"threshold_uncertainty_score":0.6584638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1396849339788591,"score_gpt":0.4251206091289365,"score_spread":0.2854356751500773,"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."}}