{"id":"W4290600861","doi":"10.3390/healthcare10081475","title":"Health-Related Rumor Control through Social Collaboration Models: Lessons from Cases in China during the COVID-19 Pandemic","year":2022,"lang":"en","type":"article","venue":"Healthcare","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Sichuan University","keywords":"Rumor; Government (linguistics); Public relations; Credibility; Social media; Public health; Socialization; Pandemic; Distrust; Political science; Sociology; Internet privacy; Computer science; Data science; Psychology; Coronavirus disease 2019 (COVID-19); Social psychology; Medicine; Law","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008925592,0.0004539782,0.0004354911,0.001840112,0.003257121,0.002000444,0.001041662,0.001290485,0.00123638],"category_scores_gemma":[0.02283522,0.0002535861,0.0005723619,0.00131795,0.002277209,0.003623215,0.001975039,0.001469883,0.0001297665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004824845,"about_ca_system_score_gemma":0.004028751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06612119,"about_ca_topic_score_gemma":0.08674432,"domain_scores_codex":[0.9954672,0.003308707,0.0001591392,0.000244637,0.000272456,0.0005478855],"domain_scores_gemma":[0.9834698,0.01025927,0.00216326,0.001359212,0.001796259,0.0009522057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000302936,0.0006062196,0.668386,0.0004010738,0.0001840477,0.004468383,0.2202466,0.003835242,0.000712849,0.0157296,0.005053326,0.08007365],"study_design_scores_gemma":[0.000120281,0.0005290285,0.4280767,0.0008895959,0.0002901221,0.0009557324,0.4524903,0.06742302,0.001146541,0.03297679,0.0149087,0.0001930918],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991697,0.0003973458,0.001724729,0.003191693,0.00001981567,0.0000779619,0.00008135015,0.00001587515,0.002794199],"genre_scores_gemma":[0.9983652,0.0002631398,0.0008596326,0.000150689,0.00001602955,0.00003038738,0.00005343842,0.000004221379,0.0002571547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06612119,"threshold_uncertainty_score":0.1314726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1298038895908917,"score_gpt":0.4191768799179748,"score_spread":0.2893729903270831,"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."}}