{"id":"W4214864613","doi":"10.1080/17538068.2022.2044606","title":"Prebunking messaging to inoculate against COVID-19 vaccine misinformation: an effective strategy for public health","year":2022,"lang":"en","type":"article","venue":"Journal of Communications In Healthcare","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia; Centre hospitalier universitaire de Québec; Izaak Walton Killam Health Centre; Institut National de Santé Publique du Québec; University of Manitoba; Dalhousie University; Université Laval","funders":"Canadian Institutes of Health Research; Canadian Immunization Research Network; Public Health Agency; Public Health Agency of Canada","keywords":"Misinformation; Coronavirus disease 2019 (COVID-19); Public health; Internet privacy; 2019-20 coronavirus outbreak; Pandemic; Virology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Computer science; Outbreak; Computer security; Nursing; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.009319572,0.00009487147,0.0002666199,0.0006790847,0.002620428,0.0001787046,0.001120321,0.00004599076,0.00004045202],"category_scores_gemma":[0.001354735,0.0001004327,0.00007236833,0.001062724,0.00003945877,0.001371676,0.0001687742,0.0005331758,0.000001399628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00233932,"about_ca_system_score_gemma":0.003459236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001498746,"about_ca_topic_score_gemma":0.004610347,"domain_scores_codex":[0.996229,0.001744061,0.001017187,0.00008216241,0.0005407627,0.0003868321],"domain_scores_gemma":[0.9966846,0.0005562663,0.0009260785,0.0005466343,0.0005196654,0.0007667525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001566774,0.0003095455,0.004988137,0.0002548004,0.00003890302,0.000001574601,0.5295938,0.02826723,0.00000429057,0.1568875,0.005037654,0.2744599],"study_design_scores_gemma":[0.002184005,0.001213144,0.02302005,0.0001310205,0.000007331945,0.00003663694,0.2675487,0.005596017,0.000002791008,0.004632576,0.6953333,0.0002944263],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1677119,0.002209248,0.007336938,0.8127816,0.000423993,0.003320668,0.0000893934,0.00008637577,0.006039874],"genre_scores_gemma":[0.9793036,0.000458907,0.003202666,0.0168394,0.0000568353,0.00004738069,0.0000537287,0.000009395788,0.00002808975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8115917,"threshold_uncertainty_score":0.998678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2310427398795626,"score_gpt":0.4922633144176999,"score_spread":0.2612205745381373,"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."}}