{"id":"W3160281828","doi":"10.2139/ssrn.3841301","title":"Social Media COVID-19 Information and Vaccine Decision: A Latent Class Analysis","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Artificial Intelligence in Medicine (Canada); York University","funders":"","keywords":"Latent class model; Social media; Vaccination; Psychological intervention; Coronavirus disease 2019 (COVID-19); Social distance; Pandemic; Psychology; Class (philosophy); Social class; Medicine; Demography; Family medicine; Virology; Computer science; Political science; Sociology; Internal medicine","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":[0.007618513,0.0005108521,0.0008449072,0.002264553,0.001420451,0.003853703,0.001107414,0.001748756,0.01962238],"category_scores_gemma":[0.02007531,0.0003406501,0.002007646,0.002241651,0.0008429993,0.001933478,0.001438713,0.002386664,0.001975779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028812,"about_ca_system_score_gemma":0.001189317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01315575,"about_ca_topic_score_gemma":0.01071416,"domain_scores_codex":[0.9946208,0.00305554,0.0003531282,0.0005282151,0.0006421665,0.000800166],"domain_scores_gemma":[0.9540719,0.03510117,0.005546248,0.001932843,0.001321102,0.002026694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002903573,0.002606796,0.9755157,0.00006462723,0.0008705495,0.00009287773,0.0008391885,0.001337738,0.0002839542,0.001513222,0.001774592,0.01219722],"study_design_scores_gemma":[0.0003153434,0.001205317,0.9136098,0.00007811715,0.001177933,0.0001154805,0.003284915,0.07390751,0.0005010904,0.003503256,0.002222301,0.00007881796],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917735,0.0001923553,0.0016127,0.001211689,0.00007009532,0.0001170665,0.002099969,0.0000252111,0.002897375],"genre_scores_gemma":[0.9950743,0.00007561633,0.0004058731,0.00007652481,0.00008602045,0.00006181491,0.001234101,0.00001038341,0.002975422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01962238,"threshold_uncertainty_score":0.06564343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540144282119167,"score_gpt":0.3049328831502174,"score_spread":0.2895314403290257,"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."}}