{"id":"W4379159590","doi":"10.1093/geront/gnad061","title":"Using Twitter to Understand COVID-19 Vaccine-Related Ageism During the Pandemic","year":2023,"lang":"en","type":"article","venue":"The Gerontologist","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Toronto Rehabilitation Institute; University Health Network; Dalhousie University; University of Toronto; University of Saskatchewan; University of Ottawa; Thompson Rivers University","funders":"Consortium canadien en neurodégénérescence associée au vieillissement; Centre for Aging + Brain Health Innovation; Canada Research Chairs; Canadian Institutes of Health Research; Alzheimer Society; Saskatchewan Health Research Foundation; Thompson Rivers University","keywords":"Misinformation; Pandemic; Coronavirus disease 2019 (COVID-19); Thematic analysis; Political science; Racism; Blame; Politics; Public relations; Sociology; Psychology; Social psychology; Medicine; Gender studies; Qualitative research; Social science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002516891,0.0003988892,0.0002114006,0.001265274,0.002514207,0.003245897,0.0004111543,0.001288965,0.003198326],"category_scores_gemma":[0.01129546,0.0001964278,0.0002454692,0.001153686,0.002230224,0.008486732,0.003263349,0.001238597,0.0007307746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001658938,"about_ca_system_score_gemma":0.0007804054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003570684,"about_ca_topic_score_gemma":0.006195115,"domain_scores_codex":[0.9980556,0.001297644,0.00009607692,0.0001520527,0.0002104995,0.0001881159],"domain_scores_gemma":[0.9934237,0.004906686,0.0008335919,0.0001453951,0.0004461613,0.000244531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001837742,0.00004073838,0.04303771,0.0007911735,0.00002281527,0.0008461809,0.9003072,0.0002881191,0.002612529,0.008376757,0.01013184,0.03336112],"study_design_scores_gemma":[0.00001265477,0.00006923077,0.03905493,0.0009472385,0.00003575408,0.000353035,0.8316445,0.001967688,0.001407476,0.006191904,0.1182395,0.00007615115],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9408447,0.001576844,0.003875366,0.02046064,0.0005148379,0.00011583,0.001947759,0.00008794266,0.03057615],"genre_scores_gemma":[0.9900384,0.001459502,0.001561168,0.002631383,0.0001863681,0.0001468211,0.000463133,0.00006799775,0.003445253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003570684,"threshold_uncertainty_score":0.01331079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1833257608031652,"score_gpt":0.4025405471759703,"score_spread":0.219214786372805,"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."}}