{"id":"W4207076990","doi":"10.2196/32452","title":"COVID-19 and Vitamin D Misinformation on YouTube: Content Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Economics; University of Alberta; University of Calgary; Simon Fraser University; Alberta Health Services; University of British Columbia","funders":"Alberta Innovates; Ministero dello Sviluppo Economico; Government of Alberta","keywords":"Misinformation; Social media; Content analysis; Coronavirus disease 2019 (COVID-19); Psychology; Pandemic; Vitamin; Internet privacy; Medicine; Computer science; Disease; World Wide Web; Sociology; Pathology; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.00329467,0.0002809962,0.0003981161,0.008862441,0.0009725119,0.001779062,0.0003686576,0.0004276221,0.002874355],"category_scores_gemma":[0.02104517,0.0001484595,0.0003863922,0.007780165,0.0007868655,0.001877839,0.001645161,0.0004462549,0.0004795033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003073213,"about_ca_system_score_gemma":0.001846679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01630762,"about_ca_topic_score_gemma":0.02415732,"domain_scores_codex":[0.9977406,0.000945575,0.0002818329,0.000217549,0.0005833374,0.0002310811],"domain_scores_gemma":[0.9838359,0.01100112,0.001983891,0.0002503439,0.00265034,0.0002782829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008135773,0.0003255962,0.4547977,0.01189771,0.0002371871,0.002144468,0.2189742,0.0005228809,0.004759548,0.003798498,0.03580669,0.265922],"study_design_scores_gemma":[0.00004193494,0.000279874,0.6739968,0.004866172,0.0002386337,0.0007420957,0.2476132,0.005139704,0.002364101,0.001390929,0.06320903,0.0001174389],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623123,0.002375417,0.002032405,0.001684503,0.0001003597,0.002764004,0.01773231,0.00006957533,0.01092909],"genre_scores_gemma":[0.9657443,0.00371387,0.009647536,0.0006511152,0.0001541232,0.004952033,0.01098157,0.00006994756,0.004085466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01630762,"threshold_uncertainty_score":0.0324254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1283484001829413,"score_gpt":0.4006313461663453,"score_spread":0.272282945983404,"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."}}