{"id":"W4385615308","doi":"10.1186/s44247-023-00029-w","title":"A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications","year":2023,"lang":"en","type":"article","venue":"BMC Digital Health","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Public Health Ontario; University of Toronto","funders":"National Institutes of Health; Vector Institute; National Institute on Drug Abuse; Government of Canada; Canadian Institute for Advanced Research","keywords":"Medical prescription; Social media; Sentiment analysis; Classifier (UML); Machine learning; Artificial intelligence; Data science; Computer science; Medicine; Natural language processing; World Wide Web; Nursing","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":[0.00798818,0.001628695,0.0009082885,0.009191939,0.001376498,0.003800092,0.002236732,0.001893531,0.00204415],"category_scores_gemma":[0.01371567,0.0007768262,0.003681142,0.003528605,0.002073756,0.003582622,0.002861105,0.001833347,0.00121773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002326289,"about_ca_system_score_gemma":0.003039417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02208436,"about_ca_topic_score_gemma":0.02317868,"domain_scores_codex":[0.9944211,0.002360671,0.0006028894,0.00144588,0.0009448534,0.0002246607],"domain_scores_gemma":[0.9898484,0.006520413,0.00110506,0.0007092213,0.001474711,0.0003420886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003143907,0.0007960706,0.01910557,0.001549554,0.0007524369,0.002551009,0.01272126,0.1530681,0.02236064,0.3294023,0.01430554,0.4430731],"study_design_scores_gemma":[0.00001797531,0.00008345345,0.003222547,0.0001681756,0.00007289179,0.0003345835,0.001032286,0.8760641,0.001575686,0.09993359,0.01740601,0.0000886208],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00201993,0.0001860468,0.9948003,0.0003391513,0.00002930456,0.0002421722,0.0005971654,0.0009816893,0.0008041579],"genre_scores_gemma":[0.06188598,0.0003051198,0.9330692,0.0001447961,0.0001169886,0.0008176137,0.002356961,0.0001186545,0.001184714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02208436,"threshold_uncertainty_score":0.04391164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4523857827081957,"score_gpt":0.4883531304623201,"score_spread":0.03596734775412441,"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."}}