{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001761307,0.0001045512,0.0005104369,0.0004798967,0.0003655315,0.00004613527,0.0002329269,0.0002662458,0.00005308014],"category_scores_gemma":[0.02890054,0.0001088023,0.0002308734,0.002375063,0.0003977933,0.0003576057,0.00003853671,0.0001573978,0.000008798659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002895657,"about_ca_system_score_gemma":0.001977652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002509192,"about_ca_topic_score_gemma":0.006799134,"domain_scores_codex":[0.9972116,0.0002149409,0.0008201686,0.0002640404,0.00100895,0.0004802474],"domain_scores_gemma":[0.9914524,0.006938522,0.0006138089,0.0001852741,0.0004195723,0.000390416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001748873,0.0009265919,0.4205557,0.001219133,0.0006626158,5.877327e-7,0.4276617,0.00002921063,0.00001443232,0.057926,0.009001757,0.08182732],"study_design_scores_gemma":[0.0006960817,0.00008904136,0.9513492,0.0002973478,0.0002035032,9.315182e-8,0.03550235,0.00511079,0.000009850366,0.0009966916,0.005554059,0.0001909752],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9266488,0.0000878768,0.05156923,0.01357694,0.004116385,0.003072223,0.0006474436,0.0001886581,0.00009245119],"genre_scores_gemma":[0.9921297,0.0001059818,0.005598971,0.00028703,0.0006856653,0.0006696656,0.0004354509,0.00002016355,0.0000673035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5307935,"threshold_uncertainty_score":0.9792795,"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."}}