{"id":"W3204961421","doi":"10.2196/22313","title":"Precision Public Health Campaign: Delivering Persuasive Messages to Relevant Segments Through Targeted Advertisements on Social Media","year":2021,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Target audience; Social media; Public health; Social marketing; Process (computing); Health communication; Population; Advertising campaign; Advertising; Digital media; Population health; Digital advertising; Computer science; Public relations; Business; Medicine; Political science; World Wide Web; Social media marketing; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01450952,0.001294662,0.0005443987,0.003717203,0.001499304,0.003616965,0.001503428,0.001903208,0.005904802],"category_scores_gemma":[0.015701,0.000478758,0.0006001673,0.001221768,0.002148039,0.003582303,0.002638056,0.001523663,0.001455421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001346699,"about_ca_system_score_gemma":0.003740591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003090191,"about_ca_topic_score_gemma":0.004710867,"domain_scores_codex":[0.9913312,0.005597857,0.000341136,0.0006513176,0.00170937,0.0003689952],"domain_scores_gemma":[0.9851188,0.01050584,0.00124349,0.001152037,0.001403765,0.000575895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008369428,0.004098565,0.01636078,0.005334513,0.0002446967,0.0004063899,0.01570973,0.003898225,0.03125913,0.08211252,0.01971154,0.820027],"study_design_scores_gemma":[0.001632705,0.02033907,0.08417197,0.006304538,0.001410822,0.001770357,0.01768709,0.04950152,0.1079814,0.1254355,0.5829138,0.0008512416],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1717118,0.00280235,0.6233479,0.009649248,0.0006777804,0.02101928,0.00250385,0.008508203,0.1597796],"genre_scores_gemma":[0.4548954,0.001591519,0.5121427,0.00199257,0.0002664021,0.009640273,0.000966004,0.000300163,0.01820492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01450952,"threshold_uncertainty_score":0.07673466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3428035722456433,"score_gpt":0.5372954881646607,"score_spread":0.1944919159190173,"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."}}