{"id":"W3163988867","doi":"10.2196/23876","title":"Pre-exposure Prophylaxis (PrEP) Information on Instagram: Content Analysis","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Content analysis; Popularity; Government (linguistics); Medicine; Internet privacy; Computer science; Psychology; World Wide Web","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":[0.007151806,0.0003487199,0.0004320889,0.01108374,0.001031642,0.001614652,0.0006915649,0.0003470946,0.004031632],"category_scores_gemma":[0.02454374,0.0002399571,0.0005367597,0.01034692,0.001133646,0.00210458,0.002466869,0.0004898887,0.0009000191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003150361,"about_ca_system_score_gemma":0.003570221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006560758,"about_ca_topic_score_gemma":0.009873155,"domain_scores_codex":[0.9960164,0.001642129,0.0006847873,0.0003565145,0.0009913846,0.0003087364],"domain_scores_gemma":[0.9739149,0.01686024,0.003046456,0.0007009946,0.005066133,0.0004112231],"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":[0.0005191876,0.0005022747,0.1922475,0.0153702,0.0001037036,0.001123854,0.3478568,0.0008635556,0.0114286,0.007103506,0.038115,0.3847658],"study_design_scores_gemma":[0.00007084509,0.0004738641,0.5312448,0.005348678,0.0002189837,0.0006575013,0.3185797,0.005627601,0.007311874,0.004332402,0.1259435,0.0001901948],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9276789,0.0007839496,0.01203496,0.001393639,0.00007939451,0.01331448,0.02744211,0.0002062464,0.0170664],"genre_scores_gemma":[0.8717407,0.001987958,0.07611582,0.00056771,0.0001300151,0.02405172,0.01950302,0.0001900075,0.005713098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01108374,"threshold_uncertainty_score":0.03782284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04970388360568886,"score_gpt":0.3499835950027198,"score_spread":0.300279711397031,"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."}}