{"id":"W2992594339","doi":"10.2196/14886","title":"Social Media Recruitment of Marginalized, Hard-to-Reach Populations: Development of Recruitment and Monitoring Guidelines","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Harm; Social media; Inclusion (mineral); Population; Transgender; Psychology; Harm reduction; Internet privacy; Public relations; Advertising; Social psychology; Political science; Medicine; Business; Public health; Environmental health; Computer science","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4926735,0.001916874,0.002224149,0.007166004,0.007471781,0.006415932,0.007500295,0.00607169,0.007509803],"category_scores_gemma":[0.5198908,0.002419592,0.002202681,0.003384814,0.003857974,0.01072094,0.008015387,0.006077559,0.008195561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005611627,"about_ca_system_score_gemma":0.03922902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005016787,"about_ca_topic_score_gemma":0.01169275,"domain_scores_codex":[0.5567453,0.3562207,0.04439497,0.005580438,0.03287588,0.004182765],"domain_scores_gemma":[0.5491681,0.2351716,0.04063392,0.03766358,0.1277267,0.00963629],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001276855,0.004226767,0.02982674,0.01305294,0.0002181463,0.0007544673,0.04186947,0.001813218,0.005283655,0.01189824,0.1283407,0.7614388],"study_design_scores_gemma":[0.004754089,0.009433308,0.09355241,0.09450433,0.001166766,0.001594828,0.04078954,0.02958133,0.02401744,0.03811796,0.6614042,0.001083801],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"methods","genre_scores_codex":[0.03172547,0.00376138,0.2253471,0.03072204,0.002185407,0.6748343,0.002045505,0.002184134,0.02719471],"genre_scores_gemma":[0.0231396,0.00236915,0.3926165,0.006016694,0.0004637665,0.5720474,0.0006561779,0.0001834898,0.002507212],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5073265,"threshold_uncertainty_score":0.6256239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5754377719442392,"score_gpt":0.5059037402230032,"score_spread":0.06953403172123596,"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."}}