{"id":"W4385620942","doi":"10.1080/26410397.2023.2235796","title":"Perils and promise providing information on sexual and reproductive health via the Nurse Nisa WhatsApp chatbot in the Democratic Republic of the Congo","year":2023,"lang":"en","type":"article","venue":"Sexual and Reproductive Health Matters","topic":"COVID-19 Impact on Reproduction","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ipas; Grand Challenges Canada","keywords":"Chatbot; Reproductive health; Democracy; The Republic; Political science; Nursing; Medicine; Environmental health; World Wide Web; Politics; Computer science; Law; Population","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.002209178,0.0001539072,0.0001110881,0.0003183833,0.007284563,0.002701808,0.0006428607,0.001890371,0.05662157],"category_scores_gemma":[0.006422815,0.0002312638,0.00007684888,0.0002183957,0.001563917,0.002251988,0.003407716,0.00214283,0.002865813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00362547,"about_ca_system_score_gemma":0.007446106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04269349,"about_ca_topic_score_gemma":0.1744594,"domain_scores_codex":[0.9982541,0.001108914,0.00002508647,0.00007001884,0.0001077595,0.0004340117],"domain_scores_gemma":[0.9936574,0.002728332,0.000351854,0.00009578672,0.0003500496,0.002816592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0009284494,0.001060114,0.07641892,0.001346344,0.0000237237,0.01098812,0.2248869,0.0003667986,0.006722692,0.03260885,0.3907478,0.2539013],"study_design_scores_gemma":[0.00007064192,0.0004614099,0.05191869,0.001020224,0.00002653661,0.001393376,0.2445982,0.0007298383,0.001388717,0.001718528,0.696607,0.00006699126],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4686242,0.002461704,0.00118525,0.2264732,0.001644197,0.0003114271,0.0006636509,0.0001984668,0.298438],"genre_scores_gemma":[0.8236541,0.0008040045,0.0006154213,0.01721075,0.0002115575,0.0002232242,0.0001192018,0.00004334597,0.1571184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05662157,"threshold_uncertainty_score":0.189418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03493059982531316,"score_gpt":0.3367302443346494,"score_spread":0.3017996445093362,"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."}}