{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005470964,0.0002954163,0.0005163855,0.0002839552,0.0006413553,0.00008571483,0.0001457391,0.0000727968,0.000003443821],"category_scores_gemma":[0.00142039,0.0001560107,0.00003125606,0.0009847691,0.0006869775,0.0005500825,0.00009870602,0.0005716521,0.000004293724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002259822,"about_ca_system_score_gemma":0.0004987643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001275297,"about_ca_topic_score_gemma":0.0000658935,"domain_scores_codex":[0.9964111,0.0007438624,0.0007709584,0.001005794,0.0005433637,0.0005249635],"domain_scores_gemma":[0.9974732,0.0002508893,0.0006397153,0.001320223,0.000157691,0.000158315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.002846635,0.0007676371,0.05413518,0.01019483,0.0003066874,0.00001087749,0.5419304,0.0001693172,0.00667407,0.001052662,0.04480231,0.3371094],"study_design_scores_gemma":[0.002419713,0.004006376,0.8390961,0.000403842,0.00008216172,0.0007674059,0.1437664,0.0005298078,0.0006233804,0.0009492008,0.006967685,0.0003878984],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.612327,0.000464416,0.00001223395,0.3836288,0.0003192413,0.003162797,0.00001096411,0.00005205099,0.00002250706],"genre_scores_gemma":[0.9721738,0.0001907733,0.00005205624,0.02630292,0.0005171627,0.0002183154,0.00003109342,0.00003257074,0.0004813382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7849609,"threshold_uncertainty_score":0.6361933,"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."}}