{"id":"W2809768081","doi":"10.2196/10190","title":"Insights From a Text Messaging–Based Sexual and Reproductive Health Information Program in Tanzania (m4RH): Retrospective Analysis","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FHI 360; United States Agency for International Development","keywords":"mHealth; Psychological intervention; Reproductive health; Tanzania; Population; Documentation; Computer science; Data collection; Medicine; Environmental health; Nursing; Geography; Statistics","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":[0.007613093,0.0002942255,0.0003774936,0.001882319,0.001158673,0.001108029,0.0006987137,0.0004243954,0.001275394],"category_scores_gemma":[0.01797647,0.0005302734,0.0005345871,0.002544858,0.0007514112,0.001034892,0.001805459,0.0009019601,0.0003460317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001593364,"about_ca_system_score_gemma":0.003058948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02328446,"about_ca_topic_score_gemma":0.03407595,"domain_scores_codex":[0.995544,0.002084234,0.0006417769,0.0004492177,0.0006896585,0.0005911157],"domain_scores_gemma":[0.9906089,0.003146655,0.003083731,0.0006517288,0.001979429,0.0005296218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001735302,0.0002184336,0.9547929,0.0005054391,0.00007607191,0.0007327076,0.02317097,0.0001114347,0.000371403,0.0002792119,0.003091115,0.01647684],"study_design_scores_gemma":[0.00001984338,0.0006010158,0.952887,0.0007140019,0.0001221226,0.0007054881,0.03696063,0.000604082,0.0003811315,0.0001865076,0.006778382,0.00003969519],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992779,0.0004980253,0.001161172,0.0004317615,0.00001411975,0.0005716225,0.003500058,0.00001499569,0.001029288],"genre_scores_gemma":[0.9910217,0.0008447769,0.002440611,0.0004970603,0.00002998947,0.001133702,0.003422485,0.00002432381,0.0005854266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02328446,"threshold_uncertainty_score":0.04629785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03996915167455582,"score_gpt":0.4313726868020927,"score_spread":0.3914035351275368,"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."}}