{"id":"W4400482889","doi":"10.2196/59434","title":"Use of Generative AI for Improving Health Literacy in Reproductive Health: Case Study","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Readability; Reading (process); Pill; Reproductive health; Computer science; Preprint; Action (physics); Artificial intelligence; Health literacy; Medical education; Medicine; Psychology; Internet privacy; Population; World Wide Web; Environmental health; Nursing; Linguistics; Health care","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004531169,0.0001288678,0.0003852114,0.0008727534,0.0002960109,0.00009931457,0.00006739073,0.00005968775,0.00001789768],"category_scores_gemma":[0.0006472237,0.0001063597,0.00006817479,0.001250995,0.0001235251,0.0008386515,0.00006175094,0.0007735363,0.00001490671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009500705,"about_ca_system_score_gemma":0.002350872,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01992606,"about_ca_topic_score_gemma":0.001323993,"domain_scores_codex":[0.9968998,0.0006735889,0.0009164108,0.0004592578,0.0004554135,0.0005955577],"domain_scores_gemma":[0.9970796,0.001013401,0.0001183194,0.0003829921,0.001197668,0.0002079837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003018725,0.0009928207,0.00254911,0.00240591,0.00003504712,0.0001227925,0.4345682,0.00001276489,0.0001208764,0.0004399627,0.0051264,0.5533242],"study_design_scores_gemma":[0.0009224308,0.05152654,0.01387759,0.004159594,0.00003004558,0.001660993,0.7744057,0.1131259,0.02039273,0.00596376,0.01324612,0.0006885859],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.967188,0.001349072,0.001890023,0.02330703,0.0003808237,0.005783773,0.00004687614,0.0000359449,0.00001844691],"genre_scores_gemma":[0.996936,0.00007765598,0.0009711075,0.0003843674,0.0003300191,0.001000346,0.0000354705,0.00002389139,0.0002411114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5526356,"threshold_uncertainty_score":0.9866003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4980415356820135,"score_gpt":0.6323155312266975,"score_spread":0.134273995544684,"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."}}