{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003484287,0.000441401,0.0002528081,0.0009489973,0.002256769,0.001342813,0.001294256,0.002144196,0.00400912],"category_scores_gemma":[0.01657855,0.0002681058,0.0005342356,0.0007860175,0.00230482,0.001039292,0.002273804,0.0014928,0.0005774332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327644,"about_ca_system_score_gemma":0.001135674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003492956,"about_ca_topic_score_gemma":0.006792534,"domain_scores_codex":[0.9965051,0.002636516,0.0001219903,0.0001366325,0.0003110696,0.0002885849],"domain_scores_gemma":[0.9896422,0.008578744,0.0004219979,0.0004512049,0.0002484176,0.0006574659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"case_report","study_design_scores_codex":[0.0003625832,0.00638968,0.1029218,0.002194254,0.00009908928,0.1334679,0.3654384,0.002763724,0.005246157,0.01626635,0.01226352,0.3525866],"study_design_scores_gemma":[0.0004210485,0.005876584,0.102704,0.002468566,0.0003796566,0.2732602,0.3267517,0.03587653,0.02125791,0.02208834,0.2086312,0.0002843429],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9548298,0.001385722,0.008302317,0.004803786,0.00005723437,0.0007440316,0.0001246278,0.0001810594,0.02957146],"genre_scores_gemma":[0.9761126,0.001240918,0.01567625,0.001259969,0.00006005516,0.0002838787,0.00007459445,0.00004404566,0.005247687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00400912,"threshold_uncertainty_score":0.01842695,"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."}}