{"id":"W4386615526","doi":"10.2196/45977","title":"Optimizing an mHealth Program to Promote Type 2 Diabetes Prevention in High-Risk Individuals: Cross-Sectional Questionnaire Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public health; Outreach; mHealth; Prediabetes; Mobile phone; Medicine; Computer science; Software deployment; Phone; Type 2 diabetes; Nursing; Psychological intervention; Diabetes mellitus; Political science; Telecommunications","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":[0.003726443,0.0003330501,0.0004122643,0.0006906902,0.0007875132,0.0006667773,0.0004564747,0.0007609026,0.001648329],"category_scores_gemma":[0.005235138,0.0004843823,0.0007242096,0.000718193,0.0003449301,0.0008423389,0.0006125762,0.0008278768,0.0004933724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008544999,"about_ca_system_score_gemma":0.001292223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007983484,"about_ca_topic_score_gemma":0.007972156,"domain_scores_codex":[0.9984947,0.0007405141,0.0001611445,0.0001456324,0.0002253683,0.000232588],"domain_scores_gemma":[0.9965089,0.0009660442,0.001010385,0.0001928136,0.0007077726,0.0006140949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000218599,0.00438825,0.9852524,0.0001152676,0.00008280388,0.0001417906,0.002185661,0.0001677597,0.0004018152,0.00003646271,0.0004065288,0.0066028],"study_design_scores_gemma":[0.00006382862,0.004652559,0.9905452,0.00003405537,0.00005941352,0.0001023684,0.003115309,0.0005913199,0.0001715754,0.00002365261,0.0006247748,0.00001589984],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986621,0.00003203519,0.0001691,0.00004507319,0.000003683237,0.0004366469,0.0002797072,0.000004438122,0.000367097],"genre_scores_gemma":[0.9973351,0.00008874779,0.0009779364,0.0001674731,0.000008989395,0.0007462986,0.0003340098,0.000003885634,0.0003374423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007983484,"threshold_uncertainty_score":0.0197075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1542346367363161,"score_gpt":0.5823073904584567,"score_spread":0.4280727537221406,"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."}}